Molecular analyses using long cell-free fragments in pregnancy

Abstract

Methods and systems described herein involve using long cell-free DNA fragments to analyze a biological sample from a pregnant subject. The status of methylated CpG sites and single nucleotide polymorphisms (SNPs) is often used to analyze DNA fragments of a biological sample. A CpG site and a SNP are typically separated from the nearest CpG site or SNP by hundreds or thousands of base pairs. Finding two or more consecutive CpG sites or SNPs on most cell-free DNA fragments is improbable or impossible. Cell-free DNA fragments longer than 600 bp may include multiple CpG sites and / or SNPs. The presence of multiple CpG sites and / or SNPs on long cell-free DNA fragments may allow for analysis than with short cell-free DNA fragments alone. The long cell-free DNA fragments can be used to identify a tissue of origin and / or to provide information on a fetus in a pregnant female.

Description

MOLECULAR ANALYSIS USING CIRCULATING LONG EXTRACELLULAR FRAGMENTS IN PREGNANCY CROSS REFERENCES WITH RELATED REQUESTS This application claims the priority benefit of US Provisional Application no. 62 / 970,634, filed February 5, 2020, and U.S. Provisional Application no. 63 / 135,486, filed January 8, 2021, the entire contents of which are incorporated herein for all purposes. BACKGROUND The modal size of circulating extracellular DNA in pregnancy has been reported to be approximately 166 bp (Lo et al. Sci Transí Med. 2010;2:61 ra91). There is very little published data on fragments larger than 600 bp. An example is the work of Amicucci et al who reported the PCR amplification of an 8 kb fragment of the basic protein Y2 (BPY2) gene from the Y chromosome of maternal plasma (Amicucci et al. Clin Chem 2000;40: 301- 2). It is not known whether such data can be generalized genome-wide. Indeed, there are many challenges to using massively parallel short-read sequencing technologies, for example, using the lllumina platform, to detect such long DNA fragments, for example, above 600 bp (Lo et al. Sci Transí Med . 2010;2:61ra91; Fan et al, Clin Chem. 2010;56:1278-86). These challenges include: (1) the recommended size range for the lllumina sequencing platform typically spans 100-300 bp (De Maio et al. Micob Genom. 2019;5(9)); (2) DNA amplification would be involved in sequencing library preparation (via PCR) or generation of sequencing pools via bridge amplification in a flow cell. Such an amplification process may favor the amplification of shorter DNA fragments due in part to the fact that long DNA templates (e.g., >600 bp) would require a relatively long time to complete synthesis of the daughter strands compared to with short DNA templates (e.g. <200 bp). Therefore, within a fixed time frame for these PCR processes before or during sequencing on the lllumina platform, those long DNA molecules, whose daughter strands were not completely generated during a PCR process, would not be available in the subsequent analysis; (3) the long DNA molecule would have a higher probability of forming secondary structures that would make amplification difficult; (4) Using lllumina sequencing technology, long DNA molecules are more likely to result in clusters containing more than one clonal DNA molecule, compared to short DNA molecules, as libraries are denatured, diluted, and they diffuse on the two-dimensional surface, followed by amplification of the bridge (Head et al. Biotechniques. 2014;56:61-4). BRIEF COMPENDIUM The methods and systems described herein involve the use of long fragments of circulating extracellular DNA to analyze a biological sample. The use of these long fragments of circulating extracellular DNA allows analysis not contemplated or impossible with shorter fragments of circulating extracellular DNA. The status of methylated CpG sites and single nucleotide polymorphisms (SNPs) is often used to analyze DNA fragments from ηζοΑηη / ζζηζ / Ε / γίΛΐ a biological sample. A CpG site and SNP are typically separated from the nearest CpG site or SNP by hundreds or thousands of base pairs. The length of most circulating extracellular DNA fragments in a biological sample is usually less than 200 bp. As a result, it is unlikely or impossible to find two or more consecutive CpG or SNP sites in most circulating extracellular DNA fragments. Circulating extracellular DNA fragments larger than 200 bp, including those larger than 600 bp or 1 kb, may include multiple CpG and / or SNP sites. The presence of multiple CpG sites and / or SNPs in long fragments of circulating extracellular DNA may allow more efficient and / or accurate analysis than with short fragments of circulating extracellular DNA alone. Long fragments of circulating extracellular DNA can be used to identify a tissue of origin and / or provide information about a fetus in a pregnant woman. Furthermore, the use of long fragments of circulating extracellular DNA to accurately analyze samples from pregnant women is surprising, as one would expect such long fragments of circulating extracellular DNA to be predominantly of maternal origin. One would not expect long fragments of circulating extracellular DNA of fetal origin to be present in sufficient quantities to provide information about the fetus. Long fragments of circulating extracellular DNA with a SNP present can be used to determine the haplotype inherited by a fetus. Long fragments of circulating extracellular DNA, having multiple CpG sites, may have a methylation pattern indicative of a tissue of origin. Additionally, trinucleotide repeats and other repeated sequences may be present in long fragments of circulating extracellular DNA. These repeats can be used to determine the likelihood of a genetic disorder in the fetus or the paternity of a fetus. The number of long fragments of circulating extracellular DNA can be used to determine gestational age. Similarly, motifs at the end of long fragments of circulating extracellular DNA can also be used to determine gestational age. Long circulating extracellular DNA fragments (including, for example, quantities, length distribution, genomic locations, methylation status, etc. of such fragments) can be used to determine a pregnancy-associated disorder. These and other embodiments of the description are described in detail below. For example, other embodiments are directed to systems, devices and computer-readable media associated with the methods described herein. A better understanding of the nature and advantages of the embodiments of the present description can be obtained with reference to the following detailed description and the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS FIGS. 1A and 1B show the size distribution of circulating extracellular DNA determined in accordance with embodiments of the present invention. (A) 0-20 kb on linear scale, (B) 0-20 kb on logarithmic scale. FIGS. 2A and 2B show the size distribution of circulating extracellular DNA determined in accordance with embodiments of the present invention. (A) 0-5 kb on a linear scale for the y-axis. (B) 0-5 kb on a logarithmic scale for the y-axis. nzoAnn / zznz / E / YiAi FIGS. 3A and 3B show the size distribution of circulating extracellular DNA determined in accordance with embodiments of the present invention. (A) 0-400 bp on a linear scale for the y-axis. (B) 0-400 bp on a logarithmic scale for the y-axis. FIGS. 4A and 4B show the size distribution of circulating extracellular DNA between fragments carrying shared alleles (Shared) and fetus-specific alleles (Fetus-Specific) determined in accordance with embodiments of the present invention. (A) 0-20 kb bp on a linear scale for the y-axis. (B) 0-20 kb on a logarithmic scale for the y-axis. The blue line indicates fragments carrying shared alleles (predominantly of maternal origin) and the red line indicates fragments carrying fetus-specific alleles (of placental origin). FIGS. 5A and 5B show the size distribution of circulating extracellular DNA between fragments carrying shared alleles (Shared) and fetus-specific alleles (Fetus-Specific) determined in accordance with embodiments of the present invention. (A) 0-5 kb bp on a linear scale for the y-axis. (B) 0-5 kb on a logarithmic scale for the y-axis. The blue line indicates fragments carrying shared alleles (predominantly of maternal origin) and the red line indicates fragments carrying fetus-specific alleles (of placental origin). FIGS. 6A and 6B show the size distribution of circulating extracellular DNA between fragments carrying shared alleles (Shared) and fetus-specific alleles (Fetus-Specific) determined in accordance with embodiments of the present invention. (A) 0-1 kb on a linear scale for the y-axis. (B) 0-1 kb on a logarithmic scale for the y-axis. The blue line indicates fragments carrying shared alleles (predominantly of maternal origin) and the red line indicates fragments carrying fetus-specific alleles (of placental origin). FIGS. 7A and 7B show the size distribution of circulating extracellular DNA between fragments carrying shared alleles (Shared) and fetus-specific alleles (Fetus-Specific) determined in accordance with embodiments of the present invention. (A) 0-400 bp on a linear scale for the y-axis. (B) 0-400 bp on a logarithmic scale for the y-axis. The blue line indicates the fragments that carry shared alleles (predominantly of maternal origin) and the red line indicates the fragments that carry fetus-specific alleles (of placental origin). FIG 8 shows the levels of single-molecule double-stranded DNA methylation between fragments carrying the mother-specific alleles and the fetus-specific alleles according to embodiments of the present invention. FIG 9A and 9B show (A) the fitted distribution of single-molecule double-stranded DNA methylation levels between fragments carrying the mother-specific alleles and the fetus-specific alleles and (B) the analysis of the characteristic receptor operational (ROC) using single-molecule double-stranded DNA methylation levels according to embodiments of the present invention. FIGS. 10A and 10B show correlation between single-molecule double-stranded DNA methylation levels and DNA fragment sizes in plasma according to embodiments of the present invention. (A) A size range of 0 - 20 kb. (B) a size range of 0 -1 kb. FIGS. 11A and 11B show an example of a long fetus-specific DNA molecule identified in the maternal plasma DNA of a pregnant woman according to embodiments of the present invention. (A) Black bar indicates the specific fetal DNA molecule aligned to a region on chromosome 10 of a human reference genome. (B) The detailed illustration of genetic and epigenetic determination using PacBio sequencing according to the description. The base highlighted in yellow (marked with an arrow) is likely due to a sequencing error that could be corrected in some embodiments. FIGS. 12A and 12B show an example of a long maternal DNA molecule carrying shared alleles identified in the maternal plasma DNA of a pregnant woman according to embodiments of the present invention. (A) The black bar indicates the long mother-specific DNA molecule aligned to a region on chromosome 6 of a human reference. (B) Detailed illustration of the genetic and epigenetic information determined using PacBio sequencing according to embodiments of the present invention. FIG 13 shows the frequency distribution of DNA from placental (red) and maternal (blue) blood cells according to the methylation level at different resolutions from 1 kb to 20 kb according to embodiments of the present invention. FIGS. 14A and 14B show the frequency distribution for DNA from placental (red) and maternal (blue) blood cells according to methylation levels within 16 kb and 24 kb windows according to embodiments of the present invention. FIGS. 15A and 15B show an example of a maternally specific long DNA molecule identified in the maternal plasma DNA of a pregnant woman according to embodiments of the present invention. (A) Black bar indicates the long mother-specific DNA molecule aligned to a region on chromosome 8 of a human reference. (B) Detailed illustration of genetic and epigenetic determination using PacBio sequencing according to embodiments of the present invention. FIG 16 shows an illustration of the deduction of maternal inheritance from the fetus according to embodiments of the present invention. FIG 17 illustrates the determination of genetic / epigenetic disorders in a plasma DNA molecule with information of maternal and fetal origins according to embodiments of the present invention. FIG 18 illustrates the identification of fetal aberrant fragments according to embodiments of the present invention. FIGS. 19A-19G show illustrations of genotyping error correction of circulating extracellular DNA using PacBio sequencing in accordance with embodiments of the present invention. A represents a base identical to the reference base in the Watson chain. represents a base identical to the reference base in the Crick chain. The 'letter of the alphabet' represents an alternative allele that is different from the reference allele. '*' represents an insertion. wrepresents a deletion. FIG 20 shows a method of analyzing a biological sample obtained from a woman pregnant with a fetus according to embodiments of the present invention. FIG 21 shows a method of analyzing a biological sample obtained from a woman pregnant with a fetus to determine the inheritance of a haplotype according to embodiments of the present invention. FIG 22 shows methylation patterns for determining the tissue of origin of a long DNA molecule in plasma according to embodiments of the present invention. ηζοΑηη / ζζηζ / Ε / γίΛΐ FIG 23 shows a receiver operating characteristic (ROC) curve for determining maternal and fetal origins according to embodiments of the present invention. FIG 24 shows pairwise methylation patterns according to embodiments of the present invention. FIG 25 is a table of the distribution of selected marker regions between different chromosomes according to embodiments of the present invention. FIG 26 is a table of the classification of plasma DNA molecules based on their single-molecule methylation patterns using different percentages of buffy coat DNA molecules that have a mismatch score greater than 0.3 as a selection criterion for marker regions according to embodiments of the present invention. FIG 27 shows a process flow for using a placenta-specific methylation haplotype to determine fetal inheritance in a non-invasive manner according to embodiments of the present invention. FIG 28 illustrates the principle of non-invasive prenatal detection of fragile X syndrome using long circulating extracellular DNA in maternal plasma according to embodiments of the present invention. FIG 29 illustrates the maternal inheritance of the fetus based on methylation patterns according to embodiments of the present invention. FIG 30 illustrates the qualitative analysis of maternal inheritance of the fetus using genetic and epigenetic information from DNA molecules in plasma according to embodiments of the present invention. FIG 31 illustrates the detection rate of qualitative analysis for maternal inheritance of the fetus in a genomic manner using genetic and epigenetic information of DNA molecules in plasma compared to relative haplotype dosage (RHDO) analysis according to modalities of the present invention. FIG 32 shows the relationship between the detection rate of specific paternal variants in a complete genome and the number of sequenced plasma DNA molecules with different sizes used for analysis according to embodiments of the present invention. FIG 33 shows a workflow for non-invasive detection of fragile X syndrome according to embodiments of the present invention. FIG 34 shows a methylation pattern of a DNA in plasma compared to the DNA methylation profiles of the buffy coat and placenta according to embodiments of the present invention. FIG 35 is a table showing the distribution of CpG sites in a 500 bp region along a human genome according to embodiments of the present invention. FIG 36 is a table showing the distribution of CpG sites in a 1 kb region along a human genome according to embodiments of the present invention. FIG 37 is a table showing the distribution of CpG sites in a 3 kb region along a human genome according to embodiments of the present invention. FIG 38 is a table showing the proportional contributions of DNA molecules from different tissues in maternal plasma using methylation status matching analysis according to embodiments of the present invention. ηζοΑηη / ζζηζ / Ε / γίΛΐ FIGS. 39A and 39B show the relationship between the placental contribution and the fetal DNA fraction deduced by the SNP approach according to embodiments of the present invention. FIG 40 shows a method of analyzing a biological sample obtained from a woman pregnant with a fetus to determine the tissue of origin using methylation pattern analysis in accordance with embodiments of the present invention. FIGS. 41A and 41B show the size distributions of circulating extracellular DNA molecules from maternal plasma samples from the first, second and third trimester according to embodiments of the present invention. FIG 42 is a table showing the proportion of long DNA molecules in plasma in different trimesters of pregnancy according to embodiments of the present invention. FIGS. 43A and 43B show size distributions of DNA molecules covering fetus-specific alleles from maternal plasma of the first, second and third trimester according to embodiments of the present invention. FIGS. 44A and 44B show size distributions of DNA molecules covering mother-specific alleles from maternal plasma from the first, second and third trimester according to embodiments of the present invention. FIG 45 is a table of the proportion of long DNA molecules in fetal and maternal plasma in different trimesters of pregnancy according to embodiments of the present invention. FIGS. 46A, 46B and 46C show graphs of the proportions of fetus-specific plasma DNA fragments of a particular size range over different trimesters according to embodiments of the present invention. FIGS. 47A, 47B and 47C show graphs of base content ratios at the 5' end of circulating extracellular DNA molecules in maternal plasma from the first, second and third trimester in the fragment size range of 0 to 3 kb according to with embodiments of the present invention. FIG 48 is a table of the ratios of final nucleotide bases between short and long circulating extracellular DNA molecules from maternal plasma of the first, second and third trimester according to embodiments of the present invention. FIG 49 is a table of the ratios of final nucleotide bases between short and long circulating extracellular DNA molecules covering a fetus-specific allele from maternal plasma of the first, second and third trimester according to embodiments of the present invention. FIG 50 is a table of the ratios of final nucleotide bases between short and long circulating extracellular DNA molecules covering a mother-specific allele from maternal plasma of the first, second and third trimester according to embodiments of the present invention. FIG 51 illustrates hierarchical clustering analysis of short and long plasma circulating extracellular DNA molecules using 256 end motifs according to embodiments of the present invention. FIG 52A and 52B show principal component analysis of 4-mer end motif profiles according to embodiments of the present invention. FIG 53 is a table of the final 25 motifs with the highest frequencies among short plasma DNA molecules from first trimester maternal plasma according to embodiments of the present invention. ηζοΑηη / ζζηζ / Ε / γίΛΐ FIG 54 is a table of the final 25 motifs with the highest frequencies among short plasma DNA molecules from second trimester maternal plasma according to embodiments of the present invention. FIG 55 is a table of the final 25 motifs with the highest frequencies among short plasma DNA molecules from third trimester maternal plasma according to embodiments of the present invention. FIG 56 is a table of the final 25 motifs with the highest frequencies among long plasma DNA molecules from first trimester maternal plasma according to embodiments of the present invention. FIG 57 is a table of the final 25 motifs with the highest frequencies among long plasma DNA molecules from second trimester maternal plasma according to embodiments of the present invention. FIG 58 is a table of the final 25 motifs with the highest frequencies among long plasma DNA molecules from third trimester maternal plasma according to embodiments of the present invention. FIGS. 59A, 59B, and 59C show scatter plots of motif frequencies of 16 NNXY motifs between short and long plasma DNA molecules in maternal plasma from (A) first trimester, (B) second trimester, and (C) third trimester. according to embodiments of the present invention. FIG 60 shows a method of analyzing a biological sample obtained from a woman pregnant with a fetus to determine a gestational age according to embodiments of the present invention. FIG 61 shows a method of analyzing a biological sample obtained from a woman pregnant with a fetus to classify a probability of a disorder associated with pregnancy according to embodiments of the present invention. FIG 62 is a table showing clinical information of four cases of preeclampsia according to embodiments of the present invention. FIGS. 63A-63D are graphs of the size distribution of circulating extracellular DNA molecules from third trimester maternal plasma samples with preeclampsia and normotension according to embodiments of the present invention. FIGS. 64A-64D are graphs of the size distribution of circulating extracellular DNA molecules from third trimester maternal plasma samples with preeclampsia and normotension according to embodiments of the present invention. FIGS. 65A-65D are graphs of the size distributions of DNA molecules covering fetus-specific alleles from third trimester maternal plasma samples with preeclampsia and normotension according to embodiments of the present invention. FIGS. 66A-66D are graphs of the size distributions of DNA molecules covering fetus-specific alleles from third trimester maternal plasma samples with preeclampsia and normotension according to embodiments of the present invention. FIGS. 67A-67D are graphs of the size distributions of DNA molecules covering mother-specific alleles from third trimester maternal plasma samples with preeclampsia and normotension according to embodiments of the present invention. nzoAnn / zznz / E / YiAi FIGS. 68A-68D are graphs of the size distributions of DNA molecules covering mother-specific alleles from third trimester maternal plasma samples with preeclampsia and normotension according to embodiments of the present invention. FIGS. 69A and 69B are graphs of the proportion of short DNA molecules covering fetus-specific alleles and maternal-specific alleles in preeclamptic and normotensive maternal plasma samples sequenced with PacBio SMRT sequencing in accordance with embodiments of the present invention. FIGS. 70A and 70B are graphs of the proportion of short DNA molecules in preeclampsia and normotension maternal plasma samples sequenced with PacBio SMRT sequencing and lllumina sequencing in accordance with embodiments of the present invention. FIG 71 is a graph of size ratios indicating the relative proportions of short and long DNA molecules, in preeclamptic and normotensive maternal plasma samples sequenced with PacBio SMRT sequencing according to embodiments of the present invention. FIGS. 72A-72D show the proportion of different ends of plasma DNA molecules in preeclampsia and normotension maternal plasma samples sequenced with PacBio SMRT sequencing according to embodiments of the present invention. FIG 73 shows a hierarchical clustering analysis of maternal plasma DNA samples with preeclampsia and normotension in the third trimester using the frequency of plasma DNA molecules with each of the four types of fragment ends (first nucleotide in the 5' end of each chain), namely C-end, G-end, T-end and A-end, according to embodiments of the present invention. FIG 74 shows a hierarchical clustering analysis of maternal plasma DNA samples with preeclampsia and normotension in the third trimester using 16 two-nucleotide XYNN motifs (dinucleotide sequence of the first and second nucleotide of the 5 'end) according to embodiments. the present invention. FIG 75 shows hierarchical clustering analysis of maternal plasma DNA samples with preeclampsia and normotension in the third trimester using 16 two-nucleotide NNXY motifs (dinucleotide sequence of the third and fourth nucleotides of the 5 'end) according to modalities of the present invention. FIG 76 shows the hierarchical clustering analysis of maternal plasma DNA samples with preeclampsia and normotension in the third trimester using 256 four-nucleotide motifs (dinucleotide sequence from the first to the fourth nucleotide of the 5 'end) according to embodiments of the present invention. FIGS. 77A-77D show the contribution of T cells among four types of fragment ends in maternal plasma DNA samples with preeclampsia and normotension according to embodiments of the present invention. FIG 78 shows a method of analyzing a biological sample obtained from a woman pregnant with a fetus to determine the probability of a pregnancy-associated disorder according to embodiments of the present invention. FIG 79 shows an illustration of the deduction of maternal inheritance from the fetus for diseases associated with recurrence according to embodiments of the present invention. nzoAnn / zznz / E / YiAi FIG 80 shows an illustration of the deduction of paternal inheritance from the fetus for diseases associated with repetition according to embodiments of the present invention. FIGs 81,82 and 83 are tables showing examples of repeat expansion diseases. FIG 84 is a table showing examples for the detection of repeat expansion in the fetus and the determination of repeat-associated methylation according to embodiments of the present invention. FIG 85 shows a method of analyzing a biological sample obtained from a woman pregnant with a fetus to determine the probability of a genetic disorder in the fetus according to embodiments of the present invention. FIG 86 shows a method of analyzing a biological sample obtained from a woman pregnant with a fetus to determine paternity according to embodiments of the present invention. FIG 87 shows methylation patterns for two representative plasma DNA molecules after size selection. FIG 88 is a table of sequencing information for samples with and without size selection according to embodiments of the present invention. FIGS. 89A and 89B show graphs of plasma DNA size profiles for samples with and without bead-based size selection according to embodiments of the present invention. FIGS. 90A and 90B show size profiles between fetal and maternal DNA molecules in a size-selected sample according to embodiments of the present invention. FIG 91 is a table of statistics for the number of plasma DNA molecules carrying informative SNPs between samples with and without size selection according to embodiments of the present invention. FIG 92 is a table of the methylation level in size-selected and non-size-selected plasma DNA samples according to embodiments of the present invention. FIG 93 is a table of the methylation level in maternal or fetal-specific circulating extracellular DNA molecules according to embodiments of the present invention. FIG 94 is a table of the top 10 motifs in samples with and without size selection according to embodiments of the present invention. FIG 95 is a receiver operating characteristic (ROC) graph showing that long DNA molecules in plasma improve the performance of tissue of origin analysis according to embodiments of the present invention. FIG 96 illustrates the principle of airport sequencing for DNA molecules in plasma according to embodiments of the present invention. FIG 97 is a table of the percentage of DNA molecules in plasma in a particular size range and their corresponding methylation levels according to embodiments of the present invention. FIG 98 is a graph of the size distribution and methylation patterns across different sizes according to embodiments of the present invention. FIG 99 is a table of the fetal DNA fraction determined using nanopore sequencing according to embodiments of the present invention. ηζοΑηη / ζζηζ / Ε / γίΛΐ FIG 100 is a table of methylation levels between fetus-specific and maternal-specific DNA molecules according to embodiments of the present invention. FIG 101 is a table of the percentages of plasma DNA molecules in a particular size range and their corresponding methylation levels for fetal and maternal DNA molecules according to embodiments of the present invention. FIGS. 102A and 102B are graphs of the size distributions of fetal and maternal DNA molecules determined by nanopore sequencing according to embodiments of the present invention. FIG 103 is a graph showing the difference in methylation levels between fetal and maternal DNA molecules based on a single informative SNP and two informative SNPs according to embodiments of the present invention. FIG 104 is a table of the difference in methylation levels between fetal and maternal DNA molecules according to embodiments of the present invention. FIG 105 illustrates a measurement system according to embodiments of the present invention. FIG 106 shows a computer system according to embodiments of the present invention. TERMS A tissue corresponds to a group of cells that are grouped together as a functional unit in a pregnant person or her fetus. More than one type of cell can be found in a single tissue. Different types of tissue can consist of different types of cells (for example, hepatocytes, alveolar cells, or blood cells), but they can also correspond to tissue from different organisms (mother versus fetus; tissues in a pregnant person who has received a transplant; tissues of a pregnant organism or its fetus that are infected by a microorganism or a virus). Reference tissues may correspond to tissues used to determine tissue-specific methylation levels. Multiple samples of the same tissue type from different pregnant people or their fetuses can be used to determine a tissue-specific methylation level for that tissue type. A biological sample refers to any sample that is taken from a pregnant person (for example, a human (or other animal), such as a pregnant woman, a person with a disorder, or a pregnant person suspected of having a disorder, a recipient organ transplant pregnant or pregnant person suspected of having a pathological process that involves an organ (for example, the heart in a myocardial infarction, or the brain in a stroke, or the hematopoietic system in anemia) and contains one or more nucleic acid molecules of interest. The biological sample may be a body fluid, such as blood, plasma, serum, urine, vaginal fluid, vaginal fluids, pleural fluid, ascites fluid, cerebrospinal fluid, saliva, sweat, tears, sputum, fluid bronchoalveolar lavage, nipple discharge fluid, aspiration fluid from different parts of the body (e.g., thyroid, breast), intraocular fluids (e.g., aqueous humor), etc. Stool samples may also be used. In various embodiments, the majority of the DNA in a biological sample from a pregnant woman that has been enriched for circulating extracellular DNA (e.g., a plasma sample obtained through a centrifugation protocol) may be circulating extracellular, e.g. About 50%, 60%, 70%, 80%, 90%, 95%, or 99% of the DNA may be circulating extracellular. ηζοΑηη / ζζηζ / Ε / γίΛΐ The centrifugation protocol may include, for example, 3,000 g x 10 minutes, obtaining the fluid part and centrifuging again at, for example, 30,000 g for another 10 minutes to remove residual cells. As part of an analysis of a biological sample, a statistically significant number of circulating extracellular DNA molecules can be analyzed (for example, to provide an accurate measurement) for a biological sample. In some embodiments, at least 1000 circulating extracellular DNA molecules are analyzed. In other embodiments, at least 10,000 or 50,000 or 100,000 or 500,000 or 1,000,000 or 5,000,000 circulating extracellular DNA molecules, or more, may be analyzed. At least the same number of sequence reads can be analyzed. A sequence read refers to a string of nucleotides sequenced from any part or all of a nucleic acid molecule. For example, a sequence read may be a short string of nucleotides (e.g., 20-150 nucleotides) sequenced from a nucleic acid fragment, a short string of nucleotides at one or both ends of a nucleic acid fragment, or the sequencing of the entire nucleic acid fragment that exists in the biological sample. A sequence read can be obtained in various ways, for example, using sequencing techniques or using probes such as can be used in microarrays, for example, in hybridization arrays or capture probes, or amplification techniques, such as the chain reaction. polymerase (PCR) or linear amplification using a single primer or isothermal amplification. As part of an analysis of a biological sample, a statistically significant number of sequence reads may be analyzed, for example, at least 1,000 sequence reads may be analyzed. As other examples, at least 10,000 or 50,000 or 100,000 or 500,000 or 1,000,000 or 5,000,000, or more sequence reads may be analyzed. A “site” (also called a “genomic site”) corresponds to a single site, which may be a single base position or a group of correlated base positions, for example, a CpG site or a larger group of correlated base positions. . A locus can correspond to a region that includes multiple sites. A locus may include only one site, which would make the locus equivalent to a site in that context. A methylation status refers to the state of methylation at a given site. For example, a site may be either methylated, unmethylated, or, in some cases, indeterminate. The methylation index for each genomic site (e.g., a CpG site) may refer to the proportion of DNA fragments (e.g., as determined from sequence reads or probes) that show methylation at the site over the number total readings covering that site. A read may correspond to information (e.g., methylation status at a site) obtained from a DNA fragment. A reading can be obtained using reagents (e.g., primers or probes) that hybridize, preferably, to DNA fragments of a particular methylation state at one or more sites. Typically, these reagents are applied after treatment with a process that differentially modifies or differentially recognizes DNA molecules depending on their methylation state, e.g., bisulfite conversion, methylation-sensitive restriction enzyme, methylation-binding proteins. , anti-methylcytosine antibodies or single-molecule sequencing techniques (e.g., single-molecule real-time sequencing and nanopore sequencing (e.g., from Oxford Nanopore Technologies)) that recognize methylcytosines and hydroxymethylcytosines. nzoAnn / zznz / E / YiAi The mediation density of a region can refer to the number of reads at sites within the region that show methylation divided by the total number of reads covering sites in the region. Sites can have specific characteristics, for example being CpG sites. Therefore, the CpG methylation density of a region can refer to the number of reads showing CpG methylation divided by the total number of reads covering CpG sites in the region (e.g., a particular CpG site, CpG sites within a CpG island, or larger region). For example, the methylation density for each 100 kb interval in the human genome can be determined from the total number of unconverted cytosines after bisulfite treatment (corresponding to methylated cytosine) at CpG sites as a proportion of all the CpG sites covered by the read sequences mapped to the 100 kb region. This analysis can also be performed for other interval sizes, for example, 500 bp, 5 kb, 10 kb, 50 kb or 1 -Mb, etc. A region could be the entire genome or a chromosome or part of a chromosome (for example, a chromosome arm). The methylation index of a CpG site is the same as the methylation density for a region when the region only includes that CpG site. The proportion of methylated cytosines may refer to the number of cytosine sites, C, shown methylated (e.g., not converted after bisulfite conversion) over the total number of cytosine residues analyzed, i.e., includes cytosines outside of the CpG context, in the region. Methylation index, methylation density, count of molecules methylated at one or more sites, and proportion of molecules methylated (e.g., cytosines) at one or more sites are examples of methylation levels. In addition to bisulfite conversion, other processes known to those skilled in the art can be used to investigate the methylation status of DNA molecules, including, but not limited to, enzymes sensitive to methylation status (e.g. methylation-sensitive restriction enzymes), methylation-binding proteins, single-molecule sequencing using a platform sensitive to methylation status (e.g., nanopore sequencing (Schreiber et al. Proc Nati Acad Sci 2013; 110: 18910- 18915) and single-molecule real-time sequencing (e.g., that of Pacific Biosciences) (Flusberg et al. Nat Methods 2010; 7: 461-465)). A methylome provides a measure of the amount of DNA methylation at a plurality of sites or loci in a genome. The methylome can correspond to the entire genome, a substantial part of the genome, or a relatively small portion(s) of the genome. A “methylation profile includes information related to DNA or RNA methylation for multiple sites or regions. Information related to DNA methylation may include, but is not limited to, a methylation index of a CpG site, a methylation density (MD for short) of CpG sites in a region, a distribution of CpG sites in a contiguous region, a pattern or level of methylation for each individual CpG site within a region containing more than one CpG site, and non-methylation of CpG. In one embodiment, the methylation profile may include the pattern of methylation or non-methylation of more than one type of base (e.g., cytosine or adenine). A methylation profile of a substantial part of the genome can be considered equivalent to the methylome. DNA methylation in mammalian genomes typically refers to the addition of a methyl group to the 5' carbon of cytosine residues (i.e., 5-methylcytosines) between CpG dinucleotides. DNA methylation can occur on cytosines in other contexts, for example, CHG and CHH, where H is adenine, cytosine, or thymine. Cytosine methylation can also be in the form of 5-hydroxymethylcytosine. Non-cytosine methylation, such as N6-methyladenine, has also been reported. ηζοΑηη / ζζηζ / Ε / γίΛΐ A methylation pattern refers to the order of methylated and unmethylated bases. For example, the methylation pattern may be the order of methylated bases in a single strand of DNA, a single double-stranded DNA molecule, or another type of nucleic acid molecule. For example, three consecutive CpG sites can have any of the following methylation patterns: UUU, MMM, UMM, UMU, UUM, MUM, MUU, or MMU, where U indicates an unmethylated site and M indicates a methylated site. When extending this concept to base modifications that include, but are not restricted to, methylation, the term modification pattern would be used, which refers to the order of modified and unmodified bases. For example, the modification pattern may be the order of modified bases on a single strand of DNA, a single double-stranded DNA molecule, or another type of nucleic acid molecule. For example, three consecutive potentially modifiable sites can have any of the following modification patterns: UUU, MMM, UMM, UMU, UUM, MUM, MUU, or MMU, where U indicates an unmodified site and M indicates a modified site. An example of base modification that is not based on methylation is oxidative changes, such as in 8-oxo-guanine. The terms hypermethylated and hypomethylated may refer to the methylation density of a single DNA molecule measured by its single-molecule methylation level, for example, the number of methylated bases or nucleotides within the molecule divided by the total number of bases. or methylable nucleotides within that molecule. A hypermethylated molecule is one in which the methylation level of a single molecule is at or above a threshold, which can be defined from application to application. The threshold can be 5%, 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, or 95%. A hypomethylated molecule is one in which the methylation level of a single molecule is at or below a threshold, which can be defined from one application to another, and which can change from one application to another. The threshold can be 5%, 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, or 95%. The terms hypermethylated and hypomethylated can also refer to the level of methylation of a population of DNA molecules as measured by the methylation levels of multiple molecules of these molecules. A hypermethylated population of molecules is one in which the methylation level of multiple molecules is at or above a threshold that can be defined from one application to another, and which can change from one application to another. The threshold can be 5%, 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, or 95%. A hypomethylated population of molecules is one in which the methylation level of multiple molecules is at or below a threshold that can be defined from application to application. The threshold can be 5%, 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, and 95%. In one embodiment, the population of molecules can be aligned to one or more selected genomic regions. In one embodiment, the selected genomic region(s) may be related to a disease such as a genetic disorder, an imprinting disorder, a metabolic disorder, or a neurological disorder. Selected genomic regions can be 50 nucleotides (nt), 100 nt, 200 nt, 300 nt, 500 nt, 1000 nt, 2 knt, 5 knt, 10 knt, 20 knt, 30 knt, 40 knt, 50 knt in length. , 60 knt, 70 knt, 80 knt, 90 knt, 100 knt, 200 knt, 300 knt, 400 knt, 500 knt, or 1 Mnt. The term sequencing depth refers to the number of times a locus is covered by a sequence read aligned to the locus. The locus can be as small as a nucleotide, as large as a chromosome arm, or as large as the entire genome. Sequencing depth can be expressed as 50x, 10Ox, etc., where x refers to the number of times a locus is covered with a sequence read. Sequencing depth can also apply to multiple loci, or to the entire genome, in which case x can refer to the average number of times the loci or the haploid genome, or the entire genome, are sequenced, respectively. . Ultra-deep sequencing can refer to at least 10Ox in sequencing depth. A calibration sample may correspond to a biological sample whose clinically relevant fractional DNA concentration (e.g., tissue-specific DNA fraction) is known or determined by a calibration method, for example, using a tissue-specific allele, as in Transplantation in a pregnant woman in which an allele present in the donor's genome but absent in the recipient's genome can be used as a marker for the transplanted organ. As another example, a calibration sample may correspond to a sample from which final motifs can be determined. A calibration sample can be used for both purposes. A calibration data point includes a calibration value and a measured or known fractional concentration of the clinically relevant DNA (e.g., DNA from a particular tissue type). The calibration value can be determined from relative frequencies (e.g., an aggregate value) as determined for a calibration sample, for which the fractional concentration of the clinically relevant DNA is known. Calibration data points can be defined in several ways, for example, as discrete points or as a calibration function (also called calibration curve or calibration surface). The calibration function could be derived from a further mathematical transformation of the calibration data points. A separation value corresponds to a difference or a relationship involving two values, for example, two fractional contributions or two methylation levels. The separation value could be a simple difference or ratio. As examples, a direct ratio of x / y is a separation value, as is x / (x + y). The separation value may include other factors, for example, multiplicative factors. As other examples, a difference or ratio of functions of the values ​​may be used, for example, a difference or ratio of the natural logarithms (In) of the two values. A separation value can include a difference and a relationship. Separation value and an aggregate value (for example, relative frequencies) are two examples of a parameter (also called metric) that provides a measure of a sample that varies between different classifications (states), and therefore can be used to determine the different classifications. An aggregate value may be a separation value, for example, when a difference is taken between a set of relative frequencies of a sample and a set of relative frequencies of reference, as can be done in pooling. The term classification, as used herein, refers to any number or other characters associated with a particular property of a sample. For example, a + symbol (or the word positive) could mean that a sample is classified as having deletions or amplifications. The classification can be binary (for example, positive or negative) or have more levels of classification (for example, a scale of 1 to 10 or 0 to 1). The term parameter, as used herein, means a numerical value that characterizes a set of quantitative data and / or a numerical relationship between sets of quantitative data. For example, a ratio (or function of a ratio) between a first amount of a first nucleic acid sequence and a second amount of a second nucleic acid sequence is a parameter. The term size profile generally relates to the sizes of the DNA fragments in a biological sample. A size profile may be a histogram that provides a distribution of a number of ηζοΑηη / ζζηζ / Ε / γίΛΐ DNA fragments over a range of sizes. Various statistical parameters (also called size parameters or simply parameter) can be used to distinguish one size profile from another. A parameter is the percentage of DNA fragments of a particular size or size range relative to all DNA fragments or relative to DNA fragments of another size or range. The terms limit and threshold refer to predetermined numbers used in a trade. For example, a limit size may refer to a size above which fragments are excluded. A threshold value can be a value above or below which a particular classification applies. Any of these terms can be used in any of these contexts. A limit or threshold may be a reference value or derived from a reference value that is representative of a particular classification or discriminates between two or more classifications. Said reference value can be determined in various ways, as the expert will appreciate. For example, metrics can be determined for two different cohorts of subjects with different known classifications, and a reference value can be selected as representative of a classification (e.g., a mean) or a value that lies between two groupings of the metrics (e.g. example, chosen to obtain the desired sensitivity and specificity). As another example, a reference value may be determined based on statistical analysis or sample simulations. A particular value for a limit, threshold, reference, etc. can be determined based on a desired precision (e.g., sensitivity and specificity). A pregnancy-associated disorder includes any disorder characterized by abnormal relative expression levels of genes in maternal and / or fetal tissue or by abnormal clinical features in the mother and / or fetus. These disorders include, but are not limited to, preeclampsia (Kaartokallío et al. Sel Rep. 2015;5:14107; Medina-Bastidas et al. Int J Mol Sci. 2020;21:3597), intrauterine growth restriction (Faxén et al Am J PerinatoL 1998;15:9-13; MedinaBastidas et al. Int J Mol Sci. 2020;21:3597), invasive placentation, premature birth (Enquobahrie et al. BMC Pregnancy Childbirth. 2009;9:56), disease hemolytic disease of the newborn, placental insufficiency (Kelly et al. Endocrinology. 2017;158:743-755), hydrops fetalis (Magor et al. Blood. 2015;125:2405-17), fetal malformation (Slonim et al. Proc Nati Acad Sci USA. 2009;106:9425-9), HELLP syndrome (Dijk et al. J Clin Invest. 2012;122:4003-4011), systemic lupus erythematosus (Hong et al. J Exp Med. 2019;216:1154 -1169), and other immunological diseases of the mother. The abbreviation bp refers to base pairs. In some cases, bp may be used to indicate a length of a DNA fragment, although the DNA fragment may be single-stranded and does not include a base pair. In the context of single-stranded DNA, bp can be interpreted as giving the length in nucleotides. The abbreviation nf refers to nucleotides. In some cases, nt can be used to indicate a length of a single-stranded DNA in one base unit. Additionally, nt can be used to indicate relative positions, such as upstream or downstream of the locus being analyzed. For a double-stranded DNA, nt can still refer to the length of a single strand rather than the total number of nucleotides in the two strands, unless the context clearly indicates otherwise. In some contexts related to technological conceptualization, presentation, processing and analysis of data, nt and pb can be used interchangeably. The term machine learning models may include models based on using sample data (e.g., training data) to make predictions on test data and may therefore include supervised ηζοΑηη / ζζηζ / Ε / γίΛΐ learning. Machine learning models are often developed using a computer or processor. Machine learning models can include statistical models. The term data analysis framework can include algorithms and / or models that can take data as input and then generate a predicted outcome. Examples of data analysis frameworks include statistical models, mathematical models, machine learning models, other artificial intelligence models, and combinations of these. The term real-time sequencing can refer to a technique that involves collecting or monitoring data during the progress of a reaction involved in sequencing. For example, real-time sequencing may involve optical monitoring or filming of DNA polymerase incorporating a new base. The term subsequence can refer to a chain of bases that is smaller than the complete sequence corresponding to a nucleic acid molecule. For example, a subsequence may include 1,2,3, or 4 bases when the entire sequence of the nucleic acid molecule includes 5 or more bases. In some embodiments, a subsequence may refer to a chain of bases that form a unit where the unit is repeated several times in tandem series. Examples include 3 nt units or subsequences repeated at loci associated with trinucleotide repeat disorders, 1 nt to 6 nt units or subsequences repeated 5 to 50 times as microsatellites, 10 nt to 60 nt units or subsequences repeated 5 to 50 times as minisatellites, or in other genetic elements, such as Alu repeats. The term about or approximately may mean within an acceptable error range for the particular value as determined by one skilled in the art, which will depend in part on how the value is measured or determined, i.e., the limitations of the system. measurement. For example, approximately can mean within 1 or more than 1 standard deviation, depending on practice in the art. Alternatively, approximately can mean a range of up to 20%, up to 10%, up to 5% or up to 1% of a given value. Alternatively, particularly with respect to biological systems or processes, the term approximately or approximately may mean within an order of magnitude, within 5 times, and more preferably, within 2 times, of a value. When particular values ​​are described in the application and claims, unless otherwise indicated, the meaning of the term should be assumed approximately within an acceptable error range for the particular value. The term approximately may have the meaning commonly understood by one skilled in the art. The term approximately may refer to ±10%. The term approximately may refer to ±5%. Where a range of values ​​is given, it is understood that each intermediate value, to one-tenth of a unit of the lower limit unless the context clearly dictates otherwise, between the upper and lower limits of that range is also specifically disclosed. Each smaller range between any set value or intermediate value in a set range and any other set or intermediate value in that set range is encompassed within the embodiments of this disclosure. The upper and lower limits of these smaller ranges may be independently included or excluded in the range, and each range where neither or both limits are included in the smaller ranges are also included within the present description, subject to any limits specifically excluded in the indicated range. Where the stated range includes one or both limits, ranges that exclude one or both of the included limits are also included in this description. Standard abbreviations may be used, for example, bp, base pair(s); kb, kilobase(s); pi, picoliter(s); s or sec, second(s); min, minute(s); h or hr, hour(s); aa, amino acid(s); nt, nucleotide(s); and the like. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one skilled in the art to which this description pertains. Although any methods and materials similar or equivalent to those described herein may be used in the practice or testing of the embodiments of the present disclosure, some potential and illustrative methods and materials may now be described. DETAILED DESCRIPTION Analysis of circulating extracellular DNA molecules predominantly involves short fragments of circulating extracellular DNA, often as a result of limitations of analytical techniques. The limited ability to obtain sequence information from long DNA molecules using lllumina sequencing technology was demonstrated in recent sequencing results from circulating mouse extracellular DNA (Serpas et al., Proc Nati Acad Sci USA. 2019;116: 641-649). Only 0.02% of DNA molecules sequenced were within a range of 600 bp and 2000 bp using lllumina sequencing in wild-type mice. Even using Pacific Biosciences' single-molecule real-time (SMRT) technology (i.e., PacBio SMRT sequencing) to sequence the DNA libraries that were originally prepared for lllumina sequencing, there were still only 0.33% of the DNA sequenced within a range of 600 bp and 2000 bp. These reported data suggested that the sequencing step would miss 93% of the long DNA molecules within a range of 600 bp and 2000 bp present in the original DNA library. We speculate that the DNA library preparation step would also miss a considerable proportion of circulating extracellular long DNA molecules due to the limitation of PCR in the amplification of long DNA molecules described above. Jahr et al, using gel electrophoresis, reported the presence of large fragments of many kilobases, for example, -10,000 (Jahr et al. Cancer Res. 2001;61:1659-65). However, the bands shown in the gel electrophoresis image would not readily provide the sequence information for these molecules in the gel, much less provide the epigenetic information. We had previously used the Oxford Nanopore Technologies sequencing platform to study circulating extracellular DNA extracted from maternal plasma (Cheng et al Clin Chem. 2015;61:1305-6). We observed a very small proportion of long plasma DNA greater than 1 kb (0.06% to 0.3%). We hypothesize that such a low percentage could be a result of the low sequencing accuracy of this platform. In this field of circulating extracellular DNA, most studies focused on short DNA molecules (e.g., <600 bp). The properties, including genetic and epigenetic information, of circulating extracellular long DNA molecules have not been explored. This description provided a systemic way to analyze circulating extracellular long DNA molecules, including the decoding of their genetic and epigenetic information, as well as their clinical utilities in non-invasive prenatal testing, such as, limited, non-invasive detection of single genetic disorders, fetal genome elucidation (e.g., non-invasive whole fetal genome sequencing), genome-wide detection of de novo mutations, and detection / monitoring of pregnancy-associated disorders , such as preeclampsia and premature labor. SIZE ANALYSIS OF CIRCULATING EXTRACELLULAR DNA Circulating extracellular DNA samples obtained from pregnant women were sequenced and a significant portion of the DNA fragments were found to be long. Accurate sequencing of long fragments of circulating extracellular DNA was demonstrated. The size profiles of these long circulating extracellular DNA molecules were analyzed. The amounts of fetal and maternal long circulating extracellular DNA molecules were compared. Circulating extracellular long DNA molecules can be more precisely aligned to a reference genome. Circulating extracellular long DNA molecules can be used to determine haplotype inheritance. A plasma DNA sample from a pregnant woman in the third trimester was analyzed by PacBio SMRT sequencing. Circulating double-stranded extracellular DNA molecules were ligated with hairpin adapters and subjected to single-molecule read-time sequencing using zero-mode waveguides and single polymerase molecules (Eid etal. Science. 2009;323:133- 8). We sequenced 1.1 billion subreads, of which 659.3 million subreads could be aligned to a human reference genome (hg19). Subreads were generated from 4.6 million PacBio single molecular real-time (SMRT) sequencing wells that contained at least one subread that could be aligned to a human reference genome. On average, each molecule in a SMRT well was sequenced an average of 143 times. In this example, there were 4.5 million circular consensus sequences (CCS), suggesting 4.5 million circulating extracellular DNA molecules that could be used for further analysis. The size of each circulating extracellular DNA was determined from CCS by counting the number of bases that were identified. FIG 1A and 1B show the size distribution of circulating extracellular DNA from 0 to 20 kb. The y-axis shows the frequency. The x-axis shows the size in base pairs from 0 to 20 kb on a linear scale (FIG 1 A) or a logarithmic scale (FIG 1B). Because sequencing was performed along the entire length of the DNA molecules, the size of each DNA molecule could be determined directly by counting the number of nucleotides in a subread or CCS. Measurement of the size of DNA fragments could be achieved using any sequencing platform that could read the full length of DNA fragments and is not limited to the use of single-molecule sequencers. For example, Sanger sequencers could read up to 800 bp. Short-read sequencing, such as that on the lllumina platforms, could read up to 250 bp. Single-molecule sequencers, such as Pacific Biosciences and Oxford Nanopore, could read more than 10,000 bp. The sizes of the DNA fragments could also be determined after aligning them with the reference genome, for example, the human reference genome. The sizes of the DNA fragments could be determined by paired-end sequencing followed by alignment to the reference genome. FIG 1B shows a long tail pattern. Among 4.5 million CCS, there were 22.5% of circulating extracellular DNA greater than 200 bp, 19.0% of these greater than 300 bp, 11.8% of these greater than ηζοΑηη / ζζηζ / Ε / γίΛΐ 400 bp, 10.6% of these greater than 500 bp, 8.9% of these greater than 600 bp, 6.4% of these greater than 1 kb, 3.5% of these greater than 2 kb, 1.9% of these greater than 3 kb, 0.9 % of these greater than 4 kb and 0.04 % of these greater than 10 kb. The longest observed in the current PacBio SMRT results was 29,804 bp. Plasma DNA from a pregnant person was also sequenced on the lllumina sequencing platform using a PCR-based library preparation protocol (Lun et al. Clin Chem. 2013;59:1583-94). Among 18.2 million paired reads, there were 5.3% of circulating extracellular DNA greater than 200 bp, 2.0% of these greater than 300 bp, 0.3% of these greater than 400 bp, 0.2% of these greater than 500 bp, 0.2% of these greater than 600 bp (Table 1). As a comparison, we analyzed the size profiles by adding single-molecule real-time sequencing data (i.e., a total of 4.4 million CCS) from 5 pregnant individuals. We observed more plasma DNA molecules larger than 600 bp (28.56%), compared to the counterpart (0.2%) obtained by the lllumina sequencing platform. These results suggested that PacBio SMRT sequencing may make it possible to achieve 143 times longer DNA molecules (more than 600 bp). We can obtain 4.77% of plasma DNA molecules larger than 3 kb using single-molecule real-time sequencing, while there was no readout on the lllumina sequencing platform. In contrast to the previous report showing a very small proportion of long plasma DNA molecules greater than 1 kb (0.06% to 0.3%) using the Oxford Nanopore Technologies sequencing platform (Cheng et al Clin Chem. 2015;61 :1305-6), we were able to obtain 21 times more plasma DNA longer than 1 kb (6.4%), demonstrating that PacBio SMRT sequencing was much more efficient in obtaining sequence information from the long DNA population. Compared to paired-end short read sequencing, such as the lllumina sequencing platform, long read sequencing technologies, such as PacBio SMRT technology, have a number of advantages in determining the characteristics (e.g. length) of a long DNA fragment. For example, a long read would generally allow a more precise alignment to a human reference genome (e.g., hg19). Long read technologies would also allow the length of a DNA molecule in plasma to be accurately determined by directly counting the number of sequenced nucleotides. In contrast, plasma DNA size estimation based on short paired-end reads is an indirect method that uses the outermost coordinates of the aligned paired-end read to infer the size of a plasma DNA molecule. For such an indirect approach, alignment errors would result in an accurate size deduction. In this sense, an increase in the size interval between paired-end reads would increase the possibility of misalignment. Plasma DNA fragment size cutoff (>X bp) Percentage of desired fragments obtained by sequencing in Percentage of desired fragments obtained by the lllumina sequencing platform (%) single molecule real time (%) 200 50.32 5.3 300 46.43 2 400 35.05 0.3 500 32.34 0.2 600 28.56 0.2 700 26.74 0.00 800 24.50 0.00 900 23.08 0.00 10 00 21.37 0.00 1100 20.06 0.00 1200 18.60 0.00 1300 17.36 0.00 1400 16.08 0.00 1500 14.94 0.00 1600 13.84 0.00 1700 12.83 0.00 1800 11.88 0.00 1900 11.00 0.00 2000 10.19 0.00 2100 9.43 0.00 2200 8.75 0.00 2300 8.1 0 0.00 2400 7.51 0.00 2500 6.96 0.00 2600 6.45 0.00 2700 5.99 0.00 2800 5.55 0.00 2900 5.15 0.00 3000 4.77 0.00 Table 1. Comparison of size distribution between PacBio and lllumina sequencing of circulating extracellular DNA. ηζοΑηη / ζζηζ / Ε / γίΛΐ FIG 2A and 2B show the size distribution of circulating extracellular DNA from 0 to 5 kb. The y-axis shows the frequency. The x-axis shows the size in base pairs from 0 to 5 kb on a linear scale (FIG 2A) or a logarithmic scale (FIG 2B). There were a series of major spikes that occurred in periodic patterns. Such periodic patterns even extended to molecules within a range of 1 kb and 2 kb. The peak with the highest frequency (2.6%) was at 166 bp, which was consistent with the previous finding using lllumina technology (Lo et al. Sci Transí Med. 2010;2:61ra91). The distance between adjacent major peaks in FIG 2B was approximately 200 bp, suggesting that the generation of long circulating extracellular DNA would also involve nucleosomal structures. FIG 3A and 3B show the size distribution of circulating extracellular DNA from 0 to 400 bp. The y-axis shows the frequency. The x-axis shows the size in base pairs from 0 to 400 bp on a linear scale (FIG 3A) or a logarithmic scale (FIG 3B). The characteristic features with a more predominant peak at 166 bp and 10 bp periodicities occurring in molecules below 166 bp, which were previously reported (Lo etal. Sci Transí Med. 2010;2:61 ra91), were also reproduced using the new method according to this description. These results suggested that determining the size of a molecule by counting the number of bases sequenced from a single molecule according to this description was reliable. A. Size analysis for fetal and maternal DNA The sizes of maternal and fetal DNA fragments were analyzed and compared. As an example, buffy coat DNA from a pregnant woman and paired placental DNA were sequenced to obtain 59x and 58x haploid genome coverage, respectively. We identified a total of 822,409 informative single nucleotide polymorphisms (SNPs) for which the mother was homozygous and the fetus was heterozygous. Fetus-specific alleles are defined as those alleles that are present in the fetal genome but absent in the maternal genome. We identified 2,652 fetus-specific fragments and 24,837 shared fragments (i.e., fragments carrying the shared allele; predominantly of maternal origin) in maternal plasma (M13160) by PacBio sequencing. The fetal DNA fraction was 21.8%. FIG 4A and 4B show the size distribution of circulating extracellular DNA between fragments carrying shared alleles (Shared) and fetus-specific alleles (Fetus-Specific). The x-axis shows the size in base pairs from 0 to 20 kb on a linear scale (FIG 4A) or a logarithmic scale (FIG 4B). Both fragments carrying shared alleles (predominantly of maternal origin) and fetus-specific alleles (of placental origin) showed long-tailed distributions, suggesting the presence of long DNA molecules derived from fetal and maternal sources. There were 22.6% of plasma DNA molecules whose size exceeded 2 kb for the fragments of primarily maternal origin, while there were 8.5% of plasma DNA molecules whose size exceeded 2 kb for the fragment of fetal origin. These results suggested that fetal DNA molecules contained fewer long DNA molecules. The percentage of long DNA present in this SNP-based analysis with respect to the fetal and maternal origins of plasma DNA was apparently much higher than that observed in the overall size analysis. Such a discrepancy was probably due to the fact that a long DNA molecule has a higher probability of covering one or more SNPs than a short one and therefore long DNA would be favorably selected for analysis based on ηζοΑηη / ζζηζ / Ε / γίΛΐ SNP. The relative proportion of long DNA molecules marked by biased SNPs from the corresponding proportion of long DNA in the original pool would be governed by the sizes of those molecules. Among those fetus-specific DNA fragments, the longest was 16,186 bp, while among the fragments carrying shared alleles, the longest was 24,166 bp. FIG 5A and 5B show the size distribution of circulating extracellular DNA between fragments carrying shared alleles (Shared) and fetus-specific alleles (Fetus-Specific). The x-axis shows the size in base pairs from 0 to 5 kb on a linear scale (FIG 5A) or a logarithmic scale (FIG 5B). There were a series of major peaks that occurred periodically for those fragments below 2 kb for shared and fetus-specific DNA fragments. The main peaks were probably aligned with nucleosomal structures. FIG 6A and 6B show the size distribution of circulating extracellular DNA between fragments carrying shared alleles (Shared) and fetus-specific alleles (Fetus-Specific). The x-axis shows the size in base pairs from 0 to 1 kb on a linear scale (FIG 6A) or a logarithmic scale (FIG 6B). There were a series of major peaks that occurred periodically for those fragments below 1 kb for shared and fetus-specific DNA fragments. The main peaks were probably aligned with nucleosomal structures. There appeared to be an observable shift of the fetal DNA size profile to the left of the size profile of the shared DNA fragments, suggesting that fetal DNA would comprise shorter DNA molecules than maternal DNA. FIG 7A and 7B show the size distribution of circulating extracellular DNA between fragments carrying shared alleles (Shared) and fetus-specific alleles (Fetus-Specific). The x-axis shows the size in base pairs from 0 to 400 bp on a linear scale (FIG 7A) or a logarithmic scale (FIG 7B). The characteristic features with a most predominant peak at 166 bp and 10 bp periodicities occurring in fetal and maternal molecules below 166 bp, which was previously reported (Lo et al. Sci Transí Med. 2010; 2: 61 ra91) , were also reproducible using the new method according to this description. These results suggested that determining the size of a molecule by counting the number of bases sequenced from a single molecule according to this description was reliable. B. Methylation and size analysis Methylation levels of circulating extracellular maternal and fetal long DNA molecules were analyzed. The methylation level of fetal DNA molecules was found to be lower than the methylation level of maternal DNA molecules. In PacBio SMRT sequencing, a DNA polymerase mediates the incorporation of fluorescently labeled nucleotides into complementary strands. The characteristics of the fluorescent pulses produced during DNA synthesis, including inter-pulse duration and pulse width, would reflect polymerase kinetics that could be used to determine nucleotide modifications such as, but not limited to, 5-methylcytosine using the approaches described in our previous description (U.S. Application No. 16 / 995,607, filed August 17, 2020, entitled “DETERMINATION OF BASE MODIFICATIONS OF NUCLEIC ACIDS”), the entire contents of which are incorporated herein by reference for all purposes. ηζοΑηη / ζζηζ / Ε / γίΛΐ Across modalities, we identified 95,210 fragments carrying maternal-specific alleles and 2,652 fragments carrying fetus-specific alleles, respectively. Maternal-specific alleles are defined herein as those alleles present in the maternal genome but absent in the fetal genome, which could be identified from SNPs in which the mother is heterozygous and the fetus is homozygous. We identified a total of 677,375 informative SNPs in this example. We determine the size of each circulating extracellular DNA molecule. In an embedding, as methylation states in a genome are variable, for example the methylation levels of CpG islands are generally lower than regions without CpG islands, to minimize the variability introduced by genomic context, one could, In silico, select fragments, which are more than 1 kb, contain at least 5 CpG sites and correspond to a CpG density less than 5% (i.e., the number of CpG sites in a molecule divided by the total length of that molecule <0.05), were used for subsequent analysis. FIG 8 shows the levels of single-molecule double-stranded DNA methylation between fragments carrying the mother-specific alleles and the fetus-specific alleles. The y-axis shows the methylation level of single-molecule double-stranded DNA in percent. The x-axis shows both fragments carrying maternally specific alleles and fragments carrying fetus-specific alleles. Single-molecule double-stranded DNA methylation levels of fragments carrying fetus-specific alleles (mean: 62.7%; interquartile range, IQR: 50.0% 77.2%) are lower than those of fragments carrying maternal-specific alleles (mean: 72.7%; IQR: 60.6% 83.3%) (P < 0.0001). FIG 9A shows the empirical distribution of single-molecule double-stranded DNA methylation levels of the fragments fitted by kernel density estimation implemented in the R package (r-proyect.org / ). The frequency is shown on the y-axis. The x-axis shows the methylation level of single-molecule double-stranded DNA in percent. The distribution of long fetal-specific DNA fragments is to the left of that of maternal-specific fragments, suggesting the lower levels of double-stranded DNA methylation present in fetal DNA molecules. FIG 9B shows receiver operating characteristic (ROC) analysis using single-molecule double-stranded DNA methylation levels. The y-axis shows the sensitivity. The x-axis shows specificity. Using a single molecule, double-stranded DNA methylation levels to perform ROC analysis to investigate the power of distinguishing fetal DNA fragments from maternal DNA fragments using a single-molecule double-stranded DNA methylation level, it was found that the area under the ROC curve (AUC) was 0.62, which was greater than the random guessing result of 0.5. In embodiments, spatial patterns of methylation states, such as the sequence of methylation states, relative or absolute distances between modified bases, and genomic coordinates, could be used in a single molecule to further improve determination of the methylation states. maternal / fetal origins of the fragments in plasma. In embodiments, methylation patterns could be combined with other fragmentomic metrics (i.e., parameters related to DNA fragmentation), including, but not limited to, preferred ends (Chan et al. Proc Nati Acad Sel USA. 2016 ;113:E8159-8168), final motifs (Serpas et al. Proc Nati Acad Sel USA. 2019;116:641649), sizes (Lo et al. Sel Transí Med. 2010;2:61ra), conscious orientation (i.e. , orientation with respect to specific elements within the genome, for example, open chromatin regions, fragmentation patterns (Sun et al. nzoAnn / zznz / E / YiAi Genomes Res. 2019;29:418-427)), topological shapes (e.g., linear versus circular DNA molecules (Ma et al. Clin Chem. 2019;65:1161-1170)), to improve the classification power of distinguish fragments of placental origin (fetal origins). FIG 10A and 10B show that the single-molecule double-stranded DNA methylation levels of the fetal and maternal DNA fragments varied according to the sizes of the fragments. The y-axis shows the methylation level of single-molecule double-stranded DNA in percent. The x-axis shows the size from 0 to more than 20 kb (FIG 10A) and from 0 to more than 1 kb (FIG 10B). On the other hand, single-molecule double-stranded DNA methylation levels of fetus-specific DNA molecules were generally lower than those of maternal-specific DNA molecules at both long (FIG 10A) and short ranges (FIG 10B). This finding was consistent with the current knowledge that the methylation level of fetal DNA was lower than that of maternal DNA in the plasma of a pregnant woman (Lun etal. Clin Chem. 2013;59:1583-94) for the molecules short DNA. In embodiments, since the methylation level of fetal DNA molecules is relatively lower than that of maternal DNA molecules, molecules whose single-molecule double-stranded DNA methylation levels are less than a certain threshold would be selected, such as, but not limited to, 80%, 70%, 60%, 50%, 40%, 30%, 20%, 10% and 5%, to enrich circulating extracellular DNA molecules of fetal origin in the DNA pool in plasma. For example, the fraction of fetal DNA is 2.6% for fragments > 1 kb. If we select fragments (>1 kb) with a single-molecule double-strand methylation level of <50%, the fraction of fetal DNA from those additional selected fragments >1 kb will increase up to 5.6% (i.e., an increase of 115.4 %). In another example, the fraction of fetal DNA is 26.2% for fragments <200 bp. If we select the fragments (<200 bp) with a single-molecule double-strand methylation level of <50%, the fetal DNA fraction of those additional selected fragments >200 bp will increase to 41.6% (i.e., 58.8%). . Therefore, using single-molecule double-stranded DNA methylation level thresholds to enrich fetal DNA would be more effective for long DNA molecules under certain circumstances. C. Haplotype and methylation of long circulating extracellular DNA In embodiments, base compositions, sizes, and base modifications could be obtained for each individual DNA molecule using the methods described in this disclosure. SNP and methylation information from long circulating extracellular DNA molecules can be used for haplotype determination. The use of long DNA molecules present in the pool of circulating extracellular DNA disclosed in this description would allow the staging of variants in the genomes taking advantage of the haplotype information present in each consensus sequence, according to, but not limited to, the methods published (Edge et al. Genome Res. 2017;27:801-812; Wenger et al. Nat Biotechnol. 2019;37:1155-1162). The implementation of haplotype determination according to the sequence information of circulating extracellular DNA, which is different from previous studies that have to rely on long DNA prepared from tissue DNA. A haplotype within a genomic region may sometimes be called a haplotype block. A block of haplotypes could be thought of as a set of alleles on a chromosome that have been analyzed in phases. In some embodiments, a block of haplotypes will be spread as far as possible according to ηζοΑηη / ζζηζ / Ε / γίΛΐ with a set of sequence information that supports two alleles physically linked on a chromosome, as well as allelic overlap information between different sequences. . FIGS. 11A and 11B show an example of a long fetus-specific DNA molecule identified in the maternal plasma DNA of a pregnant woman. Among those fetal-specific DNA fragments, we hereby illustrate embodiments of our invention using a molecule that was 16,186 bp, which was aligned to a region on chromosome 10 of the human reference genome (chr10:56282981-56299166) (FIG 11 A) and carried 7 fetus-specific alleles (FIG 11B). There were 6 of 7 fetus-specific alleles that were consistent with allelic information deduced from deep sequencing of the maternal and fetal genomes (using the lllumina platform) (FIG 11B). Its methylation level was determined to be 27.1% according to the method described in this description (FIG 11B), which was much lower than the average level of mother-specific fragments (72.7%). These results suggested that single-molecule double-stranded DNA methylation patterns would serve as markers to differentiate circulating extracellular DNA molecules of fetal and maternal origin. FIG 12A and 12B show an example of a long maternal DNA molecule carrying shared alleles identified in the maternal plasma DNA of a pregnant woman. Among those fragments carrying shared alleles, the longest was 24,166 bp that was aligned with a region on chromosome 6 of a human reference (chr6: 111074371-111098536) (FIG 12A) and carried 18 shared alleles (FIG 12B). All of those shared alleles were consistent with allelic information deduced from deep sequencing of the maternal and fetal genomes (using the lllumina platform) (FIG 12B). Its methylation level was determined to be 66.9% according to the method described in this description (FIG 12B). The genetic and epigenetic information of circulating extracellular DNA molecules on the order of kilobases in length could not be easily identified using short read sequences such as bisulfite (lllumina) sequencing. Here we describe a method for determining the relative probability that a molecule is derived from the pregnant woman or the fetus. In a pregnant woman, the DNA molecules carrying fetal genotypes are actually derived from the placenta, while most of the DNA molecules carrying maternal genotypes are derived from maternal blood cells. In this method, we first constructed a frequency distribution curve of DNA molecules according to their methylation level for both placenta and maternal blood cells. To achieve this, we divide the human genome into intervals of different sizes. FIG 13 shows the frequency distribution of DNA from placental (red) and maternal (blue) blood cells according to the methylation level at different resolutions from 1 kb to 20 kb. The frequency is shown on the y-axis. The methylation level is shown on the x-axis. Examples of interval sizes include, but are not limited to, 1 kb, 2 kb, 5 kb, 10 kb, 15 kb, and 20 kb. The methylation level of each interval was determined based on the number of methylated CpG sites divided by the total number of CpG sites. After determining the methylation level of all bins, a frequency distribution curve can be constructed for each of the placental genomes and the maternal blood cell genome, for different bin sizes. nzoAnn / zznz / E / YiAi Based on the methylation level of the long DNA molecule, the probability that it is derived from the placenta or maternal blood cells can be determined by the relative abundance of the two types of DNA molecules at that methylation level, as well as as by the fractional concentration of fetal DNA in the sample. Let x and y be the frequency of DNA molecules derived from placenta and maternal blood cells, respectively, at a particular methylation level, and f be the fractional concentration of fetal DNA in the sample. The probability (P) that a DNA molecule is derived from the fetus can be calculated as: x x fp = From the previous example, a plasma DNA molecule of 16 kb and a methylation level of 27.1% is considered. FIG 14A and 14B show the frequency distribution of placental (red) and maternal (blue) blood cell DNA according to methylation levels within 16 kb (FIG 14A) and 24 kb (FIG 14B) windows. The frequency is shown on the y-axis. The methylation level is shown on the x-axis. Based on the frequency distribution graph for 16 kb fragments (FIG 14A), the frequencies for DNA molecules derived from placenta and maternal blood cells are 0.6% and 0.08%, respectively. Since the fetal DNA fraction is 21.8%, the probability that this DNA fragment is derived from the placenta is 64%, suggesting a higher probability of a placental origin. The probability that a DNA molecule is derived from fetal tissues can also be calculated for the plasma DNA molecule of 24 kb and a methylation level of 66.9%. Based on the frequency distribution graph for 24 kb fragments, the frequencies for DNA molecules derived from placenta and maternal blood cells are 0.05% and 0.16% (FIG 14B), respectively. The probability that this DNA fragment is derived from the placenta is 0.8%, suggesting that it is very unlikely to be of placental origin. In other words, there is a high probability that the molecule is of maternal origin. This calculation can further take into account the size of the DNA molecules by referring to the size distribution curves for maternal and fetal DNA. Such analysis can be performed, for example, but not limited to, using Bayes' theorem, logistic regression, multiple regression and support vector machine, random forest analysis, classification and regression tree (CART), plus neighbors algorithm. close to K. FIG 15A and 15B show that a long plasma DNA fragment has a size of 18,896 bp that aligned with a region on chromosome 8 of a human reference (chr8:108694010-108712904) (FIG 15A) and carried 7 specific alleles of the mother (FIG 15B). All of these maternally specific alleles were consistent with allelic information deduced from deep sequencing of the maternal and fetal genomes (lllumina technology) (FIG 15B). Its methylation level was determined to be 72.6% according to the method described in this description (FIG 15B), showing a methylation level comparable to the set of mother-specific fragments (72.7%). Therefore, such a molecule would most likely be classified as a fragment of maternal origin. The genetic and epigenetic information of circulating extracellular DNA molecules on the order of kilobases in length could not be easily identified using short read sequences such as bisulfite (lllumina) sequencing. ηζοΑηη / ζζηζ / Ε / γίΛΐ Using the method described above, the probability that this molecule is derived from the placenta can be calculated. According to the frequency distribution plot for 19 kb fragments, the frequencies of DNA molecules derived from placenta and maternal blood cells are 0.65% and 0.23%, respectively. The probability that this DNA fragment is derived from the placenta is 43%, suggesting a higher probability that it is of maternal origin. D. Clinical applications of haplotyping In embodiments, the ability to analyze both short and long DNA molecules in a pregnant woman's plasma DNA would allow us to perform relative haplotype dosage (RHDO) analysis (Lo et al. Sci Transí Med. 2010;2:61ra91 ; Hui et al. al. Clin Chem. 2017;63:513-524) without the requirement of prior information on the paternal or maternal or fetal genotype obtained from the tissues. This capability would be more cost-effective and clinically applicable than previously possible. FIG 16 illustrates this principle of how extracellular DNA circulating in pregnancy could be used to carry out RHDO analysis. Circulating extracellular DNA is isolated from a pregnant woman and subjected to SMRT sequencing at step 1605. The sizes, allelic information and methylation states of each molecule, including long and short DNA molecules, can be determined according to the methods described in this description. At step 1610, according to the size information, the sequenced molecules could be divided into two categories, namely, long and short DNA molecules. The cutoff used to determine long and short DNA categories could include, but is not limited to, 150 bp, 180 bp, 200 bp, 250 bp, 300 bp, 350 bp, 400 bp, 450 bp, 500 bp, 550 bp, 600 bp, 650 bp, 700 bp, 750 bp, 800 bp, 850 bp, 900 bp, 950 bp, 1 kb, 1.1 kb, 1.2 kb, 1.3 kb, 1.4 kb, 1.5 kb, 1.6 kb, 1.7 kb, 1.8 kb , 1.9 kb, 2 kb, 2.5 kb, 3 kb, 4 kb, 5 kb, 6 kb, 7 kb, 8 kb, 9 kb, 10 kb, 15 kb, 20 kb, 30 kb, 40 kb, 50 kb, 60 kb, 70 kb, 80 kb, 90 kb, 100 kb, 200 kb, 300 kb, 400 kb, 500 kb, or 1 Mb. In step 1615, in embodiments, allelic information present on long DNA molecules could be used to construct maternal haplotypes, specifically Hap I and Hap II. Short DNA molecules could align with maternal haplotypes according to allelic information. Therefore, the number of circulating extracellular DNA molecules (e.g., short DNA) originating from maternal Hap I and Hap II could be determined. At step 1620, a haplotype imbalance can be analyzed. The imbalance may be molecular count, molecular size, or molecular methylation states. At stage 1625 the maternal inheritance of the fetus can be deduced. If the dose of Hap I in maternal plasma DNA is overrepresented, the fetus is likely to inherit maternal Hap I. Otherwise, the fetus is likely to inherit maternal Hap II. Different statistical approaches would be used, including, but not limited to, sequential likelihood ratio test (SPRT), binomial test, chi-square test, Student's t test, non-parametric tests (e.g. Wilcoxon test) and hidden Markov models, to determine which haplotype is overrepresented. In addition to counting analysis, in embodiments, methylation and the size of a short DNA molecule are also determined and assigned to maternal haplotypes. The methylation imbalance between the two haplotypes (i.e., Hap I and Hap II) could be used to determine the maternal haplotype inherited by the fetus. If the fetus has inherited Hap ηζοΑηη / ζζηζ / Ε / γίΛΐ I, more fragments carrying Hap I alleles would be present in maternal plasma compared to those carrying Hap II alleles. Hypomethylation of fetal-derived DNA fragments would reduce the methylation level of Hap I compared to that of Hap II. In other words, if Hap I methylation showed a lower methylation level than Hap II, the fetus would be more likely to inherit maternal Hap I. Otherwise, the fetus would be more likely to inherit maternal Hap II. In another embodiment, the probability that the individual fragments are derived from the fetus or the mother can be calculated as described above. For all fragments that align with Hap I, an aggregate probability that these fragments are derived from the fetus can be determined based on Bayes' Theorem. Similarly, the aggregate probability that these fragments are derived from the fetus can be calculated for Hap II. The probability that the fetus inherits Hap I or Hap II can be inferred based on the two added probabilities. In embodiments, lengthening or shortening in size between the two haplotypes (i.e., Hap I and Hap II) could be used to determine the maternal haplotype inherited by the fetus. If the fetus has inherited Hap I, more fragments carrying Hap I alleles would be present in the maternal plasma compared to those carrying Hap II alleles. DNA fragments derived from the fetus would be relatively shorter than those derived from Hap II. In other words, if molecules originating from Hap I contain more short DNA than Hap II, the fetus is more likely to inherit maternal Hap I. Otherwise, the fetus is more likely to inherit maternal Hap II. In some embodiments, a combined analysis of count, size and methylation between maternal Hap I and Hap II could be performed to infer maternal inheritance of the fetus. For example, logistic regression could be used to combine those three metrics, including counts, sizes, and methylation states. In clinical practice, haplotype-based analysis with respect to counts, sizes, and methylation status would allow determination of whether an unborn fetus has inherited the maternal haplotype associated with genetic disorders, e.g., but not limited to, singleton disorders. gene, including fragile X syndrome, muscular dystrophy, Huntington's disease or beta-thalassemia. Detection of disorders involving DNA sequence repeats in long circulating extracellular reads is described separately in this description. E. Targeted Sequencing of Long Circulating Extracellular DNA Molecules The methods described in the current description can also be applied to analyze one or more selected long DNA fragments. In embodiments, one or more long DNA fragments of interest may first be enriched by a hybridization method that allows hybridization of DNA molecules from the region(s) of interest with synthetic oligonucleotides with complementary sequences. To decode size, genetic and epigenetic information all in one using the methods described in the current description, it is preferred that the target DNA molecules are not amplified by PCR before subjecting them to sequencing because the base modification information in the molecule Original DNA would not transfer to PCR products. Several methods have been developed to enrich for these target regions without performing PCR amplification. In another embodiment, the one or more target long DNA molecules can be enriched using clustered regularly interspaced short palindromic repeats (CRISPR)-CRISPR-associated protein 9 (Cas9) system (Stevens et al. PLOS One 2019;14(4): e0215441; Watson et al. Lab Invest 2020;100:135-146). Although such cuts mediated by ηζοΑηη / ζζηζ / Ε / γίΛΐ CRISPR-Cas9 would alter the size of the original long DNA molecules, their genetic and epigenetic information is still conserved and can be obtained using the methods described in this description, including, but not limited to, base content, haplotype information (i.e. i.e. phase), de novo mutations, base modifications (e.g., 4mC (N4-methylcytosine), 5hmC (5-hydroxymethylcytosine), 5fC (5-formylcytosine), 5caC (5-carboxylcytosine), 1mA(N1- methyladenine), 3mA (N3-methyladenine), 7mA (N7-methyladenine), 3mC (N3-methylcytosine), 2mG (N2-methylguanine), 6mG (O6-methylguanine), 7mG (N7-methylguanine), 3mT (N3 -methylthymine), 4mT (O4-methylthymine) and 8oxoG (8-oxo-guanine). In embodiments, the ends of DNA molecules in a DNA sample are first dephosphorylated which renders them not susceptible to ligation to adapters sequencing directly. Next, long DNA molecules of interest are targeted by the Cas9 protein with guide RNA (crRNA) to create double-stranded cuts. Molecules of interest spanned by double-strand cuts on both sides would then be ligated to sequencing adapters specified by the sequencing platform of choice. In another embodiment, DNA can be treated with exonuclease so that DNA molecules not bound by Cas9 proteins are degraded (Stevens et al. PLOS One 2019;14(4):e0215441). Since these methods do not involve PCR amplification, the original base-modified DNA molecules can be sequenced and the base modification would be determined. In embodiments, these methods can be used to target a large number of long DNA molecules that share homologous sequences by designing the guide RNAs with reference to a reference genome such as a human reference genome (hg 19), e.g. the long interspersed nuclear element (LINE) repeats. In one example, such an assay can be used for the analysis of circulating extracellular DNA circulating in maternal plasma for the detection of fetal aneuploidies (Kinde et al. PLOS One 2012;7(7):e41162. In embodiments, Cas9 inactivated or dead (dCas9) and its associated single guide RNA (sgRNA) can be used to enrich the target long DNA without cutting the double-stranded DNA molecules. For example, the 3' end of the sgRNA could be designed to support an extra universal short sequence. use biotinylated single-stranded oligonucleotides complementary to that universal short sequence to capture those target long DNA molecules bound by dCas9. In another embodiment, biotinylated dCas9 protein or sgRNA, or both, could be used to facilitate enrichment. In embodiments, size selection can be performed to enrich long DNA fragments without being restricted to one or more particular genomic regions of interest, using approaches including, but not limited to, chemical, physical, enzymatic, gel-based, and cell-based methods. on magnetic beads, or methods that combine more than such approaches. In other embodiments, immunoprecipitation can be used to enrich DNA fragments of a certain methylation profile, such as mediated by the use of anti-methylcytosine antibodies and methyl binding proteins. The methylation profile of bound or captured DNA could be determined using methylation-unaware sequencing. F. General concepts for the analysis of fetal inheritance based on long DNA molecules in plasma FIG 17 illustrates the determination of genetic / epigenetic disorders in a plasma DNA molecule with information on maternal and fetal origins. A long DNA molecule in plasma could be determined to be nzoAnn / zznz / E / YiAi of fetal or maternal origin in a pregnant woman according to the genetic and / or epigenetic profile of CpG sites on all or part of the molecule [i.e. region (a)]. Genetic information may be, but is not limited to, sequence information, single nucleotide polymorphisms, insertions, deletions, tandem repeats, satellite DNA, microsatellite, minisatellite, inversions, etc. Epigenetic information can be the methylation status of one or more CpG sites as well as their relative orders in a plasma DNA molecule. In another embodiment, the epigenetic information may be a modification of any of A, C, G or T. Long plasma DNA with information of tissue origin could be used for non-invasive prenatal testing determining the presence of genetic and / or epigenetic disorders. in such a long DNA molecule in plasma [i.e., region (b)]. FIG 18 illustrates the identification of aberrant fetal fragments. As an example, a long DNA fragment was identified to be of fetal origin based on the methylation patterns of region (a) according to this description. The probability that a fetus is affected by a genetic or epigenetic disorder could be determined based on said molecule of fetal origin. Genetic disorders can include single nucleotide variants, insertions, deletions, tandem repeats, satellite DNA, microsatellites, minisatellites, inversions, etc. Examples of genetic disorders include, but are not limited to: beta-thalassemia, alpha-thalassemia, sickle cell anemia, cystic fibrosis, sex-linked genetic disorders (e.g., hemophilia, Duchenne muscular dystrophy), spinal muscular atrophy, hyperplasia congenital adrenal, etc. Epigenetic disorders my aberrant levels of DNA methylation, for example, methylation gains (i.e. hypermethylation) or losses (hypomethylation). Examples of epigenetic disorders include, but are not limited to, fragile IGF), etc. The genetic or epigenetic disorder may be present in region (b). G. Improving sequencing accuracy Sequencing accuracy can be improved with sequence reads of long fragments of circulating extracellular DNA. In FIG 11B, among 7 alleles in a long fetal-specific DNA molecule, there was 1 allele that appeared to be inconsistent between PacBio and lllumina sequencing. FIGS. 19A-19G show illustrations of genotyping error correction of circulating extracellular DNA using PacBio sequencing. We visualize the subread alignment results for those 7 sites in FIG 11B. The 1st row indicates the genomic coordinates; the 2nd row is a reference sequence. The 3rd row and later indicate the aligned subreadings. For example, in FIG 19A, there are 8 subreads crossing that region. represents an identical base to the reference base in the Watson chain. represents a base identical to the reference in the Crick chain. 'Alphabet letter' represents an alternative allele. '*' represents an indel. It could be seen that the inconsistent site shown in FIG 19F, the parent base was named T in the consensus sequence. However, among 9 subreads at that site (FIG 19F), only 5 of 9 subreads (i.e., major allele fraction (MAF) of 56%) were determined to be 'Τ', while the others were determined to be 'C' The major allele fraction of this site (FIG 19F) was lower than that of other sites (FIG 19A-E and FIG 19G) (MAF range: 67 - 89%). Therefore, if strict criteria are established to determine the base compositions for each site in a consensus sequence, for example, using ηζοΑηη / ζζηζ / Ε / γίΛΐ MAF at least 60%, this error site will be discarded for further interpretation. On the other hand, such an erroneous site was within a homopolymer (i.e., a series of consecutive identical bases, ‘TTTTTTT’). In embodiments, a criterion could be established by which variants within a homopolymer will be flagged as quality control failures and temporarily not used for further analysis. In embodiments, different mapping qualities and base qualities could be applied to correct or filter out low quality base or underreads to improve base composition analysis. With further improvements in the sequencing accuracy of nanopore sequencing, embodiments of the present invention can also be used with such an improved sequencing platform and therefore result in improved accuracy. H. Example methods Long fragments of circulating extracellular DNA can be sequenced from biological samples obtained from pregnant women with circulating extracellular DNA fragments. These long fragments of circulating extracellular DNA can be used to determine the inheritance of a haplotype by a fetus. 1. Sequencing of long fragments of circulating extracellular DNA FIG 20 shows a method 2000 for analyzing a biological sample of a pregnant organism. The biological sample may include a plurality of circulating extracellular nucleic acid molecules. The biological sample may be any biological sample described herein. More than 20% of the circulating extracellular nucleic acid molecules in the biological sample have sizes larger than 200 nt (nucleotides). In block 2010, a plurality of circulating extracellular nucleic acid molecules are sequenced. Sequencing can be by a single molecule real-time technique. In some embodiments, sequencing can be performed using a nanopore. More than 20% of the plurality of sequenced circulating extracellular nucleic acid molecules may have lengths greater than 200 nt. In some embodiments, 15-20%, 20-25%, 25-30%, 30-35%, or more than 35% of the plurality of sequenced circulating extracellular nucleic acid molecules may have lengths greater than 200 nt. In some embodiments, more than 11% of the plurality of sequenced circulating extracellular nucleic acid molecules may have lengths greater than 400 nt. In embodiments, 5-10%, 10-15%, 15-20%, 20-25%, or more than 25% of the plurality of sequenced circulating extracellular nucleic acid molecules may have lengths greater than 400 nt. In some embodiments, more than 10% of the plurality of sequenced circulating extracellular nucleic acid molecules may have lengths greater than 500 nt. In embodiments, 5-10%, 10-15%, 15-20%, 20-25%, or more than 25% of the plurality of sequenced circulating extracellular nucleic acid molecules may have lengths greater than 500 nt. ηζοΑηη / ζζηζ / Ε / γίΛΐ In embodiments, more than 8% of the plurality of sequenced circulating extracellular nucleic acid molecules may have lengths greater than 600 nt. In embodiments, 5-10%, 10-15%, 15-20%, 20-25%, or more than 25% of the plurality of sequenced circulating extracellular nucleic acid molecules may have lengths greater than 600 nt. In some embodiments, more than 6% of the plurality of sequenced circulating extracellular nucleic acid molecules may have lengths greater than 1 nt. In embodiments, 3-5%, 5-10%, 10-15%, 15-20%, 20-25%, or more than 25% of the plurality of sequenced circulating extracellular nucleic acid molecules may have lengths greater than 1 knt. In embodiments, more than 3% of the plurality of sequenced circulating extracellular nucleic acid molecules may have lengths greater than 2 nt. In embodiments, 1-5%, 5-10%, 10-15%, 15-20%, 20-25%, or more than 25% of the plurality of sequenced circulating extracellular nucleic acid molecules may have lengths greater than 2 knt. In embodiments, more than 1% of the plurality of sequenced circulating extracellular nucleic acid molecules may have lengths greater than 3 nt. In embodiments, 1-5%, 5-10%, 10-15%, 15-20%, 20-25%, or more than 25% of the plurality of sequenced circulating extracellular nucleic acid molecules may have lengths greater than 3 knt. In some embodiments, at least 0.9% of the plurality of circulating extracellular nucleic acid molecules sequenced may have lengths greater than 4 nt. In embodiments, 0.5-1%, 1-5%, 5-10%, 10-15%, 15-20% or more than 20% of the plurality of sequenced circulating extracellular nucleic acid molecules may have lengths greater than 4 knt. In some embodiments, at least 0.04% of the plurality of circulating extracellular nucleic acid molecules sequenced may have lengths greater than 10 nt. In embodiments, from 0.01 to 0.1%, from 0.1% to 0.5%, from 0.5 to 1%, from 1 to 5%, from 5 to 10%, from 10 to 15% or more than 15% of the plurality of the molecules Sequenced circulating extracellular nucleic acid can have lengths greater than 4 knt. The plurality of circulating extracellular nucleic acid molecules may include at least 10, 50,100,150, or 200 circulating extracellular nucleic acid molecules. The plurality of circulating extracellular nucleic acid molecules may be from a plurality of different genomic regions. For example, a plurality of chromosome arms or chromosomes may be covered by circulating extracellular nucleic acid molecules. At least two of the plurality of circulating extracellular nucleic acid molecules may correspond to non-overlapping regions. The method of sequencing long fragments of circulating extracellular DNA can be used by any method described herein. Sequencing reads can be used to determine fetal aneuploidy, an aberration (e.g., copy number aberration), a genetic mutation or variation, or inheritance of a parental haplotype. The number of sequence reads may be representative of the number of circulating extracellular DNA fragments. ηζοΑηη / ζζηζ / Ε / γίΛΐ 2. Haplotypic inheritance FIG 21 shows a method 2100 of analyzing a biological sample obtained from a woman pregnant with a fetus. The woman may have a first haplotype and a second haplotype in a first chromosomal region. The biological sample may include a plurality of circulating extracellular DNA molecules from the fetus and the woman. The biological sample may be any biological sample described herein. At block 2105, readings corresponding to the plurality of circulating extracellular DNA molecules can be received. The reads may be sequence reads. In some embodiments, the method may include performing sequencing. At block 2110, the sizes of the plurality of circulating extracellular DNA molecules can be measured. Sizes can be measured by aligning one or more sequence reads corresponding to the ends of a DNA molecule to a reference genome. Sizes can be measured by sequencing the entire DNA molecule and then counting the number of nucleotides in the entire sequence. The genomic coordinates at the outermost nucleotides can be used to determine the length of the DNA molecule. At block 2115, a first set of circulating extracellular DNA molecules may be identified from the plurality of circulating extracellular DNA molecules having sizes greater than or equal to a cutoff value. The cutoff can be any cutoff value associated with long DNA. For example, the limit may include 150 bp, 180 bp, 200 bp, 250 bp, 300 bp, 350 bp, 400 bp, 450 bp, 500 bp, 550 bp, 600 bp, 650 bp, 700 bp, 750 bp, 800 bp, 850 bp, 900 bp, 950 bp, 1 kb, 1.5 kb, 2 kb, 2.5 kb, 3 kb, 4 kb, 5 kb, 6 kb, 7 kb, 8 kb, 9 kb, 10 kb, 15 kb, 20 kb, 30 kb, 40 kb, 50 kb, 60 kb, 70 kb, 80 kb, 90 kb, 100 kb, 200 kb, 300 kb, 400 kb, 500 kb, or 1 Mb. At block 2120, a sequence of the first haplotype and a sequence of the second haplotype can be determined from reads corresponding to the first set of circulating extracellular DNA molecules. Determining the sequence of the first haplotype and the sequence of the second haplotype may include aligning reads corresponding to the first set of circulating extracellular DNA molecules to a reference genome. In some embodiments, the determination of the sequence of the first haplotype and the sequence of the second haplotype may not include a reference genome. Determining the sequence may include aligning a first subset of the reads with a second subset of the reads to identify a different allele at a locus in the reads. The method may include determining that the first subset of the reads has a first allele at the locus. The method may also include determining that the second subset of the reads has a second allele at the locus. The method may further include determining that the first subset of the reads corresponds to the first haplotype. Additionally, the method may include determining that the second subset of the reads corresponds to the second haplotype. The alignment may be similar to the alignment described with FIG 16. At block 2125, a second set of circulating extracellular DNA molecules of the plurality of circulating extracellular DNA molecules may be aligned to the sequence of the first haplotype. The second set of circulating extracellular DNA molecules may have sizes smaller than the cutoff value. The second set of circulating extracellular DNA molecules may be short DNA molecules of the first haplotype. ηζοΑηη / ζζηζ / Ε / γίΛΐ At block 2130, a third set of circulating extracellular DNA molecules of the plurality of circulating extracellular DNA molecules may be aligned to the sequence of the second haplotype. The third set of circulating extracellular DNA molecules may have sizes smaller than the cutoff value. The third set of circulating extracellular DNA molecules may be short DNA molecules of the second haplotype. At block 2135, a first value of a parameter may be measured using the second set of circulating extracellular DNA molecules. The parameter may be a count of circulating extracellular DNA molecules, a size profile of circulating extracellular DNA molecules, or a methylation level of circulating extracellular DNA molecules. The values ​​can be raw values ​​or statistical values ​​(for example, mean, median, mode, percentile, minimum, maximum). In some embodiments, the values ​​may be normalized to a value of a parameter for a reference sample, another region, both haplotypes, or for other size ranges. At block 2140, a second value of the parameter may be measured using the third set of circulating extracellular DNA molecules. The parameter is the same parameter as for the second set of circulating extracellular DNA molecules. At block 2145, the first value can be compared to the second value. The comparison can use a separation value. A separation value can be calculated using the first value and the second value. The separation value can be compared with a limit value. The separation value may be any separation value described herein. The cutoff value can be determined from reference samples of pregnant women with euploid fetuses. In other embodiments, the cutoff value may be determined from reference samples of pregnant women with aneuploid fetuses. In some embodiments, the cutoff value may be determined assuming an aneuploid fetus. For example, data from reference samples of pregnant women with euploid fetuses can be adjusted to account for an increase or decrease in the copy number of a chromosomal region for an aneuploidy. The limit value can be determined by fitting the data. In 2150, the probability of the fetus inheriting the first haplotype can be determined based on the comparison of the first value with the second value. The probability can be determined based on the comparison of the separation value with the limit. When the parameter is the size profile of circulating extracellular DNA molecules, the method may include determining that the fetus has a greater probability of inheriting the first haplotype than the second haplotype when the first value is less than the second value, which indicates that the second set of circulating extracellular DNA molecules is characterized by a smaller size profile than the third set of circulating extracellular DNA molecules. When the parameter is the methylation level of circulating extracellular DNA molecules, the method may include determining that the fetus has a greater probability of inheriting the first haplotype than the second haplotype when the first value is less than the second value. In some embodiments, the methods may include identifying a number of repeats of a subsequence in a read of the reads corresponding to the first set of circulating extracellular DNA molecules. Determining the sequence of the first haplotype may include determining that the sequence includes the number of repeats of the subsequence. The first haplotype may include a recurrence-associated disease, which may be any of those described herein. The probability of the fetus inheriting the disease associated with the nzoAnn / zznz / E / YiAi repeat can be determined. The probability of the fetus inheriting the repeat-associated disease may be equal to or similar to the probability of the fetus inheriting the first haplotype. Identification of sequence repeats is described later in this description, including with FIG 16. TISSUE OF ORIGIN ANALYSIS USING METHYLATION Circulating long extracellular DNA molecules can have multiple methylation sites. As explained in this description, the methylation level of a long circulating extracellular DNA molecule in a pregnant woman can be used to determine a tissue of origin. Additionally, the methylation pattern present on a circulating extracellular long DNA molecule can be used to determine a tissue of origin. Cells from placental tissues possess unique methylomic patterns compared to white blood cells and cells from tissues such as, but not limited to, liver, lungs, esophagus, heart, pancreas, colon, small intestine , adipose tissues, adrenal glands, brain, etc. (Sun et al., Proc Nati Acad Sci USA. 2015; 112:E5503-12). The methylation profiles of circulating fetal DNA in the blood of a pregnant mother may resemble those in the placenta, providing possibilities to explore a means to develop non-invasive fetus-specific biomarkers that are not dependent on the sex or genotype of the fetus. However, bisulfite sequencing (e.g., using lllumina sequencing platforms) of DNA in maternal plasma from pregnant women may lack the ability to differentiate molecules of fetal origin from those of maternal origin due to a number of limitations: ( 1) DNA in plasma can be degraded during bisulfite treatment, and typically a long DNA molecule would be broken into shorter molecules; (2) DNA molecules larger than 500 bp may not be sequenced effectively with lllumina sequencing platforms for downstream analysis (Tan et al, Sci Rep. 2019;9:2856). For methylation-based analysis of tissues of origin, one can focus on a few differentially methylated regions (DMRs) and use the aggregate methylation signal from multiple DMR-associated molecules (Sun et al, Proc Nati Acad Sci USA. 2015 ;112:E5503-12), rather than single-molecule methylation patterns. Several studies attempted to use approaches based on methylation-sensitive restriction enzymes (Chan et al, Clin Chem. 2006;52:2211-8) or methylation-specific PCR (Lo et al, Am J Hum Genet. 1998;62:768 -75) to evaluate the contribution of the placenta to the plasma DNA pool. However, those studies were only suitable for analyzing one or a few markers and can be challenging to use to analyze molecules on a genome-wide scale. However, those reads were inferred from amplified signals (i.e., PCR-based amplification during DNA library preparation and bridging amplification during sequencing pool generation in a flow cell). Such amplification steps can potentially create a bias that prefers short DNA molecules, leading to the loss of information related to long DNA molecules. Furthermore, Lí et al. only analyzed those reads related to DMRs that were previously extracted (Li et al., Nuclei Acids Res. 2018;46:e89). Here, we describe new approaches to differentiate fetal and maternal DNA molecules in the plasma of pregnant women based on the methylation pattern of a single DNA molecule without bisulfite treatment or DNA amplification. In embodiments, one or more long DNA molecules in plasma would be used for analysis (for example, using bioinformatics and / or experimental assays for size selection). A long DNA molecule can be defined as a DNA molecule with a size of at least, but not limited to, 100 bp, 200 bp, 300 bp, 400 bp, 500 bp, 600 bp, 700 bp, 800 bp, 900 bp , 1kb, 2kb, 3kb, 4kb, 5kb, 10kb, 20kb, 30kb, 40kb, 50kb, 100kb, 200kb, etc. There is a paucity of data on the presence and methylation status of longer circulating extracellular DNA molecules in maternal plasma. For example, it is not known whether the methylation status of longer circulating extracellular DNA molecules would reflect that of cellular DNA from the tissue of origin, for example, given that such long fragments have more sites whose methylation status could change after fragmentation in the body; such a change could occur while the fragments are circulating in the plasma. For example, one study has shown that the methylation status of circulating DNA correlates with the size of DNA fragments (Lun et al. Clin Chem. 2013;59:1583-94). Therefore, the feasibility of inferring tissue origin from longer circulating extracellular DNA molecules is unknown. Therefore, the approaches adopted to identify tissue-associated methylation signatures and the methodologies adopted to determine and interpret the presence of such tissue-specific longer circulating extracellular DNA molecules are substantially different from those applied to the analysis of short circulating extracellular DNA. . According to embodiments of this disclosure, short and long DNA molecules could be identified and their biological characteristics determined, including, but not limited to, methylation patterns, fragment ends, sizes, and base compositions. A short DNA molecule could be defined as a DNA molecule with a size of less than, but not limited to, 50 bp, 60 bp, 70 bp, 80 bp, 90 bp, 100 bp, 200 bp, 300 bp, etc. A short DNA molecule can be a DNA molecule that is not in a range that is considered long. We describe a new approach to infer the tissues of origin of circulating DNA molecules in the plasma of pregnant women. This new approach makes use of methylation patterns in one or more long DNA molecules in plasma. The longer a DNA molecule is, the greater the number of CpG sites it is likely to contain. The presence of multiple CpG sites on a plasma DNA molecule would provide information on tissue origin, although the methylation status of any single CpG site may not be informative in determining tissues of origin. Such methylation patterns in a long DNA molecule may include the methylation status of each CpG site, orders of methylation status, and distances between any two CpG sites. The methylation status between two CpG sites may depend on the distance between two CpG sites. When CpG sites within a certain distance (e.g., CpG island) in a molecule exhibit a tissue-specific pattern, a statistical model can assign more weight to those signals during analysis of the tissue of origin. FIG 22 schematically illustrates this principle. FIG 22 shows methylation patterns for DNA molecules. Seven CpG sites for different tissues (placenta, liver, blood cells, colon) and six DNA fragments in plasma A-E are shown. Methylated CpG sites are shown in red and unmethylated CpG sites are shown in green. As an example, let us consider 7 CpG sites with various methylation states in the placenta, liver, blood cells, and colon tissues. Let us consider the scenario in which no CpG site shows a placenta-specific methylation status compared to other tissues. Therefore, the tissue of origin for those plasma DNA molecules A, B, C, D, and E with variable sizes could not be determined based solely on ηζοΑηη / ζζηζ / Ε / γίΛΐ methylation status at a single CpG site. . For plasma DNA molecules A and B, since the sizes of those two molecules are relatively short, they only contain 3 and 4 CpG sites, respectively. In embodiments, the methylation pattern in a DNA molecule containing more than one CpG site may be defined as a methylation haplotype. As shown in FIG 22, plasma DNA molecules A and B could be contributed by the placenta or liver based on their methylation haplotypes, since the placenta and liver shared the same methylation haplotype in the genomic positions corresponding to molecules A (positions 1,2 and 3) and B (positions 1,2, 3 and 4). However, when long DNA molecules can be obtained in plasma, such as molecules C, D, and E, it can be unambiguously determined that those molecules C, D, and E are derived from the placenta on the basis of methylation haplotype. The reference pattern for a tissue may be based on the methylation pattern of a reference tissue. In some embodiments, the methylation pattern may be based on multiple readings and / or samples. A methylation level for each CpG site (also called methylation index, MI, and described below) can be used to determine whether a site is methylated. / . Statistical models for methylation patterns In embodiments, the probability that a DNA molecule in plasma is derived from the placenta can be determined by comparing the methylation haplotype of a single DNA molecule to methylation patterns in various reference tissues. Long DNA molecules in plasma may be favored for such analysis. A long DNA molecule can be defined as a DNA molecule with a size of at least, but not limited to, 100 bp, 200 bp, 300 bp, 400 bp, 500 bp, 600 bp, 700 bp, 800 bp, 900 bp , 1kb, 2kb, 3kb, 4kb, 5kb, 10kb, 20kb, 30kb, 40kb, 50kb, 100kb, 200kb, etc. Reference tissues may include, but are not limited to, placenta, liver, lungs, esophagus, heart, pancreas, colon, small intestine, adipose tissues, adrenal glands, brain, neutrophils, lymphocytes, basophils, eosinophils, etc. In embodiments, the probability that a plasma DNA molecule is derived from the placenta can be determined by synergistic analysis of the methylation haplotype of a plasma DNA determined by single-molecule real-time sequencing and methylome data based in whole-genome bisulfite sequencing of reference tissues. As an example, placenta and buffy coat samples were sequenced at a mean genomic coverage of 94 and 75 times a haploid genome, respectively, using whole-genome bisulfite sequencing. The methylation level of each CpG site (also called methylation index, MI) was calculated based on the number of cytosines sequenced (i.e., methylated, indicated by C) and the number of thymines sequenced (i.e., unmethylated, indicated by 7) using the following formula: MI = —x 100%. C+T CpG sites were stratified into three categories based on MI values ​​inferred from placental DNA: 1. Category A CpG sites whose MI values ​​were >70. ηζοΑηη / ζζηζ / Ε / γίΛΐ 2. Category B CpG sites whose MI values ​​were between 30 and 70. 3. Category C CpG sites whose MI values ​​were <30. Similarly, MI values ​​at CpG sites deduced from buffy coat DNA were used to classify CpG sites into three categories: 1. Category A CpG sites whose MI values ​​were >70. 2. Category B CpG sites whose MI values ​​were between 30 and 70. 3. Category C CpG sites whose MI values ​​were <30. The categories used MI limits of 30 and 70. The limits may include other numbers, including 10, 20,40, 50, 60, or 90. In some embodiments, these categories can be used to determine a reference methylation pattern for a tissue. reference (for example, to use as described in FIG 22). Category A sites can be considered methylated. Category C sites can be considered unmethylated. Category B sites may be considered non-informative and not included in the reference standard. For a plasma DNA molecule harboring n CpG sites, the methylation status of each CpG site was determined using approaches described in our previous description (US Application No. 16 / 995,607). In some embodiments, methylation status can be determined by bisulfite sequencing or nanopore sequencing. To determine the probability that a DNA molecule in plasma was derived from the placenta or maternal background, the methylation patterns of that molecule were analyzed in conjugation with previous methylation information in placenta and maternal buffy coat DNA. . In embodiments, we made use of the principle that if a CpG site is determined to be methylated (M) on a DNA fragment in plasma coincided with a higher methylation index in the placenta, such an observation would indicate that this was more likely to occur. molecule is derived from the placenta. If a CpG site found to be methylated (M) on a DNA molecule in plasma coincided with a lower methylation index in the placenta, such an observation would indicate that this molecule was less likely to be derived from the placenta; whether a CpG site determined to be unmethylated (U) in a plasma DNA coincided with a lower methylation index in the placenta. Such an observation would indicate that this molecule was most likely derived from the placenta. If a CpG site determined to be unmethylated (U) in a plasma DNA coincided with a higher methylation index in the placenta, such an observation would indicate that this molecule was less likely to be derived from the placenta. We implemented the following scoring scheme. The initial score (S) reflecting the probability of fetal origin of a DNA fragment in plasma was set at 0. By comparing the methylation status of a DNA molecule in plasma with the previous methylation information of placental DNA, to. If a CpG site on the plasma DNA molecule is determined to be 'M' and its counterpart in the placenta belongs to Category A, a score of 1 would be added to S (i.e. increasing the scoring unit by 1) . b. If a CpG site on the plasma DNA molecule is determined to be 'U' and its counterpart in the placenta belongs to Category A, a score of 1 will be deducted from S (i.e., decreasing the scoring unit by 1) . c. If a CpG site on the plasma DNA molecule is determined to be 'M' and its counterpart in the placenta belongs to Category B, a score of 0.5 would be added to S. ηζοΑηη / ζζηζ / Ε / γίΛΐ d. If a CpG site on the plasma DNA molecule is determined to be 'U' and its counterpart in the placenta belongs to Category B, a score of 0.5 would be added to S. and. If a CpG site on the plasma DNA molecule is determined to be 'M' and its counterpart in the placenta belongs to Category C, a score of 1 from S would be deducted. F. If a CpG site on the plasma DNA molecule is determined to be 'U' and its counterpart in the placenta belongs to Category C, a score of 1 would be added to S. We call the above processes 'methylation status matching1. Once all CpG sites in a plasma DNA molecule were processed, the final aggregate score, S(placenta), was obtained for that plasma DNA molecule. In embodiments, the number of CpG sites was required to be at least 30 and the length of the plasma DNA molecule was required to be at least 3 kb. Other CpG site numbers and lengths may be used, including, but not limited to, any of those described herein. When comparing the methylation status of a DNA molecule in plasma with the methylation level of buffy coat DNA at the corresponding sites, a similar scoring scheme would be applied. After processing all CpG sites in a plasma DNA molecule, the final aggregate score, S(buffy coat), was obtained for that plasma DNA molecule. If S(placenta) > S(buffy coat), the DNA molecule in plasma was determined to be of fetal origin; otherwise, the plasma DNA molecule was determined to be of maternal origin. There were 17 and 405 fetus-specific and maternal-specific DNA molecules that were used to evaluate the performance of inferring the maternal-fetal origin of a plasma DNA molecule. Fetus-specific molecules were plasma DNA molecules carrying fetus-specific SNP alleles, while maternal-specific DNA molecules were those carrying maternal-specific SNP alleles. FIG 23 shows a receiver operating characteristic (ROC) curve for determination of maternal and fetal origins. The y-axis shows sensitivity and the x-axis shows specificity. The red line represents the performance of differentiation of molecules of fetal origin and of maternal origin using the method based on the comparison of methylation status present in this description. The blue line represents the performance of differentiation of fetal-derived and maternal-derived molecules using the single-molecule methylation level (i.e., the proportion of CpG sites determined to be methylated on a DNA molecule). FIG 23 shows that the area under the receiver operating characteristic curve (AUC) for the methylation status matching process (0.94) was significantly larger than that based on the single-molecule methylation level (0.86) (value of P < 0.0001; DeLong test). He suggested that analysis of methylation patterns of a long DNA molecule would be useful for determination of fetal / maternal origin. In embodiments, the magnitude of the difference (AS) between S(piacenta) and S(buffy coat) may be taken into account when determining whether a plasma DNA was of fetal or maternal origin. The absolute value of AS may be required to exceed a certain threshold, for example, but not limited to, 5,10, 20, 30, 40, 50, etc. As an illustration, when ηζοΑηη / ζζηζ / Ε / γίΛΐ we used 10 as the AS threshold, the positive predictive value (PPV) in detecting fetal DNA molecules was improved to 91.67% from 14.95%. In embodiments, the methylation status of a CpG site would be affected by the methylation status of its neighboring CpG sites. The closer the nucleotide distance between two CpG sites on a DNA molecule, the more likely it is that the two CpG sites share the same methylation status. This phenomenon has been called comethylation. Several tissue-specific CpG island methylations have been reported; Therefore, in some statistical models for tissue-of-origin analysis, more weights would be assigned to dense clusters of CpG sites (e.g., CpG islands) that share the same methylation status. For scenarios 'a' and T, if the current CpG site under interrogation was located within a genomic distance of no more than 100 bp relative to the previous CpG site and the results of the methylation status comparison process were identical for these two consecutive CpG sites, an additional 1 point would be added to the S score for the current CpG site. For scenarios 'b' and 'é, if the current CpG site under interrogation was located within a genomic distance of no more than 100 bp relative to the previous CpG site and the results of the methylation status comparison process were identical for these two consecutive CpG sites, an additional 1 point would be deducted from the S score for the current CpG site. However, if the current CpG site under interrogation was located within a genomic distance of no more than 100 bp relative to the previous CpG site, but the results of the methylation status comparison process for these two consecutive CpG sites did not were consistent, the aforementioned default scoring scheme would be used. On the other hand, if the current CpG site under interrogation were located within a genomic distance greater than 100 bp relative to the previous CpG site, the scoring scheme mentioned above with default parameters would be used. Points other than 1 and distances other than 100 bp may be used, including any of those described herein. In other embodiments, CpG sites were stratified into more than three categories based on MI values ​​inferred from placenta and buffy coat DNA. The above methylation information of reference tissues could be deduced from single-molecule real-time sequencing (i.e., nanopore sequencing and / or PacBio SMRT sequencing). The length of a DNA molecule in plasma could be required to be at least, but not limited to, 100 bp, 200 bp, 300 bp, 400 bp, 500 bp, 600 bp, 700 bp, 800 bp, 900 bp, 1 kb, 2kb, 3kb, 4kb, 5kb, 10kb, 20kb, 30kb, 40kb, 50kb, 100kb, 200kb, etc. The number of CpG sites could be required to be at least, but not limited to, 3.4, 5.6, 7, 8, 9,10,20,30,40, 50, 60, 70,80, 90,100 , etc. In embodiments, a probabilistic model can be used to characterize the methylation patterns of a DNA molecule in plasma. The methylation status of k CpG sites (k > 1) in a plasma DNA molecule was denoted as M = (mi, m2,mk), where m¡ was 0 (for the unmethylated state) or 1 (for the methylated state) at the CpG i site on a plasma DNA molecule. In embodiments, the likelihood that M is associated with a placenta-derived plasma DNA molecule could depend on baseline methylation patterns in placental tissues. The baseline methylation patterns in placental tissues for those corresponding CpG sites in 1, 2,..., Ic would follow beta distributions. The beta distribution is parameterized by two positive parameters a and β, denoted by Beta (α, β). Values ​​derived from the beta distribution would range between 0 and 1. Based on high-depth nzoAnn / zznz / E / YiAi bisulfite sequencing data for a tissue of interest, parameters a and β were determined by the number of cytosines sequenced (methylated ) and thymines (unmethylated) at each CpG site for that particular tissue, respectively. For the placenta, this beta distribution was called Beta(ap, βρ). The probability that a DNA molecule in plasma is derived from the placenta, P(M | Placenta), would be modeled by: i=p P(M | Placenta) = j”[p(m¿| Beta(«pp?)) i=i Where 7 denotes the r™™ CpG site; BetaCa?, β?) indicated the beta distribution related to methylation patterns at the CpG reshimsite in the placenta; P was the joint probability of an observed plasma DNA molecule with given methylation patterns at CpG k sites. The probability that a DNA molecule in plasma is derived from the buffy coat (i.e., white blood cells), P(M |), would be modeled by: i=p P(M | Buffy coat) = j”BetaCa-L β?)} Í = 1 Where 7 denotes the -th CpG site; Beta(af, β·7) indicated the beta distribution related to methylation patterns at the i-th CpG site in buffy coat DNA. P was the joint probability of a given plasma DNA molecule with given methylation patterns at k CpG sites. Beta(af, β?) and Beta(a^, β^) could be determined from the results of bisulfite sequencing of the whole genome of placenta and buffy coat DNA, respectively. For a plasma DNA molecule, if P(M | Placenta) > P(M | buffy coat) were observed, such a plasma DNA molecule would likely be derived from the placenta; otherwise it would probably be derived from the buffy coat. Using this model, we achieved an AUC of 0.79. J. Machine learning models In still other embodiments, a machine learning algorithm could be used to determine the fetal / maternal origin of a particular plasma DNA molecule. To test the feasibility of using the machine learning-based approach to classify fetal and maternal DNA molecules in pregnant women, we developed a graphical display of methylation patterns for a DNA molecule in plasma. FIG 24 shows a definition of pairwise methylation patterns. Nine CpG sites are shown on a plasma DNA molecule. Methylated CpG sites are shown in red and unmethylated CpG sites are shown in green. When two CpG sites in a pair shared the same methylation status (e.g., the 1erCpG and the 5oCpG), the pair would be coded as 1, as shown at the position indicated by arrow 'a'. When two CpG sites in a pair had a different methylation status (e.g., 1erCpG and 2d0CpG), the pair would be coded as 0, as shown at a position indicated by arrow 'b'. The same coding rules were applied to all pairs of any 2 CpG sites in a DNA molecule. ηζοΑηη / ζζηζ / Ε / γίΛΐ We use a plasma DNA molecule containing 9 CpG sites as an example. The methylation pattern for this DNA molecule in plasma was determined using approaches described in our previous description (US Application No. 16 / 995,607), i.e., U-M-M-M-U-U-U-M-M (U and M represented unmethylated CpG and methylated CpG, respectively). Pairwise comparison of methylation status between two CpG sites can be useful for machine learning or deep learning-based analysis. The same rules were applied to a total of 36 pairs in this example. If there were a total of n CpG sites in a plasma DNA molecule, there would be n*(n-1) / 2 comparison pairs. A different number of CpG sites can be used, including 5, 6, 7, 8,10, 11,12, 13, etc. If a molecule includes a greater number of sites than used in the machine learning model, a sliding window can be displayed to divide the sites into the appropriate number of sites. We obtained one or more molecules from placental and buffy coat DNA samples, respectively. Methylation patterns for those DNA molecules were determined using Pacific Bioscience (PacBio) single-molecule real-time (SMRT) sequencing according to approaches described in our previous description (U.S. Application No. 16 / 995,607 ). These methylation patterns were translated into pairwise methylation patterns. Pairwise methylation patterns associated with placental DNA and those associated with buffy coat DNA were used to train a convolutional neural network (CNN) to differentiate molecules potentially of fetal and maternal origin. Each target result (i.e., analogous to a dependent variable value) for a placental DNA fragment was assigned as T, while each target result for a buffy coat DNA fragment was assigned as '0'. The pairwise methylation patterns were used for training to determine the parameters (often called weights) for the CNN model. The optimal CNN parameters to differentiate the maternal-fetal origin of a DNA fragment were obtained when the overall prediction error between the output scores calculated by a sigmoid function and the desired target outputs (binary values: 0 or 1) reached a minimum iteratively adjusting the model parameters. The overall prediction error was measured using a sigmoidal cross-entropy loss function in deep learning algorithms (https: / / keras.io / ). Model parameters learned from the training data sets were used to analyze a DNA molecule (such as a DNA molecule in plasma) to generate a probabilistic score that would indicate the probability that the DNA molecule was derived from the placenta or the buffy coat. If the probabilistic score of a plasma DNA fragment exceeded a certain threshold, that plasma DNA molecule was considered to be of fetal origin. Otherwise, it would be considered of maternal origin. The threshold would include, but is not limited to, 0.1,0.2,0.3,0.4,0.5, 0.6,0.7,0.8,0.9, 0.95, 0.99, etc. In one example, using this CNN model, we achieved an AUC of 0.63 for determining whether a plasma DNA molecule was of fetal or maternal origin, indicating that it is possible to infer the tissues of origin of maternal plasma DNA molecules using deep learning algorithms. By obtaining more single-molecule real-time sequencing results, the performance of the deep learning algorithm would be further improved. In some other embodiments, statistical models could include, but are not limited to, linear regression, logistic regression, deep recurrent neural network (e.g., long short-term memory, LSTM), Bayes classifier, hidden Markov model ( HMM), linear model discriminant analysis (LDA), k-means clustering, density-based spatial clustering of applications with noise (DBSCAN), random forest algorithm, and nzoAnn / zznz / E / YiAi support vector machine (SVM). ), etc. Different statistical distributions would be involved, including, but not limited to, the binomial distribution, Bernoulli distribution, gamma distribution, normal distribution, Poisson distribution, etc. K. Placenta-specific methylation haplotypes The methylation status of each CpG site on a single DNA molecule can be determined using the approaches described in our previous description (US Application No. 16 / 995,607) or any technique described herein. In addition to the single-molecule double-stranded DNA methylation level, the single-molecule methylation pattern of each DNA molecule could be determined, which may be the sequence of the methylation status of adjacent CpG sites along a single DNA molecule. Different DNA methylation signatures can be found in different tissues and cell types. In embodiments, the tissue of origin of DNA molecules in individual plasmas could be deduced based on their single-molecule methylation patterns. Genomic DNA from ten buffy coat samples and six placental tissue samples was sequenced by SMRT sequencing (PacBio). By pooling high-quality circular consensus sequencing (CCS) reads mapped from each sample type, we were able to achieve coverages of 58.7- and 28.7-fold for buffy coat DNA and placental DNA, respectively. Using a sliding window approach, the genome was divided into approximately 28.2 million overlapping windows of 5 CpG sites. In other embodiments, different window sizes could be used, such as, but not limited to, 2,3,4,5,6, 7 and 8 CpG sites. A non-overlapping window approach could also be used. Each window was considered a potential marker region. For each potential marker region, we identified the predominant single-molecule methylation pattern among all sequenced placental DNA molecules covering the 5 CpG sites within that marker region. Comparisons would be made between the CpG sites of a plasma DNA molecule and the corresponding CpG sites of individual DNA molecules from reference tissues. We then calculated a mismatch score for each buffy coat DNA molecule covering all CpG sites within the same marker region by comparing its single-molecule methylation pattern to the predominant single-molecule methylation pattern in the placenta. Number of mismatched CpG sites Misfit score =---—----——-—-—, ---, Total number of CpG sites where the number of mismatched CpG sites refers to the number of CpG sites that show a different methylation status in the buffy coat DNA molecule compared to the predominant methylation pattern of a single molecule in the placenta. A higher mismatch score indicates that the methylation pattern of the buffy coat DNA molecule is more different from the predominant single-molecule methylation pattern in the placenta. Of the 28.2 million potential marker regions, we selected those that showed a substantial difference in the pattern of single-molecule ηζοΑηη / ζζηζ / Ε / γίΛΐ methylation between the sets of placental and buffy coat DNA molecules using the following criteria : a) more than 50% of placental DNA molecules had the predominant single-molecule methylation pattern; and b) more than 80% of buffy coat DNA molecules had a mismatch score greater than 0.3. Based on these criteria, we selected 281,566 marker regions for further analysis. FIG 25 is a table of the distribution of selected marker regions between different chromosomes. The first column shows the number of chromosomes. The second column shows the number of marker regions on the chromosome. We hereby illustrate our concept of tissue-of-origin classification for individual plasma DNA molecules based on single-molecule methylation patterns using plasma DNA molecules sequenced with SMRT sequencing that covered either a fetus-specific allele or a specific allele. from the mother as described earlier in this description. Any DNA molecule in plasma that covers a selected marker region with a methylation pattern identical to the predominant single-molecule methylation pattern in the placenta would be classified as a placenta-specific (i.e., fetus-specific) DNA molecule. In contrast, if the single-molecule methylation pattern of a DNA molecule in plasma is not identical to the predominant single-molecule methylation pattern in the placenta, we would classify this molecule as non-placenta-specific. Correct classification in this analysis was defined such that a fetus-specific DNA molecule was identified as fetal-derived (i.e., placental-specific) and a maternal DNA molecule was identified as non-fetal-derived (i.e. non-placental-specific) according to whether placenta-specific methylation haplotypes were present on that molecule. Previous methylation-based methods for tissue-of-origin analysis typically involved deconvoluting the percentage or proportional contributions of a range of tissue contributors from circulating extracellular DNA within the biological sample. An advantage of the present method over previous methods is that evidence for the contribution of circulating extracellular DNA from a tissue to the biological sample, for example, placenta-derived DNA in maternal plasma, could be determined without taking into account the presence or absence of contributions from the other tissues. Furthermore, the placental origin of any circulating extracellular DNA molecule could be determined with the present method without taking into account the fractional contribution of circulating extracellular DNA molecules from that tissue. Among the 28 DNA molecules covering a fetus-specific allele, 17 (61%) were classified as placenta-specific and 11 (39%) were classified as non-placenta-specific. On the other hand, among the 467 DNA molecules covering a maternal-specific allele, 433 (93%) were classified as non-placenta-specific and 34 (7%) were classified as placenta-specific. In embodiments, different percentages of buffy coat DNA molecules having a mismatch score greater than 0.3 could be used as a threshold, including, but not limited to, greater than 60%, 70%, 75%, 80%. 85% and 90%. %, etc. By adjusting the criteria used in marker region selection, the accuracy of the overall classification for placental or nonplacental origins of plasma DNA in pregnant ηζοΑηη / ζζηζ / Ε / γίΛΐ individuals could be improved. This is particularly important in the context of non-invasive prenatal testing when trying to determine whether the fetus has a disease-causing mutation or copy number aberration. FIG 26 is a table of the classification of plasma DNA molecules based on their single-molecule methylation patterns using different percentages of buffy coat DNA molecules that have a mismatch score greater than 0.3 as a selection criterion for marker regions. The first column shows the percentage of buffy coat DNA molecules that have a mismatch score greater than 0.3%. The second column divides the DNA molecules into those that cover a specific allele for the fetus and those that cover a specific allele for the mother. The third and fourth columns show the classification of DNA molecules as placenta-specific or placenta-non-specific based on a single-molecule methylation pattern. The fifth column shows the percentage of DNA molecules that were classified the same as the specific allele in the second column. FIG 27 shows a process flow for using a placenta-specific methylation haplotype to determine fetal inheritance in a non-invasive manner. As shown in FIG 27, circulating extracellular DNA was extracted from the plasma of a pregnant woman for single-molecule real-time sequencing. Long DNA molecules in plasma were identified according to the embodiments of this disclosure. The methylation status at each CpG site for each long DNA molecule in plasma was determined according to the embodiments of this disclosure. The methylation haplotype of each long DNA molecule in plasma was determined according to the embodiments of this disclosure. If a long DNA molecule in plasma was identified as carrying a placenta-specific methylation haplotype, the genetic and epigenetic information related to that molecule would be considered inherited by the fetus. In embodiments, if one or more long DNA molecules in plasma containing a disease-causing mutation, which is the same as the disease-causing mutation carried by a pregnant woman, is determined to be of fetal origin based on Methylation haplotype information according to the embodiments in this disclosure would suggest that the fetus had inherited the mutation from the mother. The modalities could apply to genetic diseases including, but not limited to, beta-thalassemia, sickle cell anemia, alpha-thalassemia, cystic fibrosis, hemophilia A, hemophilia B, congenital adrenal hyperplasia, Duchenne muscular dystrophy, Becker muscular dystrophy , achondroplasia, thanatophoric dysplasia, von Willebrand disease, Noonan syndrome, hereditary hearing loss and deafness, various inborn errors of metabolism (for example, citrullinemia type I, propionic acidemia, glycogen storage disease type la (von Glerke disease) , glycogen storage disease type Ib / c (von Gierke disease), glycogen storage disease type II (Pompe disease), mucopolysaccharyotosis (MPS) type I (Hurler / HurlerScheie / Scheie), MPS type II Hunter), MPS type IIIA (Sanfilippo A syndrome), MPS type IIIB (Sanfilippo B syndrome), MPS type IIIC (Sanfilippo C syndrome), MPS type IIID (Sanfilippo D syndrome), MPS type IVA (Sanfilippo syndrome Morquio A), MPS type IVB (Morquio B syndrome), MPS type VI (Maroteaux-Lamy syndrome) MPS type Vil (Sly syndrome), mucolipidosis II (I cell disease), metachromatic leukodystrophy, GM1 gangliosidosis, OTC (X-linked ornithine transcarbamylase deficiency), adrenoleukodystrophy (X-linked ALD), Krabbe disease (globose cell leukodystrophy), etc. ηζοΑηη / ζζηζ / Ε / γίΛΐ In other embodiments, a genetic disease in the fetus could be associated with de novo DNA methylation in the fetal genome that was absent in the parental genomes. An example would be hypermethylation of the FMRP translational regulator 1 (FMR1) gene in a fetus with fragile X syndrome. Fragile X syndrome is caused by a CGG trinucleotide repeat expansion in the 5' untranslated region of the FMR1 gene. A normal allele would contain approximately 5 to 44 copies of the CGG repeat. A premutation allele would contain 55 to 200 copies of the CGG repeat. A full mutation allele would contain more than 200 copies of the CGG repeat. FIG 28 illustrates the principle of non-invasive prenatal detection of fragile X syndrome in a male fetus of an unaffected pregnant woman carrying a normal or premutation allele. In FIG 28, 'n' represents the number of copies of CGG in a maternal genome; 'm' represents the number of CGG copies in a fetal genome. The genome of the unaffected pregnant woman would harbor FMR1 genes that have CGG repeats of no more than 200 copies (i.e., n < 200) and are not methylated. In contrast, the genome of the male fetus affected by fragile X syndrome would harbor an FMR1 gene that has more than 200 copies of the CGG repeats (m > 200) and is methylated. By performing single-molecule sequencing of DNA from maternal plasma, a number of long DNA molecules from a genomic region of interest (e.g., the FMR1 gene) could be identified whose number of repeats and methylation status could be determined simultaneously. . If one were to identify one or more DNA molecules covering the FMR1 gene, containing more than 200 copies of the CGG repeats and being methylated, in the plasma of an unaffected woman, it would indicate that the fetus would likely have fragile X syndrome. In yet another embodiment, the fetal origin of such plasma DNA molecules could further be determined using placenta-specific methylation haplotypes according to the embodiments of this disclosure. If one or more molecules containing one or more regions within a molecule carrying placenta-specific methylation haplotypes are identified, and such molecules cover the FMR1 gene, contain more than 200 copies of the CGG repeats, and are methylated, one could conclude more confidently that the fetus has fragile X syndrome. In contrast, if one were to identify one or more molecules that harbored placenta-specific methylation haplotypes, and such molecules covered the FMR1 gene, contained fewer than 200 copies of the CGG repeat, and were not methylated, it would indicate that the fetus would likely be unaffected. . With fragile Therefore, for fragile Detection of genetic disorders can be done with or without knowing the mother's previous status. Women with the previous mutation may not have any symptoms, but some may have mild symptoms and these are often only known in retrospect. If we do not know the maternal mutational status, one approach is to detect a long allele in the plasma of a woman who does not appear to have the disease or to analyze the maternal buffy coat and determine that it does not show such a long allele. As another approach, we could combine the repeat length with the methylation state of the cfDNA molecule. If the methylation status suggests a fetal pattern (methylation haplotype) and shows a long allele, the fetus is likely affected. This approach is applicable to many trinucleotide disorders, for example, Huntington's disease. ηζοΑηη / ζζηζ / Ε / γίΛΐ L. Non-invasive construction of the fetal genome with long DNA molecules in plasma Methylation patterns can be used to determine the inheritance of haplotypes. Determining haplotype inheritance using a qualitative approach with methylation patterns may be more efficient than a quantitative method that characterizes quantities of certain fragments. Methylation patterns can be used to determine maternal and paternal inheritance of haplotypes. 1. Maternal inheritance of the fetus Lo et al. demonstrated the feasibility of constructing a genetic map of the entire genome and determining the mutational status of the fetus from the DNA sequences of the maternal plasma, with the use of information from parental haplotypes (Lo et al. Sci Transí Med. 2010 ;2:61 ra91). This technology has been called relative haplotype dosage (RHDO) analysis and is one approach to resolving maternal inheritance of the fetus. The principle was based on the fact that the maternal haplotype inherited by the fetus would be relatively overrepresented in the plasma DNA of a pregnant woman, compared to the other maternal haplotype that is not transmitted to the fetus. Therefore, RHDO is a quantitative analytical method. The embodiments present in this disclosure make use of methylation patterns in a long plasma DNA molecule to determine the tissues of origin of that plasma DNA molecule. In one embodiment, the description herein would allow qualitative analysis of the maternal inheritance of the fetus. FIG 29 shows an example of determining the maternal inheritance of a fetus. A P genomic position was heterozygous in the maternal genome (A / G). A filled circle indicates a methylated site and an open circle indicates an unmethylated site. The methylation pattern in the placenta was “-M-U-M-M-”, where M represents a methylated cytosine and U represents an unmethylated cytosine at a CpG site. In one embodiment, the methylation pattern in the placenta and relevant reference tissues can be obtained from data previously generated from sequencing (e.g., single-molecule real-time sequencing and / or bisulfite sequencing). In plasma DNA, a non-paternal plasma DNA (indicated by Z) carrying an A allele at that particular genomic locus was found to show the methylation pattern (“-M-U-M-M-”) consistent with the methylation pattern in the placenta as opposed to the methylation patterns of other tissues. No molecule was found carrying a G allele showing the methylation pattern consistent with methylation patterns in the placenta. Therefore, based on the A allele and the presence of the “-M-U-M-M-” methylation pattern, it can be determined that the fetus inherits the maternal A allele. FIG 30 shows the qualitative analysis of maternal inheritance of the fetus using genetic and epigenetic information from DNA molecules in plasma. As shown in the upper branch of FIG 30, DNA was extracted from plasma, followed by size selection for long DNA according to embodiments of this disclosure. Plasma DNA molecules selected by size were subjected to single-molecule real-time sequencing (e.g., using a system manufactured by Pacific Biosciences). Genetic and epigenetic information was determined according to the methods of this description. For illustrative purposes, a molecule (X) was aligned to human chromosome 1, containing one allele of G at chromosomal position a (chr1:a) and one allele of A at chromosomal position e (chr1:e). Molecule X has an allele of C at chromosome position d. ηζοΑηη / ζζηζ / Ε / γίΛΐ The CpG methylation state of this molecule X was determined to be where M represented a methylated cytosine and U represented an unmethylated cytosine at a CpG site. A filled circle indicates a methylated site and an open circle indicates an unmethylated site. As a result of the analysis of a reference sample, it is known that placental DNA has a methylation pattern of in the region between positions a and e. Based on the methylation pattern of molecule As shown in the lower branch of FIG 30, DNA from maternal white blood cells was subjected to single-molecule real-time sequencing. Epigenetic and genetic information from maternal white blood cells was obtained according to embodiments of this disclosure. The genetic alleles were divided into two haplotypes, namely maternal haplotype I (Hap I) and maternal haplotype II (Hap II), using methods including, but not limited to, WhatsHap (Patterson et al. J Comput Biol. 2015; 22:498-509), HapCUT (Bansal et al. Bioinformatics. 2008;24:¡153-9), HapCHAT (Beretta et al. BMC bioinformatics. 2018; 19:252), etc. Here we obtained two haplotypes, namely, “-A-C-G-T-” (Hap I) and “G-T-A-C-” (Hap II) in the maternal genomes. Hap I was associated with the wild-type variant(s), while Hap II was associated with the disease-associated variant(s). Disease-associated variant(s) could include, but are not limited to, single nucleotide variants, insertions, deletions, translocations, inversions, repeat expansions and / or other genetic structural variations. For genomic position e, the maternal genotype was determined to be AA and the paternal genotype was determined to be GG. Due to the methylation pattern, plasma DNA molecule X was determined to be of placental origin. Due to the presence of the mother-specific A allele, but the absence of the father-specific G allele, it was inferred that the X molecule was inherited from one of the maternal haplotypes. To further determine which maternal haplotype was transmitted to the fetus, we compared allelic information at genomic positions other than the chr1:e position of this placenta-derived molecule As an example, molecule X has the G allele at position a and the C allele at position d. The presence of any of these alleles on molecule X indicates that molecule X should be assigned to the maternal Hap II, which includes the same alleles. Therefore, it could be concluded that the maternal haplotype II linked to the variant(s) associated with the disease was transmitted to the fetus. The unborn fetus was determined to be at risk of being affected by the disease. Qualitative analysis based on methylation pattern for maternal inheritance of the fetus may require fewer plasma DNA molecules to reach the conclusion of which maternal haplotype was inherited by the fetus, compared to RHDO, which was an analysis-based approach. quantitative. We performed computer simulation analyzes to evaluate the genome-wide detection rate of fetal maternal inheritance with different amounts of plasma DNA molecules used for analysis. For RHDO simulation analysis, N plasma DNA molecules were collectively aligned to M heterozygous SNPs in a block of haplotypes from the maternal genome. The fetal DNA fraction was f. The paternal genotypes for those corresponding SNPs were homozygous and identical to the maternal Hap I that was transmitted to the fetus. Among N plasma DNA molecules, the mean plasma DNA molecules aligned with the maternal Hap I was Νχ(0.5 + / 72), ηζοΑηη / ζζηζ / Ε / γίΛΐ while the mean plasma DNA molecules aligned with the maternal Hap II would be Nx(0.5 - fl2). We assumed that plasma DNA molecules sampled from the haplotypes followed binomial distributions. The number of DNA molecules in plasma was assigned to Hap I (i.e., X), following the following distribution: X~Bin(N, 0.5+ / 72)(1), where “Bin” denotes the binomial distribution. The number of DNA molecules in plasma was assigned to Hap II (i.e., Y), following the following distribution: Y~ Bin(N, 0.5 — f / 2) (2). Therefore, plasma DNA molecules assigned to maternal Hap I would be relatively overrepresented in maternal plasma, compared to maternal Hap II. To determine whether the overrepresentation was statistically significant, we compared the difference in plasma DNA counts between two maternal haplotypes with the null hypothesis in which two haplotypes (denoted by X' and Y') were equally represented in plasma. X’ ~ Bin(N, 0.5) (3), Y’ ~ Bin(N, 0.5) (4). Furthermore, we define the relative dose difference between two haplotypes as follows: D=(X-Y) / N (5), D’=(X’-Y’) / N (6). In one example, a D statistic, reflecting the relative dosage of haplotypes, was compared to the mean of D' (M), normalized by the standard deviation of D' (SD) as follows (i.e., z score ): z score = (D - M) / SD (7). A z score of > 3 indicated that Hap I was transmitted to the fetus. For the RHDO analysis, based on formulas (1) to (7), we simulated 30,000 haplotype blocks in a complete genome in which Hap I was transmitted to the fetus. The average length of the haplotype blocks was 100 kb. Each haplotype block contained an average of 100 SNPs, among which 10 SNPs would be informative in contributing to haplotype imbalance. In one example, the fetal DNA fraction was 10% and the median fragment sizes were 150 bp. We calculated the percentage of haplotype blocks with a z score of >3, referred to herein as detection rate, by varying the number of plasma DNA molecules used for RHDO analysis ranging from 1 million to 300 million. The number of plasma DNA molecules herein was adjusted by the probability that plasma DNA covered an informative SNP site according to the Poisson distribution. For computer simulation related to qualitative analysis based on methylation patterns for maternal inheritance of the fetus, we made the following assumptions for illustrative purposes: 1) There were N plasma DNA molecules covering a block of haplotypes in the maternal genome used for analysis. 2) The probability that a plasma DNA fragment was used for analysis of the tissue of origin with at least 3 kb in length was denoted by a. 3) ηζοΑηη / ζζηζ / Ε / γίΛΐ The probability that a DNA molecule in plasma carries more than 10 CpG sites was denoted by b. 4) The fetal DNA fraction of those fragments >3 kb was called f. Accurate deduction of tissues of origin could be achieved for those plasma DNA molecules larger than 3 kb with at least 10 CpG sites as illustrated in one embodiment of this disclosure. The number of plasma DNA molecules meeting the above criteria (Z) was assumed to follow a Poisson distribution, with a mean value of λ (i.e., N x a x b χ ή. Z ~ Poisson (λ) (8). In one example, based on formula (8), we simulated 30,000 haplotype blocks in which Hap I was transmitted to the fetus. The average length of each haplotype block was 100 kb. Each haplotype block contained an average of 100 SNPs among which 20 heterozygous SNPs would split into two maternal haplotypes. The fetal DNA fraction was 1%. There were 40% of plasma DNA molecules with sizes >3 kb after size selection. There were 87.1% of plasma DNA molecules with sizes >3 kb harboring at least 10 CpG sites. The percentage of haplotype blocks with a Z value > 1 indicated the detection rate. We repeated multiple computer simulation runs varying the number of plasma DNA molecules (N) used for analysis of the tissue of origin by methylation patterns, ranging from 1 million to 300 million. The number of plasma DNA molecules herein was further adjusted by the probability that plasma DNA covered a heterozygous SNP according to the Poisson distribution. FIG 31 shows the detection rate of qualitative analysis for maternal inheritance of the fetus genomically using genetic and epigenetic information from plasma DNA molecules compared to relative haplotype dosage (RHDO) analysis. The number of molecules used for analysis is shown on the x-axis. The detection rate of maternal inheritance of the fetus as a percentage is shown on the y-axis. Detection rates of maternal inheritance from the fetus were higher using the methylation pattern-based approach, compared to RHDO. For example, using 100 million fragments, the detection rate based on methylation patterns was 100%, while the detection rate based on RHDO was only 55%. These results suggested that the inference of maternal inheritance from the fetus using the method based on methylation patterns would be greater than that based on RHDO. 2. Paternal inheritance of the fetus The ability to obtain long plasma DNA molecules for analysis may be useful in improving the rate of detection of specific paternal variants in a pregnant woman's plasma DNA, as the use of long DNA molecules would increase overall genomic coverage. compared to using an equal number of short DNA molecules. Additionally, we performed a computer simulation based on the following assumptions: 1) The fetal DNA fraction was f, depending on the plasma DNA length L. It was rewritten as 4, where the subscript L indicated that plasma DNA molecules with a length of L bp were used for analysis. 2) The number of specific paternal variants that had to be identified in maternal plasma DNA was V. 3) nzQRnn / zznz / e / YiN The number of plasma DNA molecules used for analysis was N. 4) The number of DNA molecules in plasma that originated from a particular locus or genomic region followed a Poisson distribution. In one example, the fetal DNA fractions of those plasma DNA molecules with a size of 150 bp, 1 kb and 3 kb were 10% (fi¡obp= 0.1), 2% (7™ = 0.02) and 1% (f^b= 0.01), respectively. The number of specific paternal variants was 250,000 (V = 250,000) in one genome. The number of plasma DNA molecules used for analysis (N) ranged from 50 million to 500 million. FIG 32 shows the relationship between the detection rate of specific paternal variants in a complete genome and the number of sequenced plasma DNA molecules with different sizes used for analysis. The number of sequenced molecules used for analysis in millions is shown on the x-axis. The percentage of paternal-specific variants detected is shown on the y-axis. The different curves show the different sized DNA fragments used for analysis, with 3 kb at the top, 1 kb in the middle, and 150 bp at the bottom. The longer the plasma DNA molecules used for analysis, the higher the detection rate of specific paternal variants. For example, using 400 million DNA molecules in plasma, detection rates were 86%, 93%, and 98% when focusing on molecules with sizes of 150 bp, 1 kb, and 3 kb, respectively. In other embodiments, other distributions could be used, including, but not limited to, the Bernoulli distribution, the beta normal distribution, the normal distribution, the Conway-Maxwell-Poisson distribution, the geometric distribution, etc. In some embodiments, Gibbs sampling and Bayes' theorem would be used for the analysis of maternal and paternal inheritance. 3. Fragile X inheritance analysis In embodiments, methylation pattern-based determination of maternal inheritance of the fetus may facilitate non-invasive detection of fragile X syndrome using real-time single-molecule sequencing of maternal plasma DNA. Fragile X syndrome is a genetic disorder, usually caused by a CGG trinucleotide repeat expansion within the FMR1 (fragile repetition are described elsewhere in this application. The methods for detecting fragile X syndrome in a fetus can also be applied to any other repeat expansion described herein. A woman with a premutation, defined as having 55 to 200 copies of the CGG repeats in the FMR1 gene, is at risk of having a child with fragile X syndrome. The probability of becoming pregnant with a fetus with fragile X syndrome depends on the number of CGG repeats present in the FMR1 gene. The greater the number of repeats in the mother, the greater the risk of an expansion from a premutation to a full mutation when transmitted to the fetus. A maternal plasma sample was collected at a gestational age of 12 weeks from a woman, who was previously confirmed to carry a fragile fragile (the proband). Maternal plasma was then subjected to single-molecule real-time sequencing. In one example, using single-molecule real-time sequencing, we obtained 3.3 million circular consensus sequences (CCS) aligned to a human reference genome, with a median underread depth of 75 folds per CCS (interquartile range: 14 - 237 folds). Genetic and epigenetic information for each sequenced plasma DNA can be determined according to embodiments of this disclosure. To obtain the two maternal haplotypes of the maternal leukocyte count and the buccal swab of the proband. The two maternal haplotypes, namely Hap I and Hap II, can be inferred based on the genotypic information of the maternal and proband genomes. FIG 33 shows a workflow for non-invasive detection of fragile X syndrome. At heterozygous SNP sites in maternal buffy coat DNA, alleles identical to the proband's genotypes were used to define the haplotype linked to the premutation allele (i.e., Hap I) that was a potential precursor to a full mutation in the later generations. On the other hand, alleles different from the proband's genotypes were used to define the haplotype linked to the corresponding wild allele (Hap II). Maternal plasma DNA from the proband's mother pregnant with a fetus was subjected to single-molecule real-time sequencing. Sequencing reads were assigned to maternal Hap I and Hap II, depending on whether the genetic information obtained was identical to the Hap I or Hap II alleles at the genomic loci under investigation. The methylation patterns of DNA molecules in plasma were used to determine the tissues of origin (i.e., DNA molecules identified as being of placental origin based on methylation pattern analysis would be determined to originate from the fetus) from those Plasma DNA molecules containing a certain number of CpG sites, according to embodiments of this disclosure. In Scenario A, if fetal (i.e., placental) DNA molecules were detectable from plasma DNA molecules assigned to maternal Hap I, but not detectable in plasma DNA molecules assigned to maternal Hap II, It would then be determined that Hap I would be transmitted to the unborn fetus. The fetus would be determined to be at high risk for being affected by fragile X syndrome. The placental origin of DNA molecules in plasma would depend on the methylation state of the molecule, as discussed below. In Scenario B, if fetal DNA molecules were detectable from plasma DNA molecules assigned to maternal Hap II, but not detectable in plasma DNA molecules assigned to maternal Hap II, then Hap would be determined to be II would be transmitted to the unborn fetus. It would be determined that the fetus is not affected by fragile X syndrome. In embodiments, the definitions of detectable and undetectable for fetal DNA molecules may depend on limits on the percentage of plasma DNA molecules identified as being of fetal (i.e., placental) origin. Detection limits may include, but are not limited to, more than 1%, 2%, 3%, 4%, 5%, 10%, 15%, 20%, 30%, 40%, 50%, etc. Undetectable limits may include, but are not limited to, 1%, 2%, 3%, 4%, 5%, 10%, 15%, 20%, 30%, 40%, 50%, etc. . In some embodiments, it may be necessary for the difference in the percentage of plasma DNA molecules determined to be of fetal origin between Hap I and Hap II to be greater than, but not limited to, 1%, 2%, 3%. , 4%, 5%, 10%, 15%, 20%, 30%, 40%, 50%, etc. In some other embodiments, haplotype information could be obtained from long read sequencing technologies (e.g., PacBio or nanopore sequencing) (Edge et al., Nat Commun. 2019;10:4660), synthetic long reads (e.g., using the 10X Genomics platform) (Huí et al. Clin Chem. 2017;63:513-14), phase-based amplification (TLA) of targeted ηζοΑηη / ζζηζ / Ε / γίΛΐ locus amplification (Vermeulen et al. Am J Hum Genet . 2017;101:326-39), and statistical phases (e.g. Shape-IT) (Delaneau et al. Nat Method. 2011;9:179-81). In embodiments, the maternal and fetal origins of those plasma DNA molecules that are at least 200 bp and contained at least 5 CpG sites (or any other limit for long DNA molecules) can be determined, according to the comparison approach. of the methylation status described in this application. We identified a DNA molecule in plasma, located at the genomic position chrX:143,782,245 - 143,782,786 (3.2 Mb from the FMR1 gene), with one allele (position: chrX: 143782434; SNP accession number: rs6626483; allele genotype: C) identical to the corresponding allele in maternal Hap II but different from that in maternal Hap I. FIG 34 shows a plasma DNA methylation pattern compared to buffy coat and placental DNA methylation profiles. The plasma DNA molecule contained 5 CpG sites. The methylation pattern was determined to be “M-U-U-U-U.” This methylation pattern obtained from single-molecule real-time sequencing was compared to the reference methylation profiles of placental tissues and buffy coat DNA samples obtained from bisulfite sequencing, according to the methylation status matching approach described in this description. The score for this molecule from the placenta [i.e., S(placenta)] was 2, which was higher than that from the buffy coat [i.e., S(buffy coat)] by -3. Therefore, it was determined that said DNA molecule in plasma (chrX:143,782,245- 143,782,786) was of fetal origin. However, we did not observe that any plasma DNA molecules carrying the maternal Hap I alleles were of fetal origin. Therefore, we conclude that the fetus inherited maternal Hap II and was not affected by fragile X syndrome. We anticipate that the performance of the approach described here might not be significantly affected by X chromosome inactivation due to the following factors: 1) X inactivation is not complete in humans. Up to 1 / 3 of the genes on the X chromosome showed variable escape from X inactivation (Cotton et al. Hum Mol Genet. 2015;25:1528-1539). CpG sites outside the CpG islands (i.e., most CpG sites) were methylated to a similar degree in both sexes, suggesting that the methylation status of most CpG sites on the chromosome X may not be affected by inactivation of 2) We used the methylation profile of placental tissues of the same sex with respect to the unborn fetus. This strategy would be useful to detect maternal inheritance of the fetus using plasma DNA methylation patterns for a woman pregnant with a male fetus, since placental tissues involving a male fetus that were not assumed to be affected by inactivation of X would harbor unique methylation patterns different from the other maternal tissues that more or less involve inactivation of X for certain regions. We further sequenced DNA extracted from the maternal buffy coat sample using single-molecule real-time sequencing. We obtained 2.3 million CCS, with a median underread depth of 5 times per CCS. The results confirmed that maternal Hap I carried the premutation allele with 124 CGG repeats, and maternal Hap II carried the wild-type allele with 43 CGG repeats. Additionally, we further sequenced DNA extracted from chorionic villus samples of the unborn fetus with single-molecule real-time sequencing ηζοΑηη / ζζηζ / Ε / γίΛΐ. We obtained 1.1 million CCS, with a median underread depth of 4 times per CCS. The result confirmed that the unborn fetus carried a wild-type allele. M. Distribution of CpG sites in a human genome Longer DNA fragments result in a higher probability that the fragment has multiple CpG sites. These multiple CpG sites can be used for methylation pattern or other analysis. FIG 35 shows the distribution of CpG sites in a 500 bp region along a human genome. The first column shows the number of CpG sites. The second column shows the number of 500 bp regions with the number of CpG sites. The third column shows the proportion of all regions represented by regions that have the specific number of CpG sites. For example, 86.14% of the 500 bp regions would harbor at least 1 CpG site. Furthermore, 11.08% of the 500 bp regions would harbor at least 10 CpG sites. FIG 36 shows the distribution of CpG sites in a 1 kb region along a human genome. The first column shows the number of CpG sites. The second column shows the number of 1 kb regions with the number of CpG sites. The third column shows the proportion of all regions represented by regions that have the specific number of CpG sites. For example, 91.67% of the 500 bp regions would harbor at least 1 CpG site. Furthermore, 32.91% of the 500 bp regions would harbor at least 10 CpG sites. FIG 37 shows the distribution of CpG sites in a 3 kb region along a human genome. The first column shows the number of CpG sites. The second column shows the number of 3 kb regions with the number of CpG sites. The third column shows the proportion of all regions represented by regions that have the specific number of CpG sites. For example, 92.45% of 3-kb regions would harbor at least 1 CpG site. Furthermore, 87.09% of the 3 kb regions would harbor at least 10 CpG sites. In some embodiments, different numbers of CpG sites and different cutoff sizes would be used to maximize the sensitivity and specificity of placenta-specific marker identification and tissue-of-origin analysis. In general, CpG sites appear more frequently than SNPs. A given size of DNA fragment is likely to have more CpG sites than SNPs. The tables shown above may show lower ratios for regions that have the same number of SNPs as CpG sites, since there are fewer SNPs than CpG sites in the region of the same size. As a result, using CpG sites allows more fragments to be used and provides better statistics than using SNPs alone. N. Examples of tissue of origin analysis In embodiments, analysis of the tissue of origin in maternal plasma may be expanded to more than two organs / tissues, including T cells, B cells, neutrophils, liver, and placenta. We sequenced 9 maternal DNA samples using single-molecule real-time sequencing. We inferred the contribution of the placenta to maternal plasma DNA using plasma DNA methylation patterns according to the methylation status matching approach described in this description. For this methylation status matching analysis, in one embodiment, the methylation pattern of each of the DNA molecules that were at least 500 bp long and contained at least 5 methylation sites was compared. CpG in a maternal plasma DNA sample with reference tissue methylation profiles obtained from bisulfite sequencing. Five tissues were used as reference tissues, including neutrophils, T cells, B cells, liver, and placenta. A plasma DNA molecule would be assigned to the tissue that corresponded to the maximum methylation status match score for that plasma DNA molecule. The percentage of plasma DNA molecules allocated to a tissue relative to other tissues would be considered the proportional contribution of that tissue to the maternal plasma DNA of that sample. In embodiments, the sum of the proportional contribution of neutrophils, T cells and B cells in maternal plasma provided an indirect indicator of the proportional contribution of hematopoietic cells. FIG 38 shows the proportional contributions of DNA molecules from different tissues in maternal plasma using methylation status matching analysis. The first column shows the sample ID. The second column shows the contribution of hematopoietic cells as a percentage. The third column shows the contribution of the liver as a percentage. The fourth column shows the placental contribution as a percentage. FIG 38 shows that the main contributor of maternal plasma DNA was hematopoietic cells (median: 55.9%), which is consistent with previous reports (Sun etal. Proc Nati Acad Sci USA. 2015; 112:E550312; Zheng et al. Clin Chem. 2012;58:549-58). FIG 39A and 39B show the relationship between the placental contribution and the fetal DNA fraction deduced by the SNP approach. The x-axis shows the fetal fraction determined by the SNP approach. The y-axis shows the placental contribution determined in maternal plasma as a percentage using methylation status matching analysis. FIG 39A shows a good correlation between the placental contribution determined by methylation status matching analysis and the fetal DNA fraction inferred by SNP (Pearson's r = 0.95; P value < 0.0001). Additionally, we performed tissue deconvolution analysis of maternal plasma DNA by comparing plasma DNA methylation density determined by single-molecule real-time sequencing with several reference tissue methylation profiles obtained from bisulfite sequencing, from according to quadratic programming (Sun et al. Proc Nati Acad Sci USA. 2015;112:E5503-12). FIG 39B shows that, using the methylation density-based approach, the correlation between placental contribution (Sun et al. Proc Nati Acad Sci USA. 2015;112:E5503-12) and fetal DNA fraction was reduced by comparison with using methylation status matching analysis (Pearson's r = 0.65; P value = 0.059). These data suggested that it was feasible to deduce the proportions of DNA molecules contributed by different tissues in a maternal plasma DNA sample. In another embodiment, this method can also be used to measure DNA molecules from different types of cells or tissues in a sample obtained after an invasive biopsy of solid tissue, or from a solid tissue obtained after surgery. In some embodiments, using the methylation pattern at a single DNA molecule level to infer the proportional contributions of different tissues to maternal plasma DNA would be greater than approaches based on aggregate methylation densities of all plasma DNA molecules. sequenced throughout the genome. ηζοΑηη / ζζηζ / Ε / γίΛΐ O. Example methods FIG 40 shows a method 4000 of analyzing a biological sample obtained from a woman pregnant with a fetus. The biological sample may include a plurality of circulating extracellular DNA molecules from the fetus and the woman. At block 4010, sequence reads corresponding to the plurality of circulating extracellular DNA molecules may be received. In some embodiments, method 4000 may include performing sequencing of circulating extracellular DNA molecules. At block 4020, the sizes of the plurality of circulating extracellular DNA molecules can be measured. The measurement may include alignment of the sequence reads with a reference genome. In some embodiments, the measurement may include full-length sequencing and counting the number of nucleotides in the full-length sequence. In some embodiments, the measurement may include physically separating the plurality of circulating extracellular DNA molecules in the biological sample from other circulating extracellular DNA molecules in the biological sample, where the other circulating extracellular DNA molecules have sizes smaller than the cutoff value. Physical separation may include any technique described herein, including the use of beads. At block 4030, a set of circulating extracellular DNA molecules may be identified from the plurality of circulating extracellular DNA molecules having sizes greater than or equal to a cutoff value. The cutoff value may be greater than or equal to 200 nt. The limit value can be at least 500 nt, which includes 600 nt, 700 nt, 800 nt, 900 nt, 1 knt, 1.1 knt, 1.2 knt, 1.3 knt, 1.4 knt, 1.5 knt, 1.6 knt, 1.7 knt, 1.8 knt , 1.9 knt, or 2 knt. The cutoff value may be any cutoff value described herein for circulating extracellular long DNA molecules. The sizes can be several CpG sites rather than the length of the molecule. For example, the cutoff value may be 3, 4, 5, 6, 7, 8, 9,10,11,12,13,14,15 or more CpG sites. At block 4040, for a circulating extracellular DNA molecule of the set of circulating extracellular DNA molecules, a methylation status at each site of a plurality of sites may be determined. The plurality of sites may include at least 5,6, 7, 8, 9,10,11,12,13,14,15 or more CpG sites. At least one of the plurality of sites may be methylated. Two sites of the plurality of sites may be separated by at least 160 nt, 170 nt, 180 nt, 190 nt, 200 nt, 250 nt, or 500 nt. The method may include sequencing the plurality of circulating extracellular DNA molecules to obtain sequence reads and determining the methylation status of the site by measuring a feature corresponding to a nucleotide of the site and nucleotides neighboring the site. For example, methylation can be determined as in US Application no. 16 / 995,607. At block 4050, a methylation pattern can be determined. The methylation pattern may indicate a methylation state at each site of the plurality of sites. At block 4060, the methylation pattern may be compared to one or more reference patterns. Each of the one or more reference standards can be determined for a particular fabric type. In some embodiments, the comparison may include determining the number of sites that match the reference standard. The reference standard of the one or more reference standards can be determined by measuring the methylation density at each reference site of a plurality of reference sites using DNA molecules from a reference tissue. The methylation density at each reference site of the plurality of reference sites can be compared ηζοΑηη / ζζηζ / Ε / γίΛΐ with one or more threshold methylation densities. Each reference site of the plurality of reference sites can be identified as methylated, unmethylated or uninformative based on comparison of the methylation density with one or more threshold methylation densities, where the plurality of sites is the plurality of reference sites that are identified as methylated or unmethylated. Non-informative sites may include those with methylation densities between two threshold methylation densities. For example, the methylation index of non-informative sites may be between 30 and 70 or any other range, as described herein. At step 4070, a tissue of origin of the circulating extracellular DNA molecule can be determined using the methylation pattern. The tissue of origin may be the placenta. The tissue of origin can be fetal or maternal. The method may include determining the tissue of origin to be the reference tissue when the methylation pattern matches the reference pattern, similar to the description in FIG 22. Match may refer to an exact match. In some embodiments, determining the tissue of origin to be the reference tissue may be when the methylation pattern matches a certain percentage of the reference pattern sites. For example, the methylation pattern may match at least 60%, 70%, 80%, 85%, 90%, 95%, 97% or more of the reference pattern sites. The method may include determining the tissue of origin by determining a similarity score by comparing the methylation pattern to a first reference methylation pattern of a first reference tissue of a plurality of reference tissues. The similarity score can be calculated with the methylation status comparison process or the probabilistic beta distribution model described herein. The similarity score can be compared to a threshold value. The source tissue can be determined to be the first reference tissue when the similarity score exceeds the threshold value. The similarity score may be a first similarity score. The method may further include calculating the threshold value by determining a second similarity score by comparing the methylation pattern to a second reference methylation pattern of a second reference tissue of the plurality of reference tissues. The first reference fabric and the second reference fabric may be different fabrics. The threshold value may be the second similarity score. The first reference tissue may have the highest similarity score compared to all other reference tissues. The first reference methylation pattern may include a first subset of sites that have at least a first methylation probability for the first reference tissue. For example, the first subset of sites may be sites considered methylated or normally methylated. The first reference methylation pattern may include a second subset of sites that have at most a second probability of being methylated for the first reference tissue. For example, the second subset of sites may be sites considered unmethylated or normally unmethylated. Determining the similarity score may include increasing the similarity score when a site of the plurality of sites is methylated and the site of the plurality of sites is in the first subset of sites, and decreasing the similarity score when a site of the plurality of sites is methylated and the site of the plurality of sites is in the second subset of sites. The similarity score can be determined similarly to the methylation status comparison approach described herein. The first reference methylation pattern comprises the plurality of sites, with each site of the plurality of sites characterized by a probability of being methylated and a probability of being unmethylated for the first reference ηζοΑηη / ζζηζ / Ε / γίΛΐ tissue. The similarity score can be determined for each site of the plurality of sites by determining the probability in the reference tissue corresponding to the methylation status of the site on the circulating extracellular DNA molecule. The similarity score can be determined by computing a product of the plurality of probabilities. The product can be the similarity score. The probability can be determined using a beta distribution, similar to the approach described herein. Method 4000 may also include determinae! tissue of origin for each circulating extracellular DNA molecule of the pool of circulating extracellular DNA molecules. This determination may include a certain methylation status at each site of a plurality of respective sites, wherein the plurality of respective sites corresponds to the circulating extracellular DNA molecule. Determining the tissue of origin may also include determining the methylation pattern. Additionally, determining the tissue of origin may also include comparing the methylation pattern with at least one reference standard from one or more reference standards. In some embodiments, the methylation pattern comparison may be similar to FIG 22 and the accompanying description. In FIG 22, the placenta, liver, blood cells, and colon are examples of reference tissues that have the reference standards illustrated. FIG 38 shows hematopoietic cells as another example of a reference tissue. In some embodiments, a number of circulating extracellular DNA molecules corresponding to each tissue of origin can be determined. Each source fabric may include each reference fabric of a plurality of reference fabrics. The fractional contribution of the tissue of origin can be determined using the number of circulating extracellular DNA molecules corresponding to each tissue of origin. For example, the tissue of origin may be the placenta. The other tissues of origin may include hematopoietic cells and the liver. For example, the fractional contribution of the placenta can be determined from the number of circulating extracellular DNA molecules divided by the total circulating extracellular DNA molecules corresponding to all tissues of origin. In some embodiments, the fraction calculated from the number of circulating extracellular DNA molecules divided by the total circulating extracellular DNA molecules may be related to a fractional contribution through a function or set of calibration data points. Both the function and the set of calibration data points can be determined from a plurality of calibration samples with known fractional contributions from the tissue of origin. Each calibration data point can specify a fractional contribution corresponding to a calibration value of the fraction. The function may represent a linear or nonlinear fit of the calibration data points and may relate the fractional contribution to the fraction of the tissue of origin or another parameter related to the tissue of origin. The modalities for determining the fractional contribution may be similar to what has been described with FIGS 39A and 39B. A machine learning model can be used to determine the tissue of origin. The model may be trained by receiving a plurality of training methylation patterns, each training methylation pattern having a methylation state at one or more sites of the plurality of sites, each training methylation pattern determined from a DNA molecule. of a known fabric. Every known tissue molecule can be cellular DNA. Training may include storing a plurality of training samples, each training sample including one of the plurality of training methylation patterns and a label indicating the known ηζοΑηη / ζζηζ / Ε / γίΛΐ tissue corresponding to the training methylation pattern. Training may include optimizing, using the plurality of training samples, model parameters based on model outputs that match or do not match the corresponding labels when the plurality of training methylation patterns are input to the model. The parameters may include a first parameter that indicates whether a site of the plurality of sites has the same methylation status as another site of the plurality of sites. For example, the model may be similar to the pairwise comparison of FIG 24. The parameters may include a second parameter indicating a distance between sites of the plurality of sites. In some embodiments, the machine learning model may not require alignment of a methylation site to a reference genome. A model output may specify a tissue corresponding to an input methylation pattern. The machine learning model may be convolution neural networks (CNN) or any model described herein. The model may include, but is not limited to, linear regression, logistic regression, deep recurrent neural network (e.g., long short-term memory, LSTM), Bayes classifier, hidden Markov model (HMM), linear discriminant analysis (LDA). ), k-means clustering, density-based spatial clustering of applications with noise (DBSCAN), random forest algorithm and support vector machine (SVM). Paternity may be determined by method 4000. The tissue of origin may be fetal. The method may further include aligning a sequence read of the sequence reads with a first region of a reference genome, the first region comprising a plurality of sites corresponding to alleles, the plurality of sites including a threshold number of sites, determining a first haplotype using the respective allele present at each site of the plurality of sites, compare the first haplotype with a second haplotype corresponding to a male subject, and determine a ranking of a probability that the male subject is the father of the fetus using the comparison . The male subject can be considered likely to be the father if the haplotypes match or not likely to be the father if the haplotypes do not match. In some embodiments, the first haplotype can be compared to both haplotypes of the male subject. In embodiments, paternity can be proven when the tissue of origin is fetal by aligning a sequence read of the sequence reads with a first region of a reference genome. The first region may include a first plurality of sites corresponding to alleles. The plurality of sites may include a threshold number of sites. The threshold number of sites can be 3,4,5,6,7,8,9,10,11,12,13,14,15 or more sites. The allele at each site of the plurality of sites can be compared to an allele at the corresponding site in the genome of a male subject. A ranking of the probability that the male subject is the father of the fetus can be determined using the comparison. The male subject can be considered likely to be the father if a certain number or percentage of alleles match and is not likely to be the father if less than that number or percentage matches. The limit percentage can be 100%, 90%, 80%, or 70%. In some embodiments, a haplotype can be determined. The methods may include for each circulating extracellular DNA molecule of the pool of circulating extracellular DNA molecules, aligning the sequence read corresponding to the circulating extracellular DNA molecule to a reference genome. The sequence reading can be identified as corresponding to a haplotype present in the woman. The haplotype present in the woman can be known nzoAnn / zznz / E / YiAi from the genotyping of the woman. In some embodiments, the haplotype of the woman can be known by analyzing the concentrations of DNA fragments of the haplotype in a biological sample of the woman. The tissue of origin can be determined as fetal using the methylation pattern. The haplotype can be determined to be a fetal haplotype inherited from the mother. The inheritance of a haplotype can be determined using methylation of reference tissues rather than using known methylation profiles such as those associated with sealed loci. Matching or similarity scoring of a methylation pattern to a reference pattern can preclude knowledge of whether a given allele or site is methylated based on the parent from which it was inherited. The haplotype can be identified as carrying a disease-causing mutation or genetic variation. Identification of the haplotype as carrying the disease-causing genetic mutation may include identification of the mutation or genetic variation in a first sequence read. A genetic variation can include a single nucleotide difference, a deletion, or an insertion. A first methylation level may be measured in a second sequence read corresponding to a first genomic location within a first distance of the first sequence read. A second methylation level may also be measured in a third sequence read corresponding to a second genomic location within a second distance from the first sequence read. The first distance can be 100 nt, 200 nt, 300 nt, 400 nt, 500 nt, 600 nt, 700 nt, 800 nt, 900 nt, 1 knt, 2 knt, 5 knt, or 10 knt. The second sequence read and the third sequence read may be on the same chromosome arm as the first sequence read. The first methylation level and the second methylation level may be associated with the mutation or genetic variation. The first methylation level and the second methylation level may be greater than one or two threshold levels associated with the mutation or genetic variation. Threshold levels can be determined using subjects who are known to have or do not have the genetic mutation or variation. The method may include classifying that the fetus is likely to have the disease caused by the genetic mutation or variation. Fetus-specific methylation patterns can be determined. The method may include for each circulating extracellular DNA molecule of the pool of circulating extracellular DNA molecules, aligning the sequence read corresponding to the circulating extracellular DNA molecule to a reference genome. The method may include identifying the sequence read as corresponding to a region. The region can be determined by receiving a plurality of fetal sequence reads corresponding to a plurality of fetal DNA molecules from the fetal tissue. The method may include receiving a plurality of maternal sequence reads corresponding to a plurality of maternal DNA molecules. The method may include determining a fetal methylation status at each methylation site of a plurality of methylation sites within the region for each fetal sequence read of the plurality of fetal sequence reads. The method may include determining a maternal methylation status at each methylation site of the plurality of methylation sites for each maternal sequence read of the plurality of maternal sequence reads. The method for determining fetal-specific methylation patterns may include determining the value of a parameter that characterizes a number of sites where the fetal methylation status differs from the maternal methylation status. The method may include comparing the value of the parameter with a threshold value. The parameter may be a nzoAnn / zznz / E / YiAi ratio of sites that differ between fetal DNA molecules and maternal DNA molecules. The ratio may be a mismatch score described herein. The threshold value may indicate a minimum level of mismatch score and may be 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9 or more. In some embodiments, the threshold value may represent an average mismatch score for maternal or fetal DNA molecules. The method may include determining that the value of the parameter exceeds the threshold value. In some embodiments, a certain percentage of maternal or fetal DNA molecules may be required for the parameter value to exceed the threshold value. For example, the percentage can be 50%, 60%, 70%, 80%, 90%, or more. In some embodiments, it may be necessary that a certain percentage of the fetal DNA molecules corresponding to the region have the fetus-specific methylation pattern. For example, the percentage can be 40%, 50%, 60%, 70%, 80%, or more. This method may be similar to the methods described with FIG 25. The method may include enrichment of the biological sample in circulating extracellular DNA molecules from the tissue of origin. Enrichment of the biological sample may include selecting and amplifying the pool of circulating extracellular DNA molecules. Enrichment may include selection based on size, as described herein. In some embodiments, enrichment may include selection based on methylation pattern. For example, methyl-CpG binding domain (MBD)-based capture and sequencing can be used. Circulating extracellular DNA can be incubated with labeled MBD proteins that can bind methylated cytosines. The protein-DNA complex can then be precipitated with antibody-conjugated magnetic beads. DNA molecules with more methylated CpG sites can be preferentially enriched for downstream analysis. VARIATION OF LONG FRAGMENTS OF CIRCULATING EXTRACELLULAR DNA WITH GESTATIONAL AGE The amount of long fragments of circulating extracellular DNA may vary with gestational age. Long fragments of circulating extracellular DNA can be used to determine a gestational age. Furthermore, long fragments of circulating extracellular DNA may be more abundant in certain end motifs compared to shorter fragments of circulating extracellular DNA, and the relative amount of certain end motifs may vary with gestational age. The number of final motifs can also be used to determine a gestational age. A deviation from a gestational age determined using long fragments of circulating extracellular DNA and a gestational age determined through other clinical techniques may indicate a pregnancy-associated disorder. In some embodiments, long fragments of circulating extracellular DNA can be used to determine the probability of a pregnancy-associated disorder without necessarily determining a gestational age. P. Size analysis for fetal and maternal DNA Plasma DNA from two pregnant women in the first trimester (gestational age: 13 weeks), two in the second trimester (gestational age: 21 to 22 weeks), and five in the third trimester (gestational age: 38 weeks) was sequenced using single-molecule real-time (SMRT) sequencing (PacBio). A median of 176 million (range: 49 - 685 million) subreads was obtained for each case, among which 128 million (range: 35 - 507 million) subreads could be aligned to the human reference genome (hg19). Each molecule in a well ηζοΑηη / ζζηζ / Ε / γίΛΐ SMRT was sequenced 107 times on average. A median of 965,308 (range: 251,686 - 2,871,525) high-quality circular consensus sequencing (CCS) reads, which was defined as CCS reads with at least 3 subreads, could be used for further analysis. All molecules sequenced from samples obtained from each trimester of pregnancy were pooled for size analyses. There were a total of 1.94 million, 5.09 million, and 4.45 million circulating extracellular DNA molecules for the first, second, and third trimester maternal plasma samples, respectively. FIGS. 41A and 41B show the size distributions of circulating extracellular DNA molecules from first, second, and third trimester maternal plasma samples within a size range of 0 to 5 kb. The x axis shows the size. The y-axis shows the frequency. The size distribution is represented in the range for FIG 41 A, from 0 to 5 kb on a linear scale for the y axis, and for FIG 41B, from 0 to 5 kb on a logarithmic scale for the y axis. Plasma DNA from all three trimesters of pregnancy demonstrated the expected major peak at 166 bp as shown in FIG 41A and a series of major peaks occurring in periodic patterns extending to molecules within a range of 1 kb and 2 kb as shown in FIG 41B. FIG 42 is a table showing the proportion of long DNA molecules in plasma in different trimesters of pregnancy. The first column shows the gestational age associated with the plasma sample. The second column shows the proportion of DNA molecules longer than 500 bp. The third column shows the proportion of DNA molecules larger than 1 kb. Compared with the first and second trimester, the third trimester had an increase in the frequency of plasma DNA molecules of 500 bp or greater. The proportions of plasma long DNA molecules greater than 500 bp were 15.8%, 16.1%, and 32.3% for the first, second, and third trimesters, respectively. The proportions of plasma long DNA molecules greater than 1 kb were 11.3%, 10.6%, and 21.4% for the first, second, and third trimesters, respectively. While first and second trimester maternal plasma showed a similar proportion of circulating extracellular long DNA molecules, third trimester maternal plasma had approximately double the proportion of long DNA molecules. For all maternal plasma DNA samples analyzed for this description, DNA extracted from paired maternal buffy coat and fetal samples was genotyped with the Infinium Omn¡2.5Exome-8 Beadchip on the ¡Sean (lllumina) system, which is a genotyping method based on array hybridization. Fetal samples were obtained by chorionic villus sampling, amniocentesis, or placenta sampling, depending on whether the case was from the first, second, or third trimester, respectively. For each case, a median of 203,647 informative single nucleotide polymorphisms (SNPs) were identified for which the mother was homozygous and the fetus was heterozygous. We identified a total of 1,362, 2,984, and 6,082 DNA molecules covering fetus-specific alleles for the first, second, and third trimester, respectively, when DNA molecules sequenced for all cases from each trimester were pooled. Furthermore, for each case a median of 210,820 informative SNPs were identified for which the mother was heterozygous and the fetus homozygous. We identified a total of 30,574,65,258, and 78,346 DNA molecules covering maternal-specific alleles for the first, second, and third trimesters, respectively. The median fetal DNA fraction, which was determined from sequencing data for DNA molecules <600 bp, among all maternal plasma samples was 15.6% (range, 7.6-26.7%). ηζοΑηη / ζζηζ / Ε / γίΛΐ FIG 43A and 43B show size distributions of DNA molecules covering fetus-specific alleles from maternal plasma from the first, second and third trimesters. The x axis shows the size. The y-axis shows the frequency. The size distribution is represented in the range in FIG 43A, from 0 to 3 kb on a linear scale for the y-axis, and for FIG 43B, from 0 to 3 kb on a logarithmic scale for the y-axis. FIG 44A and 44B show size distributions of DNA molecules covering mother-specific alleles from maternal plasma from the first, second and third trimester. The x axis shows the size. The y-axis shows the frequency. The size distribution is represented in the range in FIG 44A, from 0 to 3 kb on a linear scale for the y-axis, and for FIG 44B, from 0 to 3 kb on a logarithmic scale for the y-axis. As shown in FIGs 43A to 44B, plasma DNA molecules covering maternal and fetal-specific alleles from all three trimesters of pregnancy showed long-tailed distributions, suggesting the presence of long DNA molecules derived from sources maternal and fetal in the three trimesters. FIG 45 is a table of the proportion of long DNA molecules in fetal and maternal plasma in different trimesters of pregnancy. The first column shows the gestational age associated with the plasma sample. The second column shows the proportion of fetal DNA molecules longer than 500 bp. The third column shows the proportion of maternal DNA molecules longer than 500 bp. The fourth column shows the proportion of fetal DNA molecules larger than 1 kb. The fifth column shows the proportion of maternal DNA molecules longer than 1 kb. Among the set of DNA molecules in maternal plasma, those covering a fetus-specific allele (placental origin) had a lower proportion of long DNA molecules compared to those covering a maternal-specific allele. The proportions of plasma long DNA molecules covering a fetus-specific allele with a size greater than 500 bp were 19.8%, 23.2%, and 31.7% for the first, second, and third trimesters, respectively. The proportions of long plasma DNA molecules covering a fetus-specific allele with a size greater than 1 kb were 15.2%, 16.5%, and 19.9% ​​for the first, second, and third trimesters, respectively. Although there was a lower proportion of long plasma DNA molecules present in first and second trimester maternal plasma compared to the third trimester, and fetal DNA molecules contained less long DNA molecules in all three trimesters, the method described in our previous description and this allowed us to analyze a substantial proportion of long DNA molecules in plasma that was not previously possible with short read sequencing technologies. Furthermore, different size selection strategies including, but not limited to, electrophoretic, chromatographic, and bead-based methods could be used to enrich long DNA fragments in plasma samples. FIGS. 46A, 46B and 46C show graphs of the proportions of fetus-specific plasma DNA fragments of a particular size range over different trimesters. The gestational ages of the evaluated pregnant women were verified by ultrasound dating. FIG 46A shows results for DNA fragments less than or equal to 150 bp. FIG 46B shows results for DNA fragments of 150 to 600 bp. FIG 46C shows results for DNA fragments greater than or equal to 600 bp. The graphs have the proportion of specific fetal fragments on the y-axis, and the gestational age on the x-axis. As shown in the graphs, both the proportions of fetus-specific fragments of less than 150 bp (FIG 46A) and more than 600 bp (FIG 46C) would achieve a certain ηζοΑηη / ζζηζ / Ε / γίΛΐ discriminatory power to differentiate the third trimester samples compared to those from the first and second trimester, compared with the proportion of fetus-specific fragments ranging between 150 and 600 bp (FIG 46B). Proportions of fetus-specific fragments longer than 600 bp may provide the best discriminatory power. This conclusion was evidenced by the fact that the absolute minimum distance between the third trimester group and the combined first and second trimester group was 0.38 when the proportions of specific fetal fragments less than 150 bp were used, while the value of the counterpart was 3.76 when proportions of specific fetal fragments greater than 600 bp were used. These results suggested that the use of long DNA molecules to reflect the pathophysiological state would be superior to the use of short DNA molecules. Q. Final plasma DNA analysis In addition to size, we determined the first nucleotide at the 5' end of the Watson and Crick strands separately for each DNA molecule sequenced. This analysis consisted of 4 types of ends, namely A-end, C-end, G-end and T-end. The percentages of plasma DNA molecules with a particular end were calculated from maternal plasma samples obtained from each trimester. The percentages of A-terminus, C-terminus, G-terminus, and T-terminus in each fragment size were further analyzed. FIGS. 47A, 47B and 47C show graphs of base content ratios at the 5' end of circulating extracellular DNA molecules in maternal plasma from the first, second and third trimester in the fragment size range of 0 to 3 kb. FIG 47A shows maternal plasma from the first trimester. FIG 47B shows maternal plasma from the second trimester. FIG 47C shows maternal plasma from the third trimester. The base content as a percentage is shown on the y-axis. The fragment size in base pairs is shown on the x-axis. As seen in the graphs, the C end was overrepresented in many size ranges (mostly less than 1 kb) and varied according to the different size ranges for the first, second, and third trimester samples. The final plasma DNA patterns of the third trimester samples appeared to be different from the first and second trimester samples. For example, the T-end and G-end curves were mixed at sizes ranging from 105 to 172 bp, while they were divergent in the first and second quarter samples. For longer fragments (e.g., more than about 1 kb), the C-terminal fragments are not the most abundant fragments. The G-terminal fragments outnumber the C-terminal fragments by about 1 kb, and then the A-terminal fragments become more abundant than the G-terminal fragments by about 2 kb. FIG 48 is a table of the ratios of final nucleotide bases between short and long molecules of circulating extracellular DNA from maternal plasma of the first, second and third trimester. The first column shows the base at the end of the molecule. The second column shows the expected proportion point and the species. The third column shows the proportion of a final species among fragments less than or equal to 500 bp for first trimester maternal plasma. The fourth column shows the proportion of a final species among fragments larger than 500 bp for first trimester maternal plasma. The fifth column and the sixth column are similar to the third column and the fourth column, respectively, except for the maternal plasma from the second trimester and instead the maternal plasma from the first trimester ηζοΑηη / ζζηζ / Ε / γίΛΐ. The seventh column and the eighth column are similar to the third column and the fourth column, respectively, except for the third trimester maternal plasma and instead the first trimester maternal plasma. If the fragmentation of circulating extracellular DNA were completely random, the ratios of final nucleotide bases should reflect the composition of the human genome, which is 29.5% A, 29.5% T, 20.5% C, and 20.5% G, as shown in the second column of FIG 48. In contrast to random fragmentation, the 5' end of circulating short extracellular DNA molecules of <500 bp showed substantial overrepresentation of the C terminus (30.4%, 30.4%, and 31.3% for maternal plasma from the first, second and third trimester, respectively), a slight overrepresentation of the G terminus (27.4%, 26.9% and 25.3% for the first, second and third trimester, respectively) and an underrepresentation of the A terminus (19.8%, 19.4% and 19.3% for the first, second and third quarters, respectively) and extreme T (22.4%, 23.3% and 24.1% for the first, second and third quarters, respectively). However, compared to circulating extracellular short DNA molecules, circulating extracellular long DNA molecules of >500 bp showed a substantial increase in the proportion of A-termini (29.6%, 26.0%, and 26.7% for maternal plasma of the former). , second and third quarter, respectively), a slight increase in the proportion of G-ends (31.0%, 29.5% and 29.9% for the first, second and third quarter, respectively), a substantial decrease in the proportion of T-ends ( 13.9%, 16.9% and 16.4% for the first, second and third quarters, respectively) and a slight decrease in the proportion of C-ends (25.5%, 27.5% and 27.1% for the first, second and third quarters, respectively). FIG 49 is a table of the ratios of final nucleotide bases between short and long molecules of circulating extracellular DNA covering a specific allele of the fetus from maternal plasma of the first, second and third trimester. FIG 50 is a table of the ratios of final nucleotide bases between short and long circulating extracellular DNA molecules covering a mother-specific allele from first, second and third trimester maternal plasma. The first column shows the base at the end of the molecule. The second column shows the expected proportion point and the species. The third column shows the proportion of a final species among fragments less than or equal to 500 bp for first trimester maternal plasma. The fourth column shows the proportion of a final species among fragments larger than 500 bp for first trimester maternal plasma. The fifth column and the sixth column are similar to the third column and the fourth column, respectively, except for the second trimester maternal plasma and instead the first trimester maternal plasma. The seventh column and the eighth column are similar to the third column and the fourth column, respectively, except for the third trimester maternal plasma and instead the first trimester maternal plasma. FIGs 49 and 50 show that such a difference in final nucleotide base ratios between short and long circulating extracellular DNA molecules remained unchanged even when we separately examined the DNA molecules covering the fetal- and maternal-specific alleles. FIG 51 illustrates hierarchical clustering analysis of short and long extracellular DNA molecules circulating in plasma using 256 4-mer end motifs. Each column indicates a sample used to analyze the final motif frequency based on short (indicated by cyan in the first row) and long fragments (indicated by yellow in the first row), respectively. Starting from the second row, each row indicates a type of final motif. The final motif frequencies were presented with a series of color gradients according to the row-normalized frequencies ηζοΑηη / ζζηζ / Ε / γίΛΐ (z-score) (i.e., the number of standard deviations below or above the frequency average in the samples). The redder color indicates a higher frequency of an end motif, while the bluer color indicates a lower frequency of an end motif. In FIG 51, we characterize short and long circulating extracellular DNA molecules by analyzing their 4-mer end motif profiles. We determined the first 4-nucleotide sequence (a 4-mer motif) at the 5' end of the Watson and Crick strands separately for each DNA molecule sequenced. For each maternal plasma sample, the frequency of each plasma DNA end motif was calculated separately for short (<500 bp) and long (>500 bp) plasma DNA molecules. Hierarchical clustering analysis based on the frequencies of the 256 4-mer end motifs showed that the end motif profiles of long DNA molecules in different maternal plasma samples formed a cluster that was distinct from that of short DNA molecules. These results suggested that long and short DNA possessed different fragmentation properties. In embodiments, the relative perturbation of these end motifs between long and short DNA molecules would be used to indicate the contributions of circulating extracellular DNA originating from cell death pathways, such as, but not limited to, apoptosis and necrosis. Increased activity of these cell death pathways may be related to pregnancy-associated and other disorders. FIG 52A and 52B show principal component analysis (PCA) using 4-mer end motif profiles for classification analysis. FIG 52A shows circulating extracellular short DNA molecules (<500 bp) from different trimesters. FIG 52B shows circulating extracellular long DNA molecules (>500 bp) from maternal plasma samples from different trimesters. Percentages in parentheses on the x and y axes represent the amount of variability explained by the corresponding component. Each blue dot represents a maternal plasma sample from the first trimester. Each yellow dot represents a maternal plasma sample from the second trimester. Each red dot represents a maternal plasma sample from the third trimester. Ellipse represents a 95% confidence level for clustering data points for a particular quarter. Compared to circulating extracellular short DNA molecules (FIG 52A) (also described in US Application No. 15 / 787,050), the 4-mer end motif profiles of circulating extracellular long DNA molecules (FIG 52B ) resulted in a clearer separation between first, second, and third trimester maternal plasma samples. In embodiments, end motif profiling of long plasma DNA molecules could be used alone or in combination with other characteristics of maternal plasma DNA, including, but not limited to, methylation level and size, for molecular assessment. of gestational age. For example, we used neural networks to train a model to predict gestational age based on the final 256 motifs, the overall methylation level, and the proportion of fragments with a size >600 bp. The output variables were 1, 2 and 3, which represent the 1st, 2d0 and 3rd quarter. Input variables included 256 end motifs, overall methylation level, and proportion of fragments >600 bp in size. We used the leave-one-out approach to evaluate the performance of gestational age prediction. For a data set comprising 9 samples, the leave-one-out approach was carried out such that one sample was selected as the test sample and the remaining 8 samples were used to train a neural network-based model. ηζοΑηη / ζζηζ / Ε / γίΛΐ said test sample was determined to be 1, 2 or 3 according to the established model. We then repeated this process for other samples that had not yet been analyzed. In total, we repeat 9 times for such training and testing process. When comparing test results with clinical information on gestational ages, 8 of 9 samples (89%) were correctly predicted in terms of gestational ages. In another embodiment, said analysis can be performed, for example, but not limited to, using Bayes' theorem, logistic regression, multiple regression and support vector machine, random forest analysis, classification and regression tree (CART), K-nearest neighbors algorithm. All molecules sequenced from samples obtained from each trimester of pregnancy were then pooled for final downstream motif analyses. The final 256 motifs were ranked according to their frequencies among short and long DNA molecules in plasma. FIGS. 53 to 58 are tables of the final 25 motifs with the highest frequencies for certain DNA fragment lengths (less than or greater than 500 bp) and for different quarters. FIGs 53, 54 and 55 are tables with final motifs ordered by their rank in short fragments (<500 bp). In FIGs 53 to 55, the first column shows the final reason. The second column shows the frequency range of the motif in short fragments. The third column shows the frequency range of the motif in long fragments. The fourth column shows the frequency of the motif in short fragments. The fifth column shows the frequency of the motif in long fragments. The sixth column shows the variation multiple (motif frequency in short fragments divided by motif frequency in long fragments). FIGs 56, 57 and 58 are tables with final motifs ordered by their rank in long fragments (>500 bp). In FIGs 56 to 58, the first column shows the final motif. The second column shows the frequency range of the motif in long fragments. The third column shows the frequency range of the motif in short fragments. The fourth column shows the frequency of the motif in long fragments. The fifth column shows the frequency of the motif in short fragments. The sixth column shows the variation multiple (motif frequency in long fragments divided by the motif frequency in short fragments). FIG 53 and 56 are samples from the first quarter. FIGs 54 and 57 are from samples from the second trimester. FIG 55 and 58 are from third trimester samples. Among the top 25 end motifs with the highest frequencies among short DNA molecules in plasma, 11 of these started with CC dinucleotides. End motifs beginning with CC together accounted for 14.66%, 14.66%, and 15.13% of plasma DNA short end motifs in first-, second-, and third-trimester maternal plasma, respectively. Among the top 25 end motifs with the highest frequencies among long DNA molecules in plasma, 4-mer motifs ending with TT dinucleotides accounted for 9 of these in second and third trimester maternal plasma, and 10 of these in the first trimester maternal plasma. We determined the dinucleotide sequence of the third (X) and fourth nucleotide (Y) of the 5' end of the Watson and Crick strands separately for each DNA molecule sequenced. X and Y can be one of the four nucleotide bases in DNA. There were 16 possible NNXY motifs, namely, NNAA, NNAT, NNAG, NNAC, NNTA, NNTT, NNTG, NNTC, NNGA, NNGT, NNGG, NNGC, NNCA, NNCT, NNCG, and NNCC. ηζοΑηη / ζζηζ / Ε / γίΛΐ FIGS. 59A, 59B and 59C show scatter plots of motif frequencies of 16 NNXY motifs between short and long plasma DNA molecules. FIG 59A shows the results for the first quarter. FIG 59B shows the results for the second quarter. FIG 59C shows the results for the third quarter. The motif frequency of long fragments is shown on the y-axis. The motif frequency of short fragments is shown on the x-axis. Each circle represents one of the 16 NNXY motifs. The pair of dashed lines in each scatterplot indicates a 1.5-fold increase (top line) and decrease (bottom line) in motif frequencies in long plasma DNA molecules (>500 bp) compared to plasma DNA molecules. in short plasmas (<500 bp). Circles located outside the shaded area represent motifs with a variation multiple of >1.5. While the ends of short DNA molecules in plasma showed high frequencies of 4-mer motifs starting with CC dinucleotides (CCNN) (Jiang et al. Cancer Discov 2020;10(5):664-673; Chan et al. Am J Hum Genet 2020; 107(5):882-894), the ends of long DNA molecules in plasma showed a > 1.5-fold increase in the frequencies of the 4-mer motif ending in TT (NNTT) in the three quarters (FIG 11A-11B). The NNTT motif accounted for 18.94%, 15.22%, and 15.30% of long plasma DNA end motifs in maternal plasma from the first, second, and third trimesters, respectively. In contrast, the NNTT motif only accounted for 9.53%, 9.29%, and 8.91% of the DNA end motifs in short plasmas in maternal plasma from the first, second, and third trimesters, respectively. As previously reported by Han et al., circulating extracellular DNA freshly released from dying cells into plasma was enriched in A-terminal fragments >150 bp. DNA fragmentation factor beta (DFFB), which is the main intracellular nuclease involved in DNA fragmentation during apoptosis, was found to be responsible for generating such fragments (Han et al. Am J Hum Genet 2020;106:202 -214). In this description, we have shown that circulating extracellular long DNA molecules of >500 bp were also enriched in A-terminal fragments, suggesting that DFFB could also be responsible for generating these fragments. In normal pregnancy, trophoblast apoptosis increases with advancing gestation (Sharp et al. Am J Reprod Immuno 2010;64(3):159-69). Indeed, our finding of increasing proportions of long DNA molecules covering the fetus-specific allele with advancing trimesters could reflect increased trophoblast apoptosis with advancing trimesters. In embodiments, methods described herein could be used to analyze circulating extracellular long DNA molecules in maternal plasma for the prediction, detection and monitoring of the progression of placenta-related pregnancy complications, including, but not limited to , preeclampsia, intrauterine growth restriction (IUGR), preterm labor and gestational trophoblastic disease. An increased level of trophoblast apoptosis has been reported in placenta-related pregnancy complications such as preeclampsia (Leung et al. Am J Obstet Gynecol 2001;184:1249-1250), IUGR (Smith et al. Am J Obstet Gynecol 1997;177:1395-1401; Levy et al. Am J Obstet Gynecol 2002;186:1056-1061), and gestational trophoblastic disease. Furthermore, an elevated level of fetal DNA in maternal plasma has been reported in preeclampsia (Lo et al. Clin Chem 1999;45(2): 184-8; Smid et al. Ann N Y Acad Sci 2001;945:132- 7), IUGR (Sekizawa et al. Am J Obstet Gynecol 2003;188:480-4), and preterm labor (Leung et al. Lancet 1998;352(9144):1904-5). We hypothesized that in placenta-related ηζοΑηη / ζζηζ / Ε / γίΛΐ pregnancy complications, there would be a higher proportion of long circulating extracellular DNA molecules of placental origin in maternal plasma samples due to increased placental apoptosis. Therefore, circulating extracellular long DNA molecules of placental origin per se, as well as long DNA signatures including, but not limited to, A-terminal fragments and NNTT motifs, could serve as biomarkers for placental apoptosis. Although one-nucleotide and 4-nucleotide motifs are used in the above analysis, in other embodiments motifs of other lengths may be used, for example, 2.3, 5, 6, 7, 8, 9,10 or more. R. Example methods Long fragments of circulating extracellular DNA can be used to determine the gestational age of a woman carrying a fetus. The amount of long fragments of circulating extracellular DNA varies with gestational age and can be used to determine gestational age. The final motif of circulating extracellular DNA fragments also varies with gestational age and can be used to determine gestational age. When the gestational age determined using long fragments of circulating extracellular DNA deviates significantly from the gestational age determined through other clinical techniques, then the pregnant woman and / or fetus may be considered to have a pregnancy-associated disorder. In some embodiments, it may not be necessary to determine gestational age to determine the likelihood of a pregnancy-associated disorder. 1. Gestational age FIG 60 shows a method 6000 of analyzing a biological sample obtained from a woman pregnant with a fetus. Gestational age can be determined and can be used to classify the likelihood of a pregnancy-associated disorder. The biological sample may include a plurality of circulating extracellular DNA molecules from the fetus and the woman. Sequence readings corresponding to the plurality of circulating extracellular DNA molecules can be received. In some embodiments, sequencing may be performed to obtain sequence reads. At block 6020, the sizes of the plurality of circulating extracellular DNA molecules can be measured. The sizes can be measured in a similar manner as described in FIG 21. The sizes can be measured using the sequence readings. At block 6030, a first quantity of circulating extracellular DNA molecules having sizes greater than a limit can be measured. The quantity can be a number, a total length, or a mass of circulating extracellular DNA molecules. In block 6040, a normalized parameter value may be generated using the first quantity. The normalized parameter value may be the first quantity normalized by the total number of circulating extracellular DNA molecules, by the number of circulating extracellular DNA molecules of the fetus or mother, or by the number of DNA molecules from a specific region . For example, the normalized parameter may be a proportion of specific fragments of the fetus, as described in FIG 46A-46C. ηζοΑηη / ζζηζ / Ε / γίΛΐ At block 6050, the normalized parameter value may be compared to one or more calibration data points. Each calibration data point can specify a gestational age corresponding to a calibration value of the normalized parameter. For example, a gestational age of a certain trimester or a certain number of weeks may correspond to a calibration value of the normalized parameter. The one or more calibration data points may be determined from a plurality of calibration samples with known gestational ages and including circulating extracellular DNA molecules having sizes greater than the cutoff value. In some embodiments, calibration data points are determined from a function that correlates gestational age with normalized parameter values. At block 6060, a gestational age can be determined using the comparison. Gestational age can be considered as the age corresponding to the calibration value closest to the normalized parameter value. In some embodiments, the gestational age may be considered as the oldest age to correspond to the calibration value exceeded by the normalized parameter value. The method may further include determining a reference gestational age of the fetus using ultrasound or the date of the woman's last menstrual period. The method may also include comparison of the gestational age with the reference gestational age. The method may further include determining a classification of the probability of a disorder associated with pregnancy using comparison of the gestational age with the reference gestational age. For example, a discrepancy between gestational age and the reference gestational age may indicate a pregnancy-associated disorder. The discrepancy may be a different trimester or a difference in gestational age by a minimum number of weeks (for example, 1,2,3, 4, 5, 6, 7 or more weeks). The method may also include the use of final motifs. For example, the method may include determining a first subsequence corresponding to at least one end of circulating extracellular DNA molecules that have sizes greater than the cutoff value. The first quantity may be circulating extracellular DNA molecules that have a size larger than the cutoff value and that have the first subsequence at one or more ends of the respective circulating extracellular DNA molecule. The first subsequence may be or include 1, 2, 3, 4, 5 or 6 nucleotides. The final motifs can be used to determine gestational age through PCA analysis, as described with FIG 52A and 52B. Calibration samples can be used with different final motifs and known gestational ages and can be subjected to PCA analysis. Other classification and regression algorithms can be used on the final motifs, such as linear discriminant analysis, logistic regression, support vector machine, linear regression, nonlinear regression, etc. Classification and regression algorithms can relate a gestational age to certain end motifs and / or fragments of a certain size. End motifs may be any motif discussed with FIG 47A-47C-59A-59C or 94. A range or frequency of an end motif may be compared to ranges or frequencies of the end motif in calibration samples from subjects of known gestational ages. The range or frequency of the final motif can be used to determine a gestational age. A final motif present in a range or frequency that deviates from a range or frequency determined from reference samples of the same gestational age may indicate a pregnancy-associated disorder. ηζοΑηη / ζζηζ / Ε / γίΛΐ Generating the normalized parameter value may include (a) normalizing the first quantity by a total quantity of circulating extracellular DNA molecules having a size greater than the cutoff value; (b) normalize the first quantity by a second quantity of circulating extracellular DNA molecules that have a size larger than the limit and that end in a second subsequence, the second subsequence is different from the first subsequence, or (c) normalize the first amount by a third amount of circulating extracellular DNA molecules that have a size smaller than the cutoff value. 2. Pregnancy-associated disorder FIG 61 shows a method 6100 of analyzing a biological sample obtained from a woman pregnant with a fetus. Modalities may include classifying a probability of a pregnancy-associated disorder without necessarily determining a gestational age. The biological sample may include a plurality of circulating extracellular DNA molecules from the fetus and the woman. Sequence readings corresponding to the plurality of circulating extracellular DNA molecules can be received. In some embodiments, sequencing may be performed to obtain sequence reads. At block 6120, the sizes of the plurality of circulating extracellular DNA molecules can be measured. The sizes can be obtained in a manner similar to that described with FIG 21. The measurement sizes can use the sequence of readings received. At block 6130, a first quantity of circulating extracellular DNA molecules having sizes greater than a threshold value may be measured. The cutoff value may be greater than or equal to 200 nt. The limit value can be at least 500 nt, which includes 600 nt, 700 nt, 800 nt, 900 nt, 1 knt, 1.1 knt, 1.2 knt, 1.3 knt, 1.4 knt, 1.5 knt, 1.6 knt, 1.7 knt, 1.8 knt , 1.9 knt, or 2 knt. The cutoff value may be any cutoff value described herein for circulating extracellular long DNA molecules. The first quantity can be a number or a frequency. At block 6140, a first value of a normalized parameter may be generated using the first quantity. Generation of the normalized parameter value may include measuring a second quantity of circulating extracellular DNA molecules that include sizes less than the cutoff value; and calculate a proportion of the first quantity and the second quantity. The limit value may be a first limit value. A second limit value may be less than the first limit value. The second amount may include circulating extracellular DNA molecules having sizes smaller than the second cutoff value or the second amount may include all circulating extracellular DNA molecules in the plurality of circulating extracellular DNA molecules. The normalized parameter may be a measure of the frequency of circulating extracellular long DNA molecules. At block 6150, a second value corresponding to an expected value of the normalized parameter for a healthy pregnancy can be obtained. The second value may depend on the gestational age of the fetus. The second value can be the expected value. In some embodiments, the second value may be a threshold value that is distinguished from an abnormal value. Obtaining the second value may include obtaining the second value from a calibration table that relates the measurements of pregnant women to the calibration values ​​of the normalized parameter. The calibration table ηζοΑηη / ζζηζ / Ε / γίΛΐ can be generated by obtaining a first table that relates the gestational ages with the measurements of the pregnant women. A second table can be obtained that relates the gestational ages with the calibration values ​​of the normalized parameter. The data in the first and second tables may be from the same subjects or from different subjects. The calibration table that relates the measurements to the calibration values ​​can be created from the first table and the second table. A calibration table may include a function that relates calibration values ​​to measurements. Measurements of pregnant women may be the time since the last menstrual period or characteristics of an image of pregnant women (for example, an ultrasound). Measurements of pregnant women may be characteristic of images of pregnant women. For example, image characteristics may include length, size, appearance, or anatomy of a fetus of the female subject. Characteristics may include biometric measurements, for example, cephalocaudal length or femur length. The appearance of certain organs can be used, including the appearance of a four-chambered heart or vertebrae in the spinal cord. A doctor can determine gestational age from an ultrasound image (e.g., Committee on Obstetric Practice et al., “Methods for estimating the due date,” Committee Opinion, No. 700, May 2017). In some embodiments, a machine learning model may associate one or more calibration data points with image features. The model can be trained by receiving a plurality of training images. Each training image may be of a female subject who is not known to have a pregnancy-associated disorder or who is not known to have a pregnancy-associated disorder. Female subjects may have a range of gestational ages. The training may include storing a plurality of training samples of the female subjects. Each training sample may include a known value of the normalized parameter associated with the training image. The model may be trained by optimizing, using the plurality of training samples, model parameters based on model outputs that match or do not match the image with the known value of the normalized parameter. The model output may specify a normalized parameter value corresponding to an image. The second normalized parameter value can be generated by inputting an image of the woman into the machine learning model. At block 6160, a deviation between the first normalized parameter value and the second normalized parameter value may be determined. The deviation can be a separation value. At block 6170, a classification of a probability of a disorder associated with pregnancy can be determined using the deviation. Pregnancy-associated disorder may be likely when the deviation exceeds a threshold. The threshold may indicate a statistically significant difference. The threshold can indicate a difference of 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, or 100%. Pregnancy-associated disorder may include preeclampsia, intrauterine growth restriction, invasive placentation, preterm birth, hemolytic disease of the newborn, placental insufficiency, hydrops fetalis, fetal malformation, hemolysis, elevated liver enzymes and low platelet count (HELLP) or systemic lupus erythematosus. nzoAnn / zznz / E / YiAi SIZE AND END ANALYSIS FOR DISORDERS ASSOCIATED WITH PREGNANCY Size and / or end analysis of long DNA molecules was used to determine the probability of preeclampsia. Such methods could also be applied to other disorders associated with pregnancy. DNA extracted from maternal plasma samples from four pregnant women diagnosed with preeclampsia was subjected to single-molecule real-time (SMRT) sequencing (PacBio). FIG 62 is a table showing clinical information of four cases of preeclampsia. The first column shows the case number. The second column shows the gestational age in weeks at the time the blood sample was not taken. The third column shows the sex of the fetus. The fourth column shows clinical information about preeclampsia (PET). M12804 was a case of severe preeclampsia (PET) and preexisting IgA nephropathy. M12873 was a case of chronic hypertension with mild superimposed PET. M12876 was a case of late-onset severe PET. M12903 was a case of late-onset severe PET with intrauterine growth restriction (IUGR). Five normotensive third trimester maternal plasma samples were used as controls for later analyzes in this description. For the four preeclamptic and five normotensive third trimester maternal plasma DNA samples analyzed for this description, DNA extracted from their paired maternal placenta and buffy coat samples was genotyped with the Infinium Omni2.5Exome-8 Beadchip on the Sean system ( lllumina). The plasma DNA concentration of each sample was quantified using the high-sensitivity Qubit dsDNA assay with a Qubit fluorometer (ThermoFisher Scientific). The mean plasma DNA concentrations for preeclampsia and third trimester cases were 95.4 ng / ml (range, 52.1 -153.8 ng / ml) plasma and 10.7 ng / ml (6.4 -19.1 ng / ml) plasma, respectively. The mean plasma DNA concentration of preeclampsia cases was about 9 times higher than that of third trimester cases. The mean fetal DNA fractions, which were determined from sequencing data of DNA molecules <600 bp covering informative single nucleotide polymorphisms (SNPs) for which the mother was homozygous and the fetus was heterozygous, were of 22.6% (range, 16.6 - 25.7%) and 20.0% (range, 15.6 - 26.7%) for third trimester maternal plasma samples with preeclampsia and normotension, respectively. S. Size analysis Size analyzes were performed on third trimester maternal plasma samples with preeclampsia and normotension according to the modalities of this description. FIGs 63A-63D and FIGs 64A-64D show the size distributions of DNA molecules in plasma of third trimester cases with preeclampsia and normotension. The x axis shows the size. The y-axis shows the frequency. The size distribution is represented in the range of FIGS. 63A-63D are 0 to 1 kb on a linear scale for the x-axis, and for FIGS. 64A-64D, 0 to 5 kb on a logarithmic scale for the x-axis. . FIG 63A and 64A show sample M12804. FIG 63B and 64B show sample M12873. FIG 63C and 64C show sample M12876. FIGs 63D and 64D show sample M12903. The blue line represents the size distribution of all sequenced plasma DNA molecules pooled from five third trimester normotensive cases. The red line represents the size distribution of plasma DNA ηζοΑηη / ζζηζ / Ε / γίΛΐ molecules sequenced from an individual case of preeclampsia. In FIG 63A-63D, the blue line is the line of the shortest peak below 200 bp and the line of the highest peak between 300 and 400 bp. In FIGs 64A-64D, the blue line corresponds to the line larger than 1 kb. Overall, the plasma DNA size profiles of preeclampsia patients were shorter than those of normotensive pregnant women in the third trimester with a greater peak height of 166 bp and a greater proportion of DNA molecules less than 166 bp (FIG 63A-63D). These changes were more pronounced in the two severe preeclampsia cases M12876 and M12903. The changes were even more dramatic in the M12903 preeclampsia case with intrauterine growth restriction (IUGR). Three of the four preeclamptic plasma samples showed reduced proportions of long plasma DNA molecules with sizes of 200 to 5000 bp (FIG 64B-64D). The proportions of plasma long DNA molecules >500 bp in M12873, M12876, and M12903 were 11.7%, 8.9%, and 4.5%, respectively, while the proportion of plasma long DNA molecules in the combined sequencing data of five normotensive cases in the third trimester was 32.3%. Plasma sample from severe preeclampsia case (PET) with pre-existing IgA nephropathy (M12804) showed a reduced proportion of shorter DNA molecules less than 2000 bp but a higher proportion of longer DNA molecules greater than 2000 bp compared with the pooled sequencing data of five third trimester normotensive cases (FIG 2A). The proportion of long DNA molecules in plasma in M12804 was 34.9%. FIGS. 65A-65D and FIGS. 66A-66D show the size distribution of DNA molecules covering fetus-specific alleles from third trimester maternal plasma samples with preeclampsia and normotension. Each of Figures A to D shows a different sample of preeclampsia. The x axis shows the size. The y-axis shows the frequency in FIG 65A-65D and the cumulative frequency in FIG 66A-66D. In FIGs 66A-66D, the size ranges from 0 to 35 kb. The blue line in each graph represents the size distribution of all sequenced plasma DNA molecules covering the fetus-specific alleles pooled from five third trimester normotensive cases. The red line in each graph represents the size distribution of sequenced plasma DNA molecules covering the fetus-specific alleles of an individual case of preeclampsia. In FIG 65A-65D, the blue line is the line of the shortest peak below 200 bp and the line of the highest peak between 300 and 400 bp. In FIG 66A-66D, the blue line corresponds to the line that is lower by 100 to 1000 bp. FIGS. 67A-67D and FIGS. 68A-68D show the size distribution of DNA molecules covering fetus-specific alleles from third trimester maternal plasma samples with preeclampsia and normotension. Each of Figures A to D shows a different sample of preeclampsia. The x axis shows the size. The y-axis shows the frequency in FIG 67A-67D and the cumulative frequency in FIG 68A-68D. In FIGs 68A-68D, the size ranges from 0 to 35 kb. The blue line in each graph represents the size distribution of all sequenced plasma DNA molecules covering maternally specific alleles pooled from five third trimester normotensive cases. The red line in each graph represents the size distribution of sequenced plasma DNA molecules that ηζοΑηη / ζζηζ / Ε / γίΛΐ cover the mother-specific alleles of an individual case of preeclampsia. In FIG 67A, the blue line is the line of the highest peak below 200 bp and the highest peak between 300 and 400 bp. In FIG 67B-67D, the blue line is the shortest peak line below 200 bp. In FIG 68A, the blue line corresponds to the line that is higher between 1000 and 10000 bp. In FIG 68B-68D, the blue line corresponds to the line that is lower by 100 to 1000 bp. The phenomenon of plasma DNA shortening was observed both in the DNA molecules covering the fetus-specific alleles (FIG 65B-65D and FIG 66B-66D) and in those covering the mother-specific alleles (FIG 67B67D and FIG 68B- 68D) in three of the four preeclamptic plasma samples compared to third trimester normotensive maternal plasma samples. The exception was case M12804 of severe PET with pre-existing IgA nephropathy which showed a higher proportion of shorter DNA molecules less than 1 kb and a reduced proportion of longer DNA molecules greater than 1 kb among DNA molecules in plasma that cover the specific alleles of the fetus (FIG 65A and 66A). Indeed, plasma DNA molecules covering the mother-specific alleles in case M12804 showed an elongated size profile (FIG 67A and 68A). FIGS. 69A and 69B are graphs of the proportion of short DNA molecules covering (A) fetal-specific alleles and (B) maternal-specific alleles, in preeclampsia and normotension maternal plasma samples sequenced with PacBio SMRT sequencing. The y-axis shows the proportion of short DNA fragments <150 bp. The x-axis shows the normal and PET samples. In embodiments, the proportion of short DNA molecules was defined as the percentage of maternal plasma DNA molecules with a size less than 150 bp. M12804 was excluded from this analysis because this case had preexisting IgA nephropathy, but other samples did not. The preeclamptic plasma sample group showed significantly higher proportions of short DNA molecules covering fetus-specific alleles (P = 0.036, Wilcoxon rank sum test) and maternal-specific alleles (P = 0.036, Wilcoxon rank sum test). Wilcoxon ranges), compared with the group of normotensive control plasma samples. FIG 70A and 70B are graphs of the proportion of short DNA molecules in preeclampsia and normotension maternal plasma samples sequenced with (A) PacBio SMRT sequencing and (B) lllumina sequencing. The y-axis shows the proportion of short DNA fragments <150 bp. In embodiments, the proportion of short DNA molecules was defined as the percentage of maternal plasma DNA molecules with a size less than 150 bp. M12804 was removed from this analysis because this case showed a different size profile compared to other preeclampsia cases in this cohort, likely due to preexisting IgA nephropathy in this case. The preeclamptic plasma sample group showed significantly higher proportions of short DNA molecules (median: 28.0%; range: 25.8 - 35.1%) compared to the normotensive control plasma sample group (median: 12.1%; range: 8.5 -15.8%) (P = 0.036, Wilcoxon rank sum test). In contrast, in a previous cohort of four preeclamptic and four normotensive maternal plasma DNA samples of the same gestational age who underwent bisulfite conversion and lllumina sequencing, the proportions of short DNA molecules in preeclamptic plasma and plasma samples control were not significantly different (P = 0.340, Wilcoxon rank sum test) (FIG 70B). ηζοΑηη / ζζηζ / Ε / γίΛΐ In some embodiments, a cutoff of 20% for the proportion of short DNA molecules in a maternal plasma sample sequenced with PacBio SMRT sequencing could be used to determine whether a pregnancy was at high or low risk of developing preeclampsia. A maternal plasma sample with a proportion of short DNA molecules greater than 20% would be determined to be at high risk of developing preeclampsia, while a maternal plasma sample with a proportion of short DNA molecules less than 20% would be determined to be at high risk for developing preeclampsia. You have a low risk of developing preeclampsia. Using this cutoff, both sensitivity and specificity were 100%. In some other embodiments, the limit for the proportion of short DNA molecules used could include, but is not limited to, 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%. , 50%, 55%, 60%, etc. In another embodiment, the proportion of short DNA molecules in a maternal plasma sample would be used to monitor and evaluate the severity of preeclampsia during pregnancy. In embodiments, a size ratio indicating the relative proportions of short and long DNA molecules was calculated for each sample using the following equation. P(5O - 150) Size ratio = ——— --——— P(200 - 1000) where P(50 - 150) denotes the proportion of plasma DNA molecules sequenced with sizes ranging from 50 bp to 150 bp; and P(200 -1000) denotes the proportion of plasma DNA molecules sequenced with sizes ranging from 200 bp to 1000 bp. FIG 71 is a graph of size ratios indicating the relative proportions of short and long DNA molecules, in preeclamptic and normotensive maternal plasma samples sequenced with PacBio SMRT sequencing. The y-axis shows the size relationship. The x-axis shows normal and PET samples. The preeclamptic plasma sample group showed a significantly larger size ratio compared with the normotensive control plasma sample group (P = 0.016, Wilcoxon rank sum test). In embodiments, size profiles generated from long read sequencing platforms including, but not limited to, PacBio SMRT sequencing and Oxford Nanopore sequencing can be used to predict the development and severity of preeclampsia in pregnancies. In some embodiments, the progression of preeclampsia and the development of severe preeclamptic features including, but not limited to, liver and kidney abnormalities can be monitored by analyzing the size profiles of plasma DNA molecules. In some embodiments, the size parameters used in the analysis could include, but are not limited to, the proportion of short or long DNA molecules, and the size ratio indicating the relative proportions of short and long DNA molecules. The cutoff used to determine short and long DNA categories could include, but is not limited to, 150 bp, 180 bp, 200 bp, 250 bp, 300 bp, 350 bp, 400 bp, 450 bp, 500 bp, 550 bp, 600bp, 650bp, 700bp, 750bp, 800bp, 850bp, 900bp, 950bp, 1kb, etc. Size ranges used to determine the size ratio of short to long molecules could include, but are not limited to, 50-150 bp, 50-166 bp, 50-200 bp, 200-400 bp, 200-1000 bp, 200 - 5000 bp or other combinations. Final size analysis may include using the method described with method 6100 in FIG 61. nzoAnn / zznz / E / YiAi T. Analysis of fragment ends Fragment end analyzes were performed on preeclamptic and normotensive third trimester plasma samples according to the embodiments of this disclosure. The first nucleotide at the 5' end of the Watson and Crick strands was determined for each plasma DNA molecule sequenced. The proportions of T-terminal, C-terminal, A-terminal, and G-terminal fragments were determined for each plasma DNA sample. FIG 72A-72D show the proportion of different ends of plasma DNA molecules in preeclampsia and normotension maternal plasma samples sequenced with PacBio SMRT sequencing. The x-axis shows normal third trimester and PET samples. The y-axis shows the ratio of a given endpoint. FIG 72A shows the T-end ratio. FIG 72B shows the C-end ratio. FIG 72C shows the A-end ratio. FIG 72D shows the G-end ratio. The preeclamptic plasma sample group showed significantly increased proportions of T-terminal plasma DNA molecules (P = 0.016, Wilcoxon rank sum test) and significantly reduced proportions of G-terminal plasma DNA molecules (P = 0.016, Wilcoxon rank sum test) in comparison to the group of normotensive control plasma samples. FIG 73 shows the hierarchical clustering analysis of third trimester preeclampsia and normotension maternal plasma DNA samples using the four types of fragment ends (first nucleotide at the 5' end of each strand), namely, C-terminus, G end, T end and A end. Each column indicates a plasma DNA sample. The first row indicates which group each sample belonged to; cyan indicates a DNA sample in normotensive third trimester maternal plasma and orange indicates a DNA sample in preeclamptic plasma. Cyan covers the first five columns. Orange covers the last four columns. Starting with the second row, each row indicates a type of fragment endpoint. Final motif frequencies were presented with a series of color gradients according to the row-normalized (z-score) frequencies (i.e., the number of standard deviations below or above the mean frequency across the samples). The redder color indicates a higher frequency of an end motif, while the bluer color indicates a lower frequency of an end motif. Hierarchical clustering analysis based on the frequencies of the 4 types of fragment ends showed that the fragment end profiles of the preeclamptic plasma DNA samples formed a distinct cluster from that of the normotensive third trimester plasma DNA samples. In embodiments, the dinucleotide sequence of the first (X) and second nucleotide (Y) of the 5' end of the Watson and Crick strands can be determined separately for each DNA molecule sequenced. X and Y can be one of the four nucleotide bases in DNA. There are 16 possible XYNN two-nucleotide end motifs, namely AANN, ATNN, AGNN, ACNN, TANN, TTNN, TGNN, TCNN, GANN, GTNN, GGNN, GCNN, CANN, CTNN, CGNN, and CCNN. The dinucleotide sequence of the third (X) and fourth nucleotide (Y) of the 5' end of the Watson and Crick strands can be determined separately for each DNA molecule sequenced in accordance with the embodiment of this disclosure. There are 16 possible two-nucleotide NNXY motifs. The first four-nucleotide sequence (a 4-mer motif) at the 5' end of the Watson and Crick strands can also be determined separately for each DNA molecule sequenced. nzoAnn / zznz / E / YiAi FIG 74 shows a hierarchical clustering analysis of maternal plasma DNA samples with preeclampsia and normotension in the third trimester using 16 two-nucleotide XYNN motifs (dinucleotide sequence of the first and second nucleotide of the 5' end). FIG 75 shows hierarchical clustering analysis of maternal plasma DNA samples with preeclampsia and normotension in the third trimester using 16 two-nucleotide NNXY motifs (dinucleotide sequence of the third and fourth nucleotides of the 5' end). FIG 76 shows hierarchical clustering analysis of maternal plasma DNA samples with preeclampsia and normotension in the third trimester using 256 four-nucleotide motifs (dinucleotide sequence from the first to the fourth nucleotide from the 5' end). In FIGs 74-76, the first row indicates which group each sample belonged to; cyan indicates a DNA sample in normotensive third trimester maternal plasma and orange indicates a DNA sample in preeclamptic plasma. The clan covers the first five columns. Orange covers the last four columns. Starting with the second row, each row indicates a type of fragment endpoint. Final motif frequencies were presented with a series of color gradients according to the row-normalized (z-score) frequencies (i.e., the number of standard deviations below or above the mean frequency across the samples). The redder color indicates a higher frequency of an end motif, while the bluer color indicates a lower frequency of an end motif. These results suggested that plasma DNA in preeclamptic and non-preeclamptic samples possessed different fragmentation properties. In one embodiment, end motif profiles generated from long read sequencing platforms including, but not limited to, PacBio SMRT sequencing and Oxford Nanopore sequencing could be used to predict the development of preeclampsia in pregnancies. Although one nucleotide, two nucleotide and four nucleotide motifs were used in the above analysis, in other embodiments motifs of other lengths may be used, for example, 3.5, 6, 7, 8, 9,10 or more. In some embodiments, fragment end analysis and tissue of origin analysis may be combined to improve the performance of predicting, detecting and monitoring pregnancy-associated conditions including, but not limited to, preeclampsia. First, fragment end analysis could be performed for each maternal plasma sample to separate plasma DNA molecules into four categories of fragment ends, namely, T-end, C-end, A-end, and T-end fragments. G. Analysis of the tissue of origin can then be performed separately using plasma DNA molecules from each of the fragment end categories for each maternal plasma DNA sample using methylation status matching analysis according to with the modalities of this description. The proportional contribution of different tissues among one of the fragment end categories was defined as the percentage of plasma DNA molecules in the corresponding fragment end category that were assigned to the corresponding tissue relative to other tissues. We analyzed three and five plasma DNA samples from pregnant women with and without preeclampsia using single-molecule real-time sequencing. We obtained a median of 658,722, 889,900, 851,501, and 607,554 A-terminal, C-terminal, G-terminal, and T-terminal plasma fragments. For A-terminal fragments, we compared the methylation patterns of any fragment with at least 10 CpG sites. with baseline methylation profiles of neutrophils, T cells, B cells, liver and placenta according to the methylation status comparison approach described in this description. A plasma DNA fragment would be assigned to a tissue that corresponded to nzQRnn / zznz / B / YiAi the maximum methylation status match scores between those tissues. Using this method, a median of 2.43% (range: 0.73 - 5.50%) of A-terminal fragments was assigned to T cells (i.e., T cell contribution) among all samples analyzed. We further analyzed those fragments with C-terminus, G-terminus and T-terminus, respectively, in a similar manner. A median T cell contribution of 3.20% (range: 1.55 - 5.19%), 3.52% (range: 1.53 - 6.27%), and 2.22% (0 - 7.79%) was observed for those fragments with C-terminus, G-terminus, and T end, respectively. FIGS. 77A-77D show the contribution of T cells among DNA molecules belonging to different categories of fragment ends, namely, (A) T-end, (B) C-end, (C) A-end, and (D) ) G end, in DNA samples in maternal plasma with preeclampsia and normotension. The x-axis shows normal third trimester and PET samples. The y-axis shows the contribution of T cells as a percentage. The results showed that among G-terminal fragments, the contribution of T cells was significantly reduced in preeclampsia plasma samples compared with normotensive third trimester plasma samples (P = 0.036, rank sum test). Wilcoxon). In embodiments, a cutoff of 3% for the T cell contribution among all G-terminal fragments in a maternal plasma DNA sample can be used to determine whether a pregnancy was at high or low risk for developing preeclampsia. U. Example methods FIG 78 shows a method 7800 of analyzing a biological sample obtained from a woman pregnant with a fetus. The biological sample may include a plurality of circulating extracellular DNA molecules from the fetus and the woman. The method can generate a classification of a probability of a disorder associated with pregnancy. The pregnancy-associated disorder may be preeclampsia or any pregnancy-associated disorder described herein. Sequence readings corresponding to the plurality of circulating extracellular DNA molecules can be received. At block 7810, the sizes of the plurality of circulating extracellular DNA molecules can be measured. Sizes can be measured through alignment or counting the number of nucleotides or any technique described herein, included with FIG 21. In block 7820, a set of circulating extracellular DNA molecules can be identified that have sizes greater than a threshold value. The cutoff value can be any cutoff value for long fragments of circulating extracellular DNA, including 500 nt, 600 nt, 700 nt, 800 nt, 900 nt, 1 knt, 1.1 knt, 1.2 knt, 1.3 knt, 1.4 knt, 1.5 knt , 1.6 knt, 1.7 knt, 1.8 knt, 1.9 knt, or 2 knt. The cutoff value may be any cutoff value described herein for circulating extracellular long DNA molecules. In block 7830, a value of a final reason parameter may be generated using a first quantity. The first amount of circulating extracellular DNA molecules in the pool that has a first subsequence at one or more ends of the circulating extracellular DNA molecules in the pool can be measured. In some embodiments, the final motif parameter may be the first quantity normalized by the total quantity of all subsequences at an extreme ηζοΑηη / ζζηζ / Ε / γίΛΐ. In some embodiments, the end may be the 3' end. In some embodiments, the end may be the 5' end. The first subsequence may be 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, or more nucleotides in length. The first subsequence may include the last nucleotide at the end of the respective circulating extracellular DNA molecule. For example, the first subsequence may be the XYNN pattern shown in FIG 74. In some embodiments, the first subsequence may not include the last nucleotide or nucleotides at the end of the respective circulating extracellular DNA molecule. For example, the first subsequence may include the pattern NNXY of FIG 75. A second quantity of circulating extracellular DNA molecules having a subsequence different from the first subsequence at one or more ends of the circulating extracellular DNA molecules can be measured. The value of the final reason parameter can be generated using a relationship of the second quantity and the third quantity. For example, the second quantity can be divided by the third quantity or the third quantity can be divided by the second quantity. At block 7840, the value of the final reason parameter may be compared to a threshold value. The threshold value may be a value that represents a statistically significant difference from a value of the associated parameter for a subject without the pregnancy-associated disorder. The threshold value may be determined from one or more reference subjects with normal pregnancies or one or more reference subjects with pregnancy-associated disorders. In some embodiments, the value of the final motif parameter may be compared to the threshold value, and the value of a second final motif parameter may be compared to a second threshold value. A second quantity of circulating extracellular DNA molecules having a second subsequence different from the first subsequence at one or more ends of the circulating extracellular DNA molecules can be measured. Therefore, quantities of different final motifs can be determined. A value of the second final reason parameter can be generated using the second quantity. The value of the second final reason parameter can be compared with a second threshold value. The second threshold value may be the same as or different from the first threshold value. Additional subsequences may be used in the same manner as the first and second subsequences. In some embodiments, all possible subsequences can be used for comparisons with threshold values. At block 7850, a probability rating of a disorder associated with pregnancy may be determined using the comparison. Pregnancy-associated disorder may be probable when the value of the size parameter or the value of the final reason parameter exceeds the threshold value. In some embodiments, determining the probability classification of a pregnancy-associated disorder may use comparison of the value of the second final reason parameter with the second cutoff value. Pregnancy-associated disorder may be probable when the value of the first final reason parameter exceeds the first threshold value and the value of the second final reason parameter exceeds the second threshold value. The method may include using a size parameter in addition to the final pattern parameter. A second set of circulating extracellular DNA molecules can be identified that have sizes in a first size range. The first size range may include sizes larger than the limit value. The first size range includes sizes that may be larger than the limit value. The first size range can be less than 550 nt, 600 nt, 650 nt, 700 nt, 750 nt, 800 nt, 850 nt, 900 nt, 950 nt, 1 nt, 1.5 knt, 2 knt, 3 knt, 5 knt, or more. A size parameter value ηζοΑηη / ζζηζ / Ε / γίΛΐ can be generated using a second amount of circulating extracellular DNA molecules in the second set. The value of the size parameter can be compared to a second threshold value. Determining the classification of the probability of the disorder associated with pregnancy can use the comparison of the value of the size parameter with the second threshold value. The classification is likely to have pregnancy-associated disorder when one or both of the first and second threshold values ​​are exceeded. The size parameter may be a normalized parameter. For example, a third quantity of circulating extracellular DNA molecules can be measured in a second size range. The second size range may include sizes smaller than the first limit value. The second size range can include all sizes. The second size range may include 50 - 150 nt, 50 - 166 nt, 50 - 200 nt, 200 - 400 nt. The second size range may include any size for short circulating extracellular DNA fragments described herein. The second size range may exclude sizes from the first size range. The value of the size parameter can be generated by determining a ratio of the second quantity and the third quantity. For example, the second quantity can be divided by the third quantity or the third quantity can be divided by the second quantity. Any number of circulating extracellular DNA molecules may be circulating extracellular DNA molecules from a particular tissue of origin. For example, the tissue of origin may be T cells or other tissue of origin described herein. The second amount may be similar to the T cell contribution described with FIGS. 77A77D. The contribution of the tissue of origin can be determined using the methylation status or pattern as described in this description. DISEASES RELATED TO REPEATING EXPANSION Long fragments of circulating extracellular DNA obtained from pregnant women can be used to identify repeat expansion in genes. Repeat expansion in genes can result in neuromuscular diseases. Expansions in tandem repeats have been associated with human diseases, including, but not limited to, neurodegenerative disorders such as fragile X syndrome, Huntington's disease, and spinocerebellar ataxia. These tandem repeat expansions can occur in protein-coding regions of genes (Machado-Joseph disease, Haw River syndrome, Huntington's disease) or non-coding regions (Friedrich's ataxia, myotonic dystrophy, some forms of fragile X syndrome). . Expansions involving minisatellites, pentanucleotides, tetranucleotides, and numerous trinucleotide repeats have been associated with fragile sites. The expansions associated with these diseases could be caused by replication slippage or asymmetric recombination or epigenetic aberrations. The number of repetitions in the sequence refers to the total number of times a subsequence appears. For example, CAGCAG includes two repetitions. Since repetitions include at least two instances of a subsequence, the number of repetitions cannot be 1. The subsequence can be understood as the unit of repetition. In embodiments, analysis of long circulating extracellular DNA in pregnant women could facilitate the detection of repeat-associated diseases. For example, a trinucleotide repeat represents a repetitive stretch of 3-bp motifs in DNA sequences. An example is that the sequence ‘CAGCAGCAG’ comprises three 3 bp ηζοΑηη / ζζηζ / Ε / γίΛΐ ‘CAG’ motifs. Microsatellite expansion, typically trinucleotide repeat expansion, has been reported to play a crucial role in neurological disorders (Kovtun et al. Cell Res. 2008;18:198-213; McMurray et al. Nat Rev Genet. 2010 ;11:786-99). An example is that more than 55 CAG repeats (165 bp total) in the ATXN3 gene are pathogenic, resulting in the disease spinocerebellar ataxia type 3 (SCA3) characterized by progressive problems with movement. This condition is inherited in an autosomal dominant pattern. Therefore, one copy of the altered gene is enough to cause the disorder. To determine the number of microsatellite repeats, polymerase chain reaction (PCR) is typically used to amplify the genomic region of interest and then the PCR product is subjected to a number of different techniques, such as capillary electrophoresis ( Lyon et al. J Mol Diagn. 2010;12:505-11), Southern blot analysis (Hsiao et al. J Clin Lab Anal. 1999; 13:188-93), melting curve analysis (Lim et al . J Mol Diagn. 2014;17:302-14) and mass spectrometry (Zhang etal. Anal Methods. 2016;8:503944). However, these methods were labor-intensive and time-consuming and difficult to apply to high-throughput screening in real clinical practice, such as prenatal testing. Sanger sequencing has substantial difficulty in inferring long repeats from complicated sequence traces through manual examination. The lllumina and Ion Torrent sequencing technologies are known to have considerable difficulty sequencing GC-rich (or GC-poor) regions harboring such repeats (Ashely et al. 2016; 17:507-22) and the length of a DNA comprising the expanded repeats easily exceeds the length of the sequence reads (Loomis et al. Genome Res. 2013;23:121-8). Another example is myotonic dystrophy which is caused by CTG repeat expansion, ranging from 50 to 4000 CTG repeats, near the DMPK gene and also an autosomal dominant disorder. Molecular diagnosis of DM is routinely performed in prenatal diagnosis by analyzing the number of CTGs in fetal genomic DNA invasively. In contrast to short read sequencing (hundreds of bases), the methods described herein can obtain long DNA molecules from maternal plasma DNA (a number of kilobases). Using the methods described in this disclosure, it could be determined whether an unborn fetus inherits this disease from the affected mother in a non-invasive manner. FIG 79 shows an illustration of the deduction of maternal inheritance from the fetus for diseases associated with repetition. At stage 7905, extracellular DNA circulating in pregnancy was subjected to single-molecule real-time sequencing (e.g., PacBio SMRT). At step 7910, the sequenced results were divided into long and short DNA categories according to the description. At stage 7915, allelic information present in long DNA molecules could be used to construct maternal haplotypes, namely Hap I and Hap II. Hap I and Hap II may each include expanded repeats of a trinucleotide subsequence (e.g., CTG). At step 7920, a haplotype imbalance, similar to that described with FIG 16, can be analyzed. At step 7925, maternal inheritance of the fetus can be deduced. The methods described herein allow us to not only determine the haplotypes (e.g., Hap I and Hap II), but also determine which haplotype harbors the expanded repeats (e.g., affected Hap I) that cause the disorder using sequence information. of long DNA molecules according to the description. Using the counts, sizes, or methylation states of short DNA molecules that distribute ηζοΑηη / ζζηζ / Ε / γίΛΐ across maternal Hap I and Hap II according to the method described herein, it could be determined whether a fetus inherits maternal Hap I (affected) or Hap II (unaffected) in this example. FIG 80 shows an illustration of the deduction of paternal inheritance from the fetus for diseases associated with repetition. Whether a fetus inherits an affected paternal haplotype could be determined using extracellular DNA circulating during pregnancy. As shown in FIG 80, circulating extracellular DNA in the pregnancy of an unaffected woman (e.g., 5 CTG repeats for Hap I and 6 CTG repeats for Hap II) whose husband was affected by repeat expansion disease ( e.g., 70 CTG repeats) was subjected to PacBio SMRT sequencing, the sequenced long DNA molecules were identified and used to determine the haplotype and number of repeats. If a haplotype harboring a long stretch of CTG repeats (e.g., 70 CTG repeats in this example) is present in the maternal plasma of the unaffected pregnant woman, it suggests that the fetus inherited an affected paternal haplotype. In some embodiments, the DNA containing the expanded repeats also carries one or more specific paternal alleles that are absent in the maternal genome. This situation would be useful to confirm paternal inheritance. In another embodiment, whether a fetus inherits an affected paternal haplotype could be determined using extracellular DNA circulating during pregnancy. As shown in FIG 80, circulating extracellular DNA in the pregnancy of an unaffected woman (e.g., 5 CTG repeats for Hap I and 6 CTG repeats for Hap II) whose husband was affected by repeat expansion disease ( e.g., 70 CTG repeats) was subjected to PacBio SMRT sequencing, the sequenced long DNA molecules were identified and used to determine the haplotype and number of repeats. If a haplotype harboring a long stretch of CTG repeats (e.g., 70 CTG repeats in this example) is present in the maternal plasma of the unaffected pregnant woman, it suggests that the fetus inherited an affected paternal haplotype. In some embodiments, the DNA containing the expanded repeats also carries one or more specific paternal alleles that are absent in the maternal genome. This situation would be useful to confirm paternal inheritance. FIGs 81, 82 and 83 are tables showing examples of repeat expansion diseases. The first column shows the disease related repeat expansion. The second column shows the repeated subsequence. The third column shows the number of repetitions in normal subjects. The fourth column shows the number of repetitions in sick subjects. The fifth column shows the genetic locations related to the repeats. The sixth column lists the gene names. The seventh column lists the inheritance patterns. The table is derived from omicslab.genetics.ac.cn / dred / index.php. 1 / . Examples of repeat expansion detection It was reported that the paternally inherited expanded CAG repeat could be detected in maternal plasma using a direct approach using PCR and subsequent fragment analysis on the 3130XL genetic analyzer (Oever et al. Prenat Diagn. 2015;35:945-9). Non-invasive prenatal testing for Huntington's could be achieved by PCR because the size of the expanded allele only starts from >35 trinucleotide repeats [i.e., a region of DNA with 105 bp (35 x 3) or more in length spanning repetitions]. Many expanded repeats, especially for most trinucleotide repeat disorders (Orr et al. Annu. Rev. Neurosci. 2007;30:575-621), would involve repeats with 300 bp or more of length, beyond the size of the short fetal DNA molecules that were documented in previous reports. DNA with large expanded repeats would cause PCR difficulty (Orr et al. Annu. Rev. Neurosci. 2007;30:575-621). As the study by Oever et al. suggests, the signal intensity of long CAG repeats is usually much lower compared to the signal of smaller repeats, and this phenomenon is observed in both genomic DNA and DNA in plasma, leading to lower sensitivity to detect those long CAG repeats (Oever et al. Prenat Diagn. 2015;35:945-9). Another limitation of PCR would be that methylation signals cannot be preserved during amplification. In one embodiment, single-molecule real-time sequencing of long DNA molecules would allow the determination of tandem repeat polymorphisms and their associated methylation levels in one or more regions. FIG 84 is a table showing examples for the detection of repeat expansion in the fetus and the determination of methylation associated with the repeat. The first column shows the type of repeat in number of base pairs. The second column shows the repeat unit. The third column shows the genomic locations. The fourth column shows the reference bases, the sequences present in the reference human genome. The fifth column shows the paternal genotypes. The sixth column shows the maternal genotypes. The seventh column shows the fetal genotypes. The eighth column shows the level of fetal DNA methylation linked to the paternal alleles. The ninth column shows the level of fetal DNA methylation linked to the maternal alleles. FIG 84 shows a number of examples of tandem repeats of 1 bp, 2 bp, 3 bp and 4 bp. For example, at the genomic location of chr3:192384705-192384706, a GATA tandem repeat was identified. The paternal genotype at this locus was T(GATA)3 / T(GATA)s for which allele 1 had 3 repeat units and allele 2 had 5 repeat units. Compared to the reference allele T(GATA)3, the paternal allele 2 suggested a genetic event related to repeat expansion. The mother's genotype at this locus was T / T, exhibiting a genetic event involving repeat contraction. The fetal genotype at this locus was T(GATA)s / T, suggesting that the fetus inherited the paternal allele 2 (i.e., T(GATA)s) and the maternal allele T. The methylation levels associated with the allele paternal and maternal allele were 50.98 and 62.8, respectively. These results suggested that the use of tandem repeat polymorphisms would allow the determination of maternal and paternal inheritance of the fetus. This technology would allow the identification of different methylation patterns associated with the two alleles. Another example shows that at the genomic location of chr4:7323715773237158, the fetus had inherited the repeat expansion [(TAAAjJ from the mother. The fetal molecule containing the repeat expansion inherited from the mother showed a higher level of methylation (95.65% ) compared to the fetal molecule containing the paternal allele (62.84%). These data suggested that we could detect repeats, repeat structures, and associated methylation changes. In one embodiment, a particular cutoff could be used to determine whether the difference in methylation between maternal and paternal inheritance was significant. The limit would be the absolute difference in methylation levels greater than, but not limited to, 5%, 10%, 15%, 20%, 25%, 30%, 35%. 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85% or 90%, etc. Determination of maternal inheritance may be similar to the methods described with method 2100 of FIG 21. ηζοΑηη / ζζηζ / Ε / γίΛΐ H / . Example methods Subsequence repeats can be used to determine information about a fetus. For example, the presence of subsequence repeats can be used to determine that a molecule is of fetal origin. Additionally, subsequence repeats may indicate the likelihood of a genetic disorder. Subsequence repeats can be used to determine the inheritance of maternal and / or paternal haplotypes. Additionally, the paternity of a fetus can be determined through subsequence repetitions. 1. Analysis of fetal origin using subsequence repetitions FIG 85 shows method 8500 for analyzing a biological sample obtained from a woman pregnant with a fetus, the biological sample including circulating extracellular DNA molecules from the fetus and the woman. The probability of a genetic disorder in the fetus can be determined. At block 8510, a first sequence read corresponding to a circulating extracellular DNA molecule may be received from the circulating extracellular DNA molecules. Circulating extracellular DNA molecules can have a length greater than a threshold value. The cutoff value may be greater than or equal to 200 nt. The limit value can be at least 500 nt, which includes 600 nt, 700 nt, 800 nt, 900 nt, 1 knt, 1.1 knt, 1.2 knt, 1.3 knt, 1.4 knt, 1.5 knt, 1.6 knt, 1.7 knt, 1.8 knt , 1.9 knt, or 2 knt. The cutoff value may be any cutoff value described herein for circulating extracellular long DNA molecules. At step 8520, the first sequence read may be aligned to a region of a reference genome. The region can be known to potentially include repetitions of a subsequence. The region may correspond to any of the locations or genes of FIGS. 81-83. The subsequence may be a trinucleotide sequence, including any of those described herein. At block 8530, a number of subsequence repeats can be identified in the first sequence read corresponding to the circulating extracellular DNA molecule. At block 8540, the number of repetitions of the subsequence may be compared to a threshold number. The threshold number can be 55, 60, 75,100,150 or more. The threshold number may be different for different genetic disorders. For example, the threshold may reflect the minimum number of repetitions in diseased subjects, the maximum number of repetitions in normal subjects, or a number between these two numbers (see FIGs 81-83). At block 8550, a probability rating that the fetus has the genetic disorder may be determined using comparison of the number of repetitions with the threshold number. It can be determined that the fetus is likely to have the genetic disorder when the number of repeats exceeds the threshold number. The genetic disorder may be fragile X syndrome or any disorder listed in FIGS. 81-83. In some embodiments, the method may include repeating the classification for several different target loci, each of which is known to potentially have a subsequence repeat. A plurality of sequence reads corresponding to circulating extracellular DNA molecules may be received. The plurality of sequence reads may be aligned to a plurality of regions of the reference genome. It can be known that the plurality of regions potentially includes repetitions of subsequences. The plurality of regions may be non-overlapping regions. Each region of a plurality of regions may have a different SNP. The plurality of regions may be from different chromosome arms or chromosomes. The plurality of regions may cover at least 0.01%, 0.1% or 1% of the reference genome. The repeat numbers of the subsequences can be identified in the plurality of sequence reads. The numbers of repetitions of the subsequences can be compared with a plurality of threshold numbers. Each threshold number can indicate the presence or probability of a different genetic disorder. For each of a plurality of genetic disorders, a ranking of the probability that the fetus has the respective genetic disorder can be determined using comparison with a threshold number of the plurality of threshold numbers. It can be determined that the circulating extracellular DNA molecule is of fetal origin. Determination of fetal origin may include receiving a second sequence read corresponding to a circulating extracellular DNA molecule of maternal origin obtained from a buffy coat or a sample from the woman before pregnancy. The second sequence read can be aligned to the reference genome region. A second number of repeats of the subsequence can be identified in the second sequence read. The second number of repetitions can be determined to be less than the first number of repetitions. Determination of fetal origin may include determining a methylation level of the circulating extracellular DNA molecule using the methylated and unmethylated sites of the circulating extracellular DNA molecule. The methylation level can be compared to a reference level. The method may include determining that the methylation level exceeds the reference level. The methylation level can be a number or proportion of sites that are methylated. Determination of fetal origin may include determining a methylation pattern of a plurality of sites on the circulating extracellular molecule. A similarity score can be determined by comparing the methylation pattern to a reference standard from maternal or fetal tissue. The similarity score can be compared to one or more threshold values. The similarity score may be any similarity score described herein, including, for example, that described with method 4000. 2. Paternity analysis using subsequence repetitions FIG 86 shows a method 8600 for analyzing a biological sample obtained from a woman pregnant with a fetus, the biological sample including circulating extracellular DNA molecules from the fetus and the woman. The biological sample can be analyzed to determine the father of the fetus. At block 8610, a first sequence read corresponding to a circulating extracellular DNA molecule may be received from the circulating extracellular DNA molecules. The method may include determining that the circulating extracellular DNA molecule is of fetal origin. The circulating extracellular DNA molecule may be determined to be of fetal origin by any method described herein, including, for example, as described by method 8500. Circulating extracellular DNA molecules may have sizes greater than a cutoff value. The cutoff value may be greater than or equal to 200 nt. The limit value can be at least 500 nt, which includes 600 nt, 700 nt, 800 nt, 900 nt, 1 knt, 1.1 knt, 1.2 knt, 1.3 knt, 1.4 knt, 1.5 knt, 1.6 knt, 1.7 knt, 1.8 knt , 1.9 knt, or 2 knt. The cutoff value may be any cutoff value described herein for circulating extracellular long DNA molecules. ηζοΑηη / ζζηζ / Ε / γίΛΐ At block 8620, the first sequence read may be aligned to a first region of a reference genome. It can be known that the first region has repetitions of a subsequence. At block 8630, a first number of repeats of a first subsequence can be identified in the first sequence read corresponding to the circulating extracellular DNA molecule. The first subsequence may include an allele. At block 8640, sequence data obtained from a male subject may be analyzed to determine whether a second number of repeats of the first subsequence is present in the first region. The second number of repetitions includes at least two instances of the first subsequence. Sequence data can be obtained by extracting a biological sample from the male subject and performing DNA sequencing on the biological sample. At block 8650, a ranking of a probability that the male subject is the father of the fetus may be determined using the determination of whether the second number of repetitions of the first subsequence is present. The classification may be that the male subject is most likely the father when the second number of repetitions of the first subsequence is determined to be present. The classification may be that the male subject is probably not the father when it is determined that the second number of repetitions of the first subsequence is not present. The method may include comparing the first number of repetitions with the second number of repetitions. Determining the ranking of the probability that the male subject is the father may include using comparison of the first number of repetitions with the second number of repetitions. The classification may be that the male subject is most likely the father when the first number of repetitions is within a threshold value of the second number of repetitions. The threshold value can be within 10%, 20%, 30%, or 40% of the second number of repetitions. The method may include using multiple repeat regions. For example, the circulating extracellular DNA molecule is a first circulating extracellular DNA molecule. The method may include receiving a second sequence read corresponding to a second circulating extracellular DNA molecule from the circulating extracellular DNA molecules. The method may also include aligning the second sequence read with a second region of the reference genome. The method may further include identifying a first number of repeats of a second subsequence in the second sequence read corresponding to the second circulating extracellular DNA molecule. The method may include analyzing the sequence data obtained from the male subject to determine whether a second number of repeats of the second subsequence is present in the second region. Determining the classification of the probability that the male subject is the father of the fetus may further include using the determination of whether the second number of repetitions of the second subsequence is present in the second region. The probability rating may be a higher probability that the male subject is the father of the fetus when repeats are present in both the first region and the second region in the male subject's sequence data. SIZE SELECTION TO ENRICH LONG DNA MOLECULES IN PLASMA In embodiments, DNA molecules with one or more desired size ranges could be physically selected prior to analysis (e.g., single-molecule real-time sequencing). As an example, ηζοΑηη / ζζηζ / Ε / γίΛΐ size selection can be performed using reversible solid phase immobilization technology. In other embodiments, size selection can be performed using electrophoresis (for example, using the Coastal Genomic system or the Pippin size selection system). Our approach is different from previous work that focused predominantly on shorter DNA (Li et al. JAMA 2005;293:843-9), as fetal DNA is known in the art to be shorter than maternal DNA. (Chan et al. Clin Chem 2004; 50: 88-92). The size selection techniques can be applied to any of the methods described herein and for any size described herein. For example, circulating extracellular DNA molecules can be enriched by electrophoresis, magnetic beads, hybridization, immunoprecipitation, amplification, or CRISPR. The resulting spiked sample may have a higher concentration or a higher proportion of fragments of a certain size than the biological sample before spiked. X. Size selection with electrophoresis In embodiments, making use of DNA electrophoretic mobilities depending on DNA sizes, gel electrophoresis-based approaches could be used to select target DNA molecules with desirable size ranges, e.g., but not limited to, >100 bp, >200 bp, >300 bp, >400 bp, >500 bp, >600 bp, >700 bp, >800 bp, >900 bp, >1 kb, >2 kb, >3 kb, >4 kb, >5 kb, >6 kb, >7 kb, >8 kb, >9 kb, >10 kb, >20 kb, >30 kb, >40 kb, >50 kb, >60 kb, >70 kb, >80 kb, >90 kb, >100 kb, >200 kb, or others, even greater than any limits described herein. For example, LightBench (Coastal Genomics), an automated gel electrophoresis system, was used for DNA size selection. In principle, shorter DNA would move faster than longer DNA during gel electrophoresis. We applied this size selection technology to a plasma DNA sample (M13190), with the aim of selecting DNA molecules larger than 500 bp. We use a 3% size selection cassette with an 'In-Channel-Filter' (ICF) collection device and a load buffer with internal size markers for size selection. The DNA libraries were loaded onto the gel and electrophoresis was started. When the target size was reached, the first fraction <500 bp was recovered from ICF. The run was resumed and electrophoresis was allowed to complete to obtain a second fraction of >500 bp. We used single-molecule real-time sequencing (PacBio) to sequence the second fraction with a molecule size of >500 bp. We obtained 1,434 high-quality circular consensus sequences (CCS) (i.e., 1,434 molecules). Among these, 97.9% of the sequenced molecules were longer than 500 bp. Such proportion of DNA molecules larger than 500 bp was much higher than the counterpart without size selection (10.6%). The overall methylation of those molecules was determined to be 75.5%. FIG 87 shows methylation patterns for two DNA molecules in representative plasmas after size selection in (I) Molecule I and (II) Molecule II. Molecule I (chr21:40,881,731 -40,882,812) was 1.1 kb in length and harbored 25 CpG sites. The single-molecule methylation level (i.e., the number of methylated sites divided by the total number of sites) of molecule I was determined to be 72.0% using the approaches described in our previous description (U.S. Application .no. 16 / 995,607). Molecule II (chr12:63,108,065-63,111,674) was 3.6 kb in length and harbored 34 CpG sites. The single-molecule methylation level of ηζοΑηη / ζζηζ / Ε / γίΛΐ molecule II was determined to be 94.1%. He suggested that methylation analysis based on size selection made it possible to efficiently analyze the methylation of long DNA molecules and compare the methylation status between two or more molecules. Y. Size selection with rocks Reversible solid-phase immobilization technology used paramagnetic beads to selectively bind to nucleic acids based on the size of the DNA molecules. Such a bead includes a polystyrene core, magnetite, and a carboxylate-modified polymer coating. DNA molecules would selectively bind to the beads in the presence of polyethylene glycol (PEG) and salt, depending on the concentration of PEG and salt in the reaction. The PEG caused the negatively charged DNA to bind to carboxyl groups on the bead surface, which would collect in the presence of the magnetic field. Molecules with the desired sizes were eluted from the magnetic beads using elution buffers, for example, Tris-HC110 mm, pH 8 buffer, or water. The volumetric ratio of PEG to DNA would determine the sizes of the DNA molecules that could be obtained. The lower the PEG:DNA ratio, the more long molecules will be retained in the beads. 1. Sample processing Peripheral blood samples were collected from two pregnant women in the third trimester in EDTA blood tubes. Peripheral blood samples were collected and centrifuged at 1,600 χ g for 10 min at 4°C. The plasma portion was further centrifuged at 16,000 χ g for 10 min at 4°C to remove residual cells and debris. The buffy coat portion was centrifuged at 5,000 χ g for 5 min at room temperature to remove residual plasma. Placental tissues were collected immediately after delivery. Plasma DNA extractions were performed using the QlAamp circulating nucleic acid kit (Qiagen). DNA extractions from buffy coat and placental tissue were performed with the QlAamp DNA Mini kit (Qiagen). 2. Plasma DNA size selection Postextraction plasma DNA samples were divided into two aliquots. An aliquot from each patient was subjected to size selection with AMPure XP SPRI beads (Beckman Coulter, Inc.). 50 dL of each extracted plasma DNA sample was mixed thoroughly with 25 dL of AMPureXP solution and incubated at room temperature for 5 minutes. The beads were separated from the solution with magnets and washed with 180 dL 80% ethanol. The beads were then resuspended in 50 dL of water and shaken for 1 minute to elute the size-selected DNA from the beads. Subsequently, the beads were removed to obtain the size-selected DNA solution. 3. Identification of single nucleotide polymorphisms Fetal and maternal genomic DNA samples were genotyped with the ¡Sean (lllumina) system. They were called single nucleotide polymorphisms (SNPs). Placental genotypes were compared with those of mothers to identify fetus-specific and maternal-specific alleles. The fetal-specific allele was defined as an allele that was present in the fetal genome but absent in the maternal genome. In one embodiment, those fetus-specific alleles could be determined by analyzing the SNP sites for which the mother was homozygous and the fetus was heterozygous. The maternal-specific allele was defined by an allele that was present in the maternal genome but absent in the fetal genome. In one embodiment, those fetus-specific alleles could be determined by analyzing the SNP sites for which the mother was heterozygous and the fetus was homozygous. 4. Single-molecule real-time sequencing Two size-selected samples, along with their corresponding unselected samples, were subjected to the construction of a single-molecule real-time (SMRT) sequencing template using a SMRTbell Tétemplate Prep Kit 1.0—SPv3 (Pacific Biosciences). DNA was purified with 1.8x AMPure PB beads and the library size was estimated using a TapeStation instrument (Agilent). Polymerase binding conditions and sequencing primer hybridization were calculated using SMRT Link v5.1.0 software (Pacific Biosciences). Briefly, the v3 sequencing primer was hybridized to the sequencing template, and then polymerase was ligated to the templates using a Sequel internal control and ligation 2.1 kit (Pacific Biosciences). Sequencing was performed on a Sequel SMRT Cell 1M v2. Sequencing movies were collected on the Sequel II system for 20 hours using a Sequel II Sequencing Kit 2.1 (Pacific Biosciences). 5. Size analysis FIG 88 is a table of sequencing information for samples with and without size selection. The first column is the sample identifier. The second column lists the sample group, whether or not there is size selection. The third column lists the number of molecules sequenced. The fourth column lists the average subread depths. The fifth column lists the average fragment size. The sixth column shows the proportion of fragments greater than or equal to 500 bp. We analyzed two samples (299 and 300) with and without bead-based size selection. As shown in FIG 88, we obtained 2.5 million and 3.1 million molecules sequenced for samples 299 and 300, respectively, without size selection, using single-molecule real-time sequencing (e.g., PacBio SMRT sequencing). The...

Claims

1. A method for analyzing a biological sample obtained from a pregnant woman from a fetus, the woman having a first haplotype and a second haplotype in a first chromosomal region, the biological sample including a plurality of cell-free DNA molecules from the fetus and the woman, the method comprising: receiving reads corresponding to the plurality of cell-free DNA molecules; measuring sizes of the plurality of cell-free DNA molecules; identifying a first set of cell-free DNA molecules from the plurality of cell-free DNA molecules having sizes greater than or equal to a threshold value, wherein the threshold value is at least 500 nt; determining a first haplotype sequence and a second haplotype sequence using reads corresponding to the first set of cell-free DNA molecules;Align a second set of cell-free DNA molecules from the plurality of cell-free DNA molecules to the sequence of the first haplotype, the second set of cell-free DNA molecules having sizes smaller than the cutoff value; align a third set of cell-free DNA molecules from the plurality of cell-free DNA molecules to the sequence of the second haplotype, the third set of cell-free DNA molecules having sizes smaller than the cutoff value; measure a first value of a parameter using the second set of cell-free DNA molecules; measure a second value of the parameter using the third set of cell-free DNA molecules; compare the first value with the second value; and determine a probability that the fetus will inherit the first haplotype based on the comparison of the first value with the second value.

2. The method of claim 1, wherein the limit value is 600 nt.

3. The method of claim 1, wherein the limit value is 1 knt.

4. The method of claim 1, wherein determining the first haplotype sequence and the second haplotype sequence of the reads corresponding to the first set of cell-free DNA molecules comprises: aligning reads corresponding to the first set of cell-free DNA molecules with a reference genome.

5. The method of claim 1, wherein determining the first haplotype sequence and the second haplotype sequence of the reads corresponding to the first set of cell-free DNA molecules comprises: aligning a first subset of the reads to a second subset of the reads to identify a different allele at a locus in the reads, determining that the first subset of the reads has a first allele at the locus, determining that the second subset of the reads has a second allele at the locus, determining that the first subset of the reads corresponds to the first haplotype, and determining that the second subset of the reads corresponds to the second haplotype.

6. The method of claim 1, wherein the parameter is a cell-free DNA molecule count, a cell-free DNA molecule size profile, or a cell-free DNA molecule methylation level.

7. The method of claim 6, wherein: the parameter is the cell-free DNA molecule count, and the method further comprises: determining that the fetus has a higher probability of inheriting the first haplotype than the second haplotype when the first value is greater than the second value.

8. The method of claim 6, wherein: the parameter is the size profile of cell-free DNA molecules, and the method further comprises: determining that the fetus has a higher probability of inheriting the first haplotype than the second haplotype when the first value is less than the second value, indicating that the second set of cell-free DNA molecules is characterized by a smaller size profile than the third set of cell-free DNA molecules.

9. The method of claim 6, wherein: the parameter is the methylation level of cell-free DNA molecules, and the method further comprises: determining that the fetus has a higher probability of inheriting the first haplotype than the second haplotype when the first value is less than the second value.

10. The method of claim 1, wherein the cutoff value is a first cutoff value, the method further comprising: calculating a separation value using the first value and the second value; comparing the separation value with a second cutoff value; and determining a probability of fetal aneuploidy based on the comparison of the separation value with the second cutoff value.

11. The method of claim 10, wherein: the second limit value is determined from reference samples of pregnant women with euploid fetuses, the second limit value is determined from reference samples of pregnant women with aneuploid fetuses, or the second limit value is calculated assuming an aneuploid fetus.

12. The method according to claim 1, further comprising: identifying a number of repeats of a subsequence in a read of the reads corresponding to the first set of cell-free DNA molecules, wherein: determining the sequence of the first haplotype comprises determining the sequence of the first haplotype comprises the number of repeats of the subsequence. ηζοΑηη / ζζηζ / E / γίΛΐ 106 13. The method of claim 12, wherein: the number of repeats of the subsequence is associated with a repeat-associated disease, and the method further comprises determining a probability that the fetus will inherit the repeat-associated disease.

14. The method of claim 1, wherein: the plurality of cell-free DNA molecules is enriched for sizes greater than or equal to the limiting value relative to the biological sample, wherein more than 20% of the cell-free DNA molecules in the biological sample have sizes greater than 200 nt.

15. The method according to claim 14, further comprising: enrichment for the plurality of cell-free DNA molecules using electrophoresis.

16. The method according to claim 14, further comprising: enrichment for the plurality of cell-free DNA molecules using magnetic beads to selectively bind cell-free DNA molecules based on size.

17. The method according to claim 14, further comprising: enrichment for the plurality of cell-free DNA molecules using hybridization, immunoprecipitation, amplification, or CRISPR.

18. The method of claim 16, wherein the enrichment is for sizes greater than 600 nt, 700 nt, 800 nt, 900 nt, or 1 knt.

19. The method of claim 1, wherein the plurality of cell-free DNA molecules is enriched for a methylation profile with respect to the biological sample, the method further comprising: enrichment for the plurality of cell-free DNA molecules using immunoprecipitation.

20. A non-transient, computer-readable means for analyzing a biological sample obtained from a pregnant woman from a fetus, the woman having a first haplotype and a second haplotype in a first chromosomal region, the biological sample including a plurality of cell-free DNA molecules from the fetus and the woman, comprising a method, the method comprising: receiving reads corresponding to the plurality of cell-free DNA molecules; measuring sizes of the plurality of cell-free DNA molecules; identifying a first set of cell-free DNA molecules from the plurality of cell-free DNA molecules having sizes greater than or equal to a threshold value, wherein the threshold value is at least 500 nt; determining a first haplotype sequence and a second haplotype sequence using reads corresponding to the first set of cell-free DNA molecules;align a second set of cell-free DNA molecules from the plurality of cell-free DNA molecules to the sequence of the first haplotype, the second set of cell-free DNA molecules having sizes smaller than the cutoff value; align a third set of cell-free DNA molecules from the plurality of cell-free DNA molecules to the sequence of the second haplotype, the third set of cell-free DNA molecules having sizes smaller than the cutoff value; measure a first value of a parameter using the second set of cell-free DNA molecules; measure a second value of the parameter using the third set of cell-free DNA molecules; compare the first value with the second value; and determine a probability that the fetus will inherit the first haplotype based on the comparison of the first value with the second value.

21. The method of claim 1, wherein the reads corresponding to the plurality of cell-free DNA molecules are obtained by sequencing a single molecule.

22. The method of claim 21, wherein the sequencing of a single molecule comprises optical monitoring of a DNA polymerase incorporating a new base into a complementary strand of a cell-free DNA molecule from the plurality of cell-free DNA molecules.