Non-invasive prenatal sample preparation and related methods and uses

Abstract

The present disclosure relates to methods for preparing cell-free DNA samples from pregnant mothers or women, and related methods of analysis of such samples.

Description

[Technical field]

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS This application claims the benefit of priority to U.S. Provisional Application No. 63 / 298,593, filed January 11, 2022, and U.S. Provisional Application No. 63 / 357,915, filed July 1, 2022, the entire contents of each application being incorporated herein by reference.

[0002] Described herein are methods for preparing samples from pregnant mothers, and associated methods for the analysis of such samples. [Background technology]

[0003] The following description of the background of the present technology is provided merely as an aid in understanding the present technology and is not admitted to describe or constitute prior art to the present technology.

[0004] Non-invasive prenatal screening (NIPS) has become a routine component of health care for pregnant mothers. NIPS can include both screening for chromosomal aneuploidies (such as Down's syndrome) and screening for other genetic abnormalities in the mother or fetus. Many such screening utilizes cell-free DNA (cfDNA), however, the use of cfDNA suffers from several difficulties, since only a small portion of the cfDNA in maternal plasma originates from the fetus.

[0005] In addition, prenatal screening for certain inherited conditions has traditionally required obtaining DNA samples from both mother and father.For example, traditional approaches to detect chromosomal aneuploidy and various genetic conditions require obtaining genomic DNA (gDNA) samples from both mother and father of fetus, and cfDNA samples from mother.Therefore, such tests require at least three samples, each of which can be processed and evaluated in a different manner. Summary of the Invention

[0006] The present disclosure addresses these challenges by providing methods to selectively enrich the fetal fraction of a maternal sample such that NIPS for both chromosomal aneuploidies and other genetic variants / variants can be performed in parallel using only a single maternal sample.

[0007] [overview] The present disclosure is generally directed to novel sample preparation and parallel screening for chromosomal aneuploidies and other genetic variations, such as pathogenic SNPs, INDELs, and single gene copy number variations, from a single sample. These compositions and processes improve non-invasive prenatal screening (NIPS) by streamlining and simplifying the required analysis, utilizing fewer samples, and reducing background noise, all with less complexity and less time required compared to traditional prenatal screening analysis.

[0008] In one aspect, the present disclosure provides a method of preparing a biological sample comprising an enriched fetal fraction, the method comprising: (a-1) obtaining a biological sample containing cell-free DNA (cfDNA) from a pregnant woman; (b-1) extracting cfDNA from a biological sample; (c-1) preparing a library of cfDNA fragments to obtain a cfDNA library; (d-1) separating the cfDNA fragments in the cfDNA library by size to retain only cfDNA fragments that are less than about 150 nucleotides in length, about 155 nucleotides in length, about 160 nucleotides in length, about 165 nucleotides in length, about 170 nucleotides in length, about 175 nucleotides in length, about 180 nucleotides in length, about 185 nucleotides in length, about 190 nucleotides in length, about 195 nucleotides in length, or about 200 nucleotides in length; (e-1) sequencing the retained cfDNA fragments to obtain a first sequence library; (f-1) identifying (i) sequences of cell-free fetal DNA (cffDNA) and (ii) sequences of cell-free maternal DNA (cfmDNA) present in at least two windows of a first sequence library based on read length; (g-1) isolating cffDNA sequences from each of at least two windows of the sequence library, thereby obtaining at least two fetal fraction enriched sequence libraries; or (a-2) obtaining a biological sample containing cell-free DNA (cfDNA) from a pregnant woman; and (b-2) extracting cfDNA from the biological sample; and (c-2) separating cfDNA fragments in the sample extracted from (b-2) to retain only cfDNA fragments that are less than about 150 nucleotides in length, about 155 nucleotides in length, about 160 nucleotides in length, about 165 nucleotides in length, about 170 nucleotides in length, about 175 nucleotides in length, about 180 nucleotides in length, about 185 nucleotides in length, about 190 nucleotides in length, about 195 nucleotides in length, or about 200 nucleotides in length; (d-2) preparing a cfDNA library from the isolated cfDNA fragments from (c-2); (e-2) sequencing the cfDNA library to obtain a first sequence library; (f-2) identifying (i) sequences of cell-free fetal DNA (cffDNA) and (ii) sequences of cell-free maternal DNA (cfmDNA) present in at least two windows of the first sequence library based on read length; (g-2) isolating cffDNA sequences from each of at least two windows of the sequence library, thereby obtaining at least two fetal fraction-enriched sequence libraries.

[0009] In some embodiments, separating the cfDNA fragments enriches the fetal fraction in the biological sample by about 1.1-fold, about 1.2-fold, about 1.3-fold, about 1.4-fold, about 1.5-fold, about 1.6-fold, about 1.7-fold, about 1.8-fold, about 1.9-fold, or about 2.0-fold.

[0010] In some embodiments, isolating sequences of cffDNA from at least two windows of the first sequence library enriches the fetal fraction in the biological sample by about 1.1 fold, about 1.2 fold, about 1.3 fold, about 1.4 fold, about 1.5 fold, about 1.6 fold, about 1.7 fold, about 1.8 fold, about 1.9 fold, about 2.0 fold, about 2.1 fold, about 2.2 fold, about 2.3 fold, about 2.4 fold, about 2.5 fold, about 2.6 fold, about 2.7 fold, about 2.8 fold, about 2.9 fold, about 3.0 fold, about 3.1 fold, about 3.2 fold, about 3.3 fold, about 3.4 fold, or about 3.5 fold.

[0011] In some embodiments, separating the cfDNA fragments comprises electrophoresis.

[0012] In some embodiments, at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, or at least ten windows of the first sequence library are evaluated to identify and isolate cffDNA sequences, thereby obtaining at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, or at least ten fetal fraction enriched sequence libraries, respectively.

[0013] In some embodiments, the method may further include identifying and separating cffDNA from cfmDNA by comparing sequence reads of cffDNA and cfmDNA in the first sequence library to a reference genome, demultiplexing sequence reads from the first library, removing duplicate sequences from the first sequence library, or a combination thereof.

[0014] In some embodiments, the method may further comprise evaluating at least two fetal fraction enriched sequence libraries for the presence of one or more genetic mutations, in some embodiments, the one or more genetic mutations are 21-hydroxylase deficiency, ABCC8-associated hyperinsulinism, ARSACS, achondroplasia, achromatopsia, adenosine monophosphate deaminase 1, agenesis of corpus callosum with neuronopathy, alkaptonuria, alpha-1-antitrypsin deficiency, alpha-mannosidosis, alpha-sarcoglycanopathy, alpha-thalassemia, angiotensin II receptor type I, Alzheimer's, Apolipoprotein E genotype Alzheimer's, Argininosuccinic aciduria, Aspartylglycosaminuria, Ataxia with Vitamin E deficiency, Ataxia-telangiectasia, Autoimmune polyendocrine syndrome type 1, BRCA1 hereditary breast / ovarian cancer, BRCA2 hereditary breast / ovarian cancer, Bardet-Biedl syndrome, Best vitelliform macular dystrophy, Beta-sarcoglycanopathy, Beta-thalassemia, Biotinidase deficiency, Brain ... Wu syndrome, Bloom syndrome, CFTR-related disorders, CLN3-related neuronal ceroid-lipofuscinosis, CLN5-related neuronal ceroid-lipofuscinosis, CLN8-related neuronal ceroid-lipofuscinosis, Canavan disease, carnitine palmitoyltransferase IA deficiency, carnitine palmitoyltransferase II deficiency, cartilage-hair hypoplasia, cavernous vascular malformation, choroideremia, Cohen syndrome, congenital cataracts, facial dysmorphisms and neuropathy, congenital glycosylation disorders Common comorbidity type Ia, congenital glycosylation disorder type Ib, congenital Finnish nephrotic syndrome, Crohn's disease, cystinosis, DFNA9 (COCH), diabetes mellitus and hearing loss, early-onset primary dystonia (DYTI), Herlitz-Pearson junctional epidermolysis bullosa, FANCC-associated Fanconi anemia, FGFR1-associated craniosynostosis, FGFR2-associated craniosynostosis, FGFR3-associated craniosynostosis, Factor V Leiden thrombophilia, Factor V R2 mutations (Factor V R2Mutation) Thrombotic diathesis, Factor XI deficiency, Factor XIII deficiency, Familial adenomatous polyposis, Familial dysautonomia, Familial hypercholesterolemia type B, Familial Mediterranean fever, Free sialic acid storage disorder, Frontotemporal dementia with parkinsonism-17, Fumarase deficiency, GJB2-related DFNA3 non-syndromic hearing loss and hearing loss, GJB2-related DFNB1 non-syndromic hearing loss and hearing loss, GNE-related myopathy, Galactosemia, Gaucher disease, Glucose-6-phosphate dehydrogenase deficiency, Glutaric acidemia type 1, Glycogen storage disease type 1a, Glycogen storage disease type Ib, Glycogen storage disease type II, Glycogen storage disease type III, Glycogen storage disease type V, Greisil syndrome, HFE-associated hereditary hemochromatosis, Halder AIM AIM), Hemoglobin S beta-thalassemia, Hereditary fructose intolerance, Hereditary pancreatitis, Hereditary thymine-uraciluria, Hexosaminidase A deficiency, Hidrotic ectodermal dysplasia 2, Homocystinuria caused by cystathionine beta synthase deficiency, Hyperkalemic periodic paralysis type 1, Hyperornithinemic-hyperammonemia-homocitrinemia syndrome, Primary type 1 hyperoxaluria, Primary type 2 hyperoxaluria, Hypochondroplasia, Hypokalemic periodic paralysis type 1, Hypokalemic periodic paralysis type 2, Hypophosphatasia, Infantile myopathy and Lactic acidosis (fatal and non-fatal forms), Isovaleric acidemia Acidemias, Krabbe disease, LGMD2I, Leber's hereditary optic neuropathy, French-Canadian Leigh encephalopathy, Long-chain 3-hydroxyacyl-CoA dehydrogenase deficiency, MELAS, MERRF, MTHFR deficiency, MTHFR thermolabile variants, MTRNR1-associated hearing loss and deafness, MTTS1-associated hearing loss and deafness, MYH-associated polyposis, Maple syrup urine disease type 1A, Maple syrup urine disease type 1B, McCune-Albright syndrome, Medium-chain acyl-Coenzyme A dehydrogenase deficiency, Macrocephalic leukoencephalopathy with subcortical cysts, Metachromatic leukodystrophy, Mitochondrial cardiomyopathy, Mitochondrial DNA-associated Leigh encephalopathy and NARP, Mucolipidosis type IV, Mucopolysaccharidosis type I, Mucopolysaccharidosis type IIIA, Mucopolysaccharidosis type VII, Multiple endocrine neoplasia type 2, Muscle-Eye-Brain diseaseDisease), Neurological Phenotype Nemaline Myopathy, Niemann-Pick Disease Due to Sphingomyelinase Deficiency, Niemann-Pick Disease Type C1, Nijmegen Chromosomal Instability Syndrome, PPT1-Associated Neuronal Ceroid-Lipofuscinosis, PROP1-Associated Pituitary Hormone Deficiency, Pallister-Hall Syndrome, Paramyotonia Congenita, Pendred Syndrome, Peroxisomal Bicylosis, Pervasive Developmental Disorders, Phenylalanine Hydroxylase Deficiency, Plasminogen Activator Inhibitor 1, Autosomal Recessive Polycystic Kidney Disease, Prothrombin G20210A Thrombosis The present invention causes at least one condition selected from the group consisting of sex-based predisposition, pseudovitamin D deficiency rickets, pyknotic dysostosis, autosomal recessive retinitis pigmentosa Bothnia type, Rett syndrome, chondrodysplasia punctata type 1, short-chain acyl-CoA dehydrogenase deficiency, Shwachman-Diamond syndrome, Sjogren-Larsson syndrome, Smith-Lemli-Opitz syndrome, spastic paraplegia 13, sulfate transporter-related skeletal dysplasia, TFR2-related hereditary hemochromatosis, TPP1-related neuronal ceroid-lipofuscinosis, thanatophoric skeletal dysplasia, transthyretin amyloidosis, trifunctional protein deficiency, tyrosine hydroxylase deficiency DRD, hypertyrosinemia type 1, Wilson's disease, X-linked juvenile retinoschisis, cystic fibrosis, spinal muscular atrophy (SMA), hemoglobinopathies, and Zellweger syndrome spectrum.

[0015] In some embodiments, the method may further comprise evaluating the biological sample comprising cfDNA for the presence of chromosomal aneuploidy. In some embodiments, the chromosomal aneuploidy is selected from monosomy, trisomy, tetrasomy, pentasomy, microdeletion, microduplication, and mosaic versions of monosomy, trisomy, tetrasomy, and pentasomy.

[0016] In another aspect, the present disclosure provides a method for parallel detection of a chromosomal aneuploidy and the presence or absence of at least one genetic variant in a single maternal sample, comprising: (i) obtaining a biological sample from a pregnant woman, the biological sample comprising cell-free DNA (cfDNA); (ii) preparing a cfDNA library; (iii) sequencing the cfDNA library to generate a sequence library; and (iv) detecting the presence or absence of a chromosomal aneuploidy and the presence or absence of at least one genetic variant in the single maternal sample; Methods are provided for detecting the presence or absence of chromosomal aneuploidy and the presence or absence of at least one genetic variant in a single maternal sample after (a) enriching a cfDNA library to increase the fetal fraction, (b) enriching a sequence library to increase the fetal fraction, or (c) a combination thereof, thereby increasing the fetal fraction of the single maternal sample by at least 1.1-fold, at least 1.2-fold, at least 1.3-fold, at least 1.4-fold, or at least 1.5-fold.

[0017] In some embodiments, the biological sample is blood, serum, or plasma.

[0018] In some embodiments, the cfDNA library is enriched to increase the fetal fraction and the sequence library is enriched to increase the fetal fraction.

[0019] In some embodiments, enriching the fetal fraction of the cfDNA library comprises removing any DNA fragments from the cfDNA library that are greater than about 150, 155, 160, 165, 170, 175, or 180 nucleotides in length. In some embodiments, removing DNA fragments from the cfDNA library comprises electrophoresis.

[0020] In some embodiments, enriching the sequence library for the fetal fraction comprises size exclusion based on read length of sequences in at least two windows of the sequence library, thereby obtaining at least two fetal fraction enriched sequence libraries. In some embodiments, at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, or at least ten windows of the first sequence library are evaluated to identify and isolate cffDNA sequences, thereby obtaining at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, or at least ten fetal fraction enriched sequence libraries, respectively. In some embodiments, at least two windows of the sequence library are selected from: (i) sequences that are 0 to 145 nucleotides, (ii) sequences that are 0 to 150 nucleotides, (iii) sequences that are 0 to 155 nucleotides, (iv) sequences that are 0 to 160 nucleotides, (v) sequences that are 0 to 165 nucleotides, (vi) sequences that are 0 to 168 nucleotides, (vii) sequences that are 0 to 170 nucleotides, (viii) sequences that are 0 to 175 nucleotides, (ix) sequences that are 0 to 180 nucleotides, (x) sequences that are 0 to 185 nucleotides, (xi) sequences that are 0 to 190 nucleotides, (xii) sequences that are 0 to 195 nucleotides, (xiii) sequences that are 0 to 200 nucleotides, and (xiv) non-gated.

[0021] In some embodiments, enriching the fetal fraction of the sequence library further comprises identifying and separating cffDNA from cfmDNA by comparing sequence reads of cffDNA and cfmDNA in the first sequence library to a reference genome, demultiplexing sequence reads from the first library, removing duplicate sequences from the first sequence library, or a combination thereof.

[0022] In some embodiments, detecting the presence or absence of at least one genetic variant comprises determining an allele balance for each allele in the sample encoding the at least one genetic variant in each of the at least two fetal fraction enriched sequence libraries, and generating an allele balance trajectory for each allele based on the allele balance in each of the at least two fetal fraction enriched sequence libraries, a depth trajectory based on the depth of the at least two fetal fraction enriched sequence libraries, or a combination of the allele balance trajectory and the depth trajectory.

[0023] In some embodiments, detecting the presence or absence of a chromosomal aneuploidy comprises analyzing the sequencing depth of at least one sequence corresponding to the chromosome of interest in the sequence library. In some embodiments, the sequencing depth of the at least one sequence corresponding to the chromosome of interest is fitted to a model of the expected sequencing depth of the chromosome of interest. In some embodiments, the sequencing depth is calculated using the following formula:

number

[0024] In some embodiments, the sequencing depth is normalized to control for GC-bias, sample background, hybridization probe capture, or a combination thereof.

[0025] In some embodiments, the methods comprise detecting the presence or absence of a chromosomal aneuploidy selected from monosomy, trisomy, tetrasomy, polysomy X, polysomy Y, microdeletion, microduplication, pentasomy, and combinations thereof.

[0026] In some embodiments, the at least one genetic variant is associated with a condition selected from the group consisting of 21-hydroxylase deficiency, ABCC8-associated hyperinsulinism, ARSACS, achondroplasia, achromatopsia, adenosine monophosphate deaminase 1, agenesis of corpus callosum with neuropathy, alkaptonuria, alpha-1-antitrypsin deficiency, alpha-mannosidosis, alpha-sarcoglycanopathy, alpha-thalassemia, angiotensin II receptor type I Alzheimer's disease, and rheumatoid arthritis. , apolipoprotein E genotype Alzheimer's, argininosuccinic aciduria, aspartylglycosaminuria, ataxia with vitamin E deficiency, ataxia telangiectasia, autoimmune polyendocrine syndrome type 1, BRCA1 hereditary breast / ovarian cancer, BRCA2 hereditary breast / ovarian cancer, Bardet-Biedl syndrome, Best vitelliform macular dystrophy, beta-sarcoglycanopathy, beta-thalassemia, biotinidase deficiency, Blau syndrome , Bloom's syndrome, CFTR-related disorders, CLN3-related neuronal ceroid-lipofuscinosis, CLN5-related neuronal ceroid-lipofuscinosis, CLN8-related neuronal ceroid-lipofuscinosis, Canavan disease, carnitine palmitoyltransferase IA deficiency, carnitine palmitoyltransferase II deficiency, cartilage-hair hypoplasia, cavernous vascular malformation, choroideremia, Cohen syndrome, congenital cataracts, facial dysmorphisms, and neurological disorders, congenital glycosylation disorders Type Ia, congenital glycosylation disorder type Ib, congenital Finnish nephrotic syndrome, Crohn's disease, cystinosis, DFNA9 (COCH), diabetes mellitus and hearing loss, early-onset primary dystonia (DYTI), Herlitz-Pearson junctional epidermolysis bullosa, FANCC-associated Fanconi anemia, FGFR1-associated craniosynostosis, FGFR2-associated craniosynostosis, FGFR3-associated craniosynostosis, factor V Leiden thrombophilia, factor V R2 factor mutation thrombophilia, factor XI deficiency, factor XIII deficiency, familial adenomatous polyposis, familial dysautonomia, familial hypercholesterolemia type B, familial Mediterranean fever, free sialic acid storage disorder, frontotemporal dementia with parkinsonism-17, fumarase deficiency, GJB2-related DFNA3 non-syndromic hearing loss and deafness, GJB2-related DFNB1 non-syndromic hearing loss and deafness, GNE-related myopathy, galactosemia,Gaucher disease, glucose-6-phosphate dehydrogenase deficiency, glutaric acidemia type 1, glycogen storage disease type 1a, glycogen storage disease type Ib, glycogen storage disease type II, glycogen storage disease type III, glycogen storage disease type V, Gracil syndrome, HFE-associated hereditary hemochromatosis, Halder AIM, hemoglobin S beta-thalassemia, hereditary fructose intolerance, hereditary pancreatitis, hereditary thymine-uraciluria, hexosaminidase A deficiency, hidrotic ectodermal dysplasia 2, homocystinuria caused by cystathionine beta synthase deficiency, hyperkalemic periodic paralysis type 1, hyperornithineemia-hyperalgia Ammoniemia-homocitrinemia syndrome, primary hyperoxaluria type 1, primary hyperoxaluria type 2, achondroplasia, hypokalemic periodic paralysis type 1, hypokalemic periodic paralysis type 2, hypophosphatasia, infantile myopathy and lactic acidosis (fatal and non-fatal forms), isovaleric acidemia, Krabbe disease, LGMD2I, Leber's hereditary optic neuropathy, French-Canadian Leigh encephalopathy, long-chain 3-hydroxyacyl-CoA dehydrogenase deficiency, MELAS, MERRF, MTHFR deficiency, MTHFR thermolabile variants, MTRNR1 Associated hearing loss and hearing impairment, MTTS1-associated hearing loss and hearing impairment, MYH-associated polyposis, Maple syrup urine disease type 1A, Maple syrup urine disease type 1B, McCune-Albright syndrome, Medium-chain acyl-coenzyme A dehydrogenase deficiency, Macrocephalic leukoencephalopathy with subcortical cysts, Metachromatic leukodystrophy, Mitochondrial cardiomyopathy, Mitochondrial DNA-associated Leigh encephalopathy and NARP, Mucolipidosis type IV, Mucopolysaccharidosis type I, Mucopolysaccharidosis type IIIA, Mucopolysaccharidosis type VII, Multiple endocrine neoplasia type 2, Muscle-eye-brain disease, Neurological phenotype of nemaline myopathy , Niemann-Pick disease due to sphingomyelinase deficiency, Niemann-Pick disease type C1, Nijmegen chromosomal instability syndrome, PPT1-associated neuronal ceroid-lipofuscinosis, PROP1-associated pituitary hormone deficiency, Pallister-Hall syndrome, paramyotonia congenita, Pendred syndrome, peroxisomal bifunctional enzyme deficiency, pervasive developmental disorder, phenylalanine hydroxylase deficiency, plasminogen activator inhibitor 1, autosomal recessive polycystic kidney disease, prothrombin G20210A thrombophilia, pseudovitamin D deficiency rickets, pyknotic dysostosis,It is associated with disorders selected from autosomal recessive retinitis pigmentosa type Bothnia, Rett syndrome, chondrodysplasia punctata type 1, short-chain acyl-CoA dehydrogenase deficiency, Schwachman-Diamond syndrome, Sjögren-Larsson syndrome, Smith-Lemli-Opitz syndrome, spastic paraplegia 13, sulfate transporter-related skeletal dysplasia, TFR2-related hereditary hemochromatosis, TPP1-related neuronal ceroid-lipofuscinosis, thanatophoric skeletal dysplasia, transthyretin amyloidosis, tricyclic enzyme deficiency, tyrosine hydroxylase deficiency (DRD), hypertyrosinemia type 1, Wilson's disease, X-linked juvenile retinoschisis, cystic fibrosis, spinal muscular atrophy (SMA), hemoglobinopathies, and Zellweger syndrome spectrum.

[0027] In another aspect, the disclosure provides a method of enriching a biological sample for cell-free fetal DNA (cffDNA), comprising obtaining a biological sample comprising cell-free DNA (cfDNA) from a pregnant woman, wherein the cfDNA comprises cffDNA and cell-free maternal DNA (cfmDNA), extracting cfDNA from the biological sample, and subjecting the extracted cfDNA to a size exclusion process, wherein the size exclusion process has a cutoff size of about 150 nucleotides, about 155 nucleotides, about 160 nucleotides, about 165 nucleotides, about 170 nucleotides, about 175 nucleotides, or about 180 nucleotides, thereby generating a sample enriched for cffDNA.

[0028] In another aspect, the disclosure provides a method of in silico processing of cell-free DNA (cfDNA), comprising: sequencing a cfDNA sample comprising cell-free fetal (cffDNA) and cell-free maternal DNA (cfmDNA) to prepare a sequence library; performing a read-length based analysis, wherein an allelic balance of nucleic acid sequences of interest is established in at least two windows of the sequence library; and establishing a trajectory based on the allelic balance of the at least two windows.

[0029] In another aspect, the present disclosure provides a method for reducing background noise from redundant genetic material in non-invasive prenatal screening (NIPS), comprising: (i) obtaining a biological sample from a pregnant woman, the biological sample comprising cell-free DNA (cfDNA); (ii) processing cfDNA used for NIPS, wherein the processing comprises enriching the biological sample for cell-free fetal DNA (cffDNA), in silico processing of cfDNA, or a combination thereof.

[0030] In some embodiments, the processing includes both enriching the biological sample for cell-free fetal DNA (cffDNA) and in silico processing of cfDNA.

[0031] In some embodiments, enriching the biological sample for cell-free fetal DNA (cffDNA) comprises any one of the methods of enriching a biological sample for cell-free fetal DNA (cffDNA) disclosed herein.

[0032] In some embodiments, the in silico processing of cfDNA comprises any one of the methods of in silico processing of cell-free DNA (cfDNA) disclosed herein.

[0033] In some embodiments, the method may further include normalization to control for GC-bias, sample background, hybridization probe capture, or a combination thereof.

[0034] The following detailed description is illustrative and explanatory but is not intended to be limiting. [Brief description of the drawings]

[0035] [Figure 1]FIG. 1 provides a diagram comparing conventional size-exclusion techniques with the disclosed size-exclusion method, which is more tolerant and retains more cffDNA. [Diagram 2] We provide a visualization of the disclosed in silico enrichment method, which relies on moving window analysis to closely observe changes in allelic balance as the amount of fetal and maternal cfDNA changes. [Diagram 3] Two ways of visualizing the allelic balance observed from the disclosed moving window analysis are shown. [Figure 4] 1 illustrates an overview of an exemplary computational flow for one embodiment of the disclosed method and system. [Diagram 5] Shown are several visual representations of how depth calling can be used to establish the presence of chromosomal aneuploidy. The top panel compares conventional karyotypes with depth reads for chromosome 21 in normal pregnancies and pregnancies in which the fetus has trisomy 21. The middle panel depicts the type of depth shift that is expected if a trisomy is observed. The bottom panel shows the expected fit of four known copy number (CN) curves (e.g., CN=1, CN=2, CN=3, and CN=4) representing various ploidies, with the shaded region indicating how the depth of reads from samples containing trisomy fit within the expected fit curve. [Figure 6] 1 shows an exemplary improvement in data plots that can be achieved by using a three-fold normalization that controls for variation caused by 1) GC bias, 2) sample background, and 3) hybridization probe capture. [Figure 7] Shown are the fits of depth reads to expected fit curves for several chromosomes using different fit samples. The shaded area in each plot represents the depth of a given sample for the indicated chromosome. The fit curves from left to right through each plot are the expected fits for 1, 2, or 3 chromosomes fitted to the model. [Figure 8] A depth trajectory plot of a gene (SMN2) in which the mother has one copy of the gene and the fetus has zero copies is shown. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0036] The sample preparation and methods disclosed herein are generally directed to a novel process of collecting a biological sample (e.g., blood or other DNA-containing sample) from a biological mother and then performing screening, such as parallel detection of chromosomal aneuploidies and genetic mutations through non-invasive prenatal screening (e.g., recessive surveillance procedures). That is, the present disclosure provides a single (e.g., parallel) test (e.g., chromosomal aneuploidy and genetic variant screening) to discover two sets of detectable genetic conditions using a sample from only one individual, the biological mother. Combining these two surveillance tests into a single test, without involving the biological father, provides efficiency and convenience over conventional tests and methods that often require a paternal sample and in which chromosomal aneuploidy screening and genetic variant screening are performed separately. Furthermore, sample preparation can improve sensitivity, specificity, and minimize noise from excess genetic material unnecessary for detection of various causative genetic variants.

[0037] The embodiments according to the present disclosure are described more fully below. However, aspects of the present disclosure may be embodied in different forms and should not be construed as being limited to the embodiments described herein. Rather, these embodiments are provided so that the present disclosure will be thorough and complete, and will fully convey the scope of the present invention to those skilled in the art. The terms used in the description of this specification are merely for the purpose of describing specific embodiments, and are not intended to limit the present invention.

[0038] Unless expressly stated otherwise, all particular embodiments, features, and terms are intended to include both the recited embodiment, feature, or term, and equivalents thereof.

[0039] I. Definition As used herein, the singular forms "a," "an," and "the" designate both the singular and the plural, unless expressly stated to designate only the singular.

[0040] As used herein, the term "about" is to be understood as a relative term that encompasses both the stated numerical value and a range of + / - 10%. For example, the phrase "about 10" should be understood to mean both "10" and "from 9 to 11."

[0041] Also, as used herein, "and / or" refers to and includes any and all possible combinations of one or more of the associated listed items, as well as the lack of combinations when interpreted in the alternative ("or").

[0042] As used herein, "optional" or "optionally" means that the subsequently described event or circumstance may or may not occur, and the description includes instances in which the event or circumstance occurs and instances in which the event or circumstance does not occur.

[0043] As used herein, "DNA-binding particles" refers to any conventional solid phase material that interacts with DNA fragments, such as cfDNA fragments, or is modified to interact with them.Solid phase materials are, for example, any kind of insoluble, usually hard material, matrix, or stationary phase material that interacts with DNA either directly or indirectly in reaction solution.In certain exemplary embodiments, DNA-binding particles are beads.

[0044] As used herein, "beads" refers to solid-phase particles of any convenient size and may have an irregular or regular shape. In certain exemplary embodiments, the surface of the beads is modified to bind to DNA, either directly and / or indirectly. For example, the beads may contain silanol groups, carboxylic acid groups, or other groups that facilitate direct and / or interaction of the beads with DNA. In certain exemplary embodiments, silica beads (and gels) may be functionalized by adding primary amines, thiols, sulfhydryls, propyls, octyl, and other derivatives to the hydroxyl groups (silanols) attached to the silica. Beads may be made from any number of known materials, including cellulose, cellulose derivatives, acrylics, glass, silica gel, polystyrene, gelatin, polyvinylpyrrolidone, copolymers of vinyl and acrylamide, such as polystyrene cross-linked with divinylbenzene, polyacrylamide, latex gel, polystyrene, dextran, rubber, silicone, plastic, nitrocellulose, natural sponge, silica gel, controlled pore glass (CPG), metal, cross-linked dextran (e.g., Sephadex®), agarose gel (Sepharose®), and other solid phase bead supports known to those of skill in the art. In certain exemplary embodiments, the beads may be packed together to form a column that can be used in conventional column chromatography.

[0045] As used herein, the term "genetic variant" when used in relation to the screening, calling, or process described herein refers to a change from what is considered to be a non-pathogenic or wild-type gene sequence. Thus, the term "genetic variant" includes pathogenic single nucleotide polymorphisms (SNPs), insertions or deletions of bases (INDELs) in the genome of a subject, substitution mutations, single gene copy number mutations, and the like. Additionally, it should be noted that the term "genetic variant" as used herein is distinct from chromosomal aneuploidy, and the term "genetic variant" does not relate to missing or extra chromosomes. Rather, the term "genetic variant" is understood to relate to features or changes (pathogenic or otherwise) in the genome sequence of a subject, rather than chromosomal abnormalities.

[0046] As used herein, the term "cfDNA library" or "nucleic acid library" may be used interchangeably to refer to a collection of nucleic acids, e.g., a collection of cell-free nucleic acids derived from a biological sample. In some embodiments, a cfDNA library or nucleic acid library is generated by amplifying nucleic acids in a sample or by preparing a library using a PCR-free based method. In some embodiments, a cfDNA library or nucleic acid library is generated by amplifying specific target fragments in a sample, as described in more detail below. In some embodiments, some or all of the nucleic acids in a cfDNA library or nucleic acid library include an adapter sequence. The adapter sequence may be located at one or both ends. The adapter sequence may be useful, for example, for sequencing methods (e.g., NGS methods), for amplification, for reverse transcription, or for cloning into a vector.

[0047] A cfDNA library or nucleic acid library may include a collection of nucleic acid fragments that may include a target nucleic acid sequence (e.g., a nucleic acid sequence capable of detecting a genetic variant associated with a disease), a reference nucleic acid sequence, or a combination thereof. In some embodiments, two or more cfDNA or nucleic acid libraries from the same subject may be combined.

[0048] As used herein, "sequence library" refers to a collection of nucleic acid sequences prepared by sequencing cfDNA library or nucleic acid library using a massively parallel method such as next generation sequencing or NGS.NGS generally refers to a sequencing method that allows for the massively parallel sequencing of clonally amplified and single nucleic acid molecules, during which multiple, for example, millions of nucleic acid fragments from a single sample or multiple different samples are sequenced simultaneously.Non-limiting examples of NGS include sequencing by synthesis, sequencing by ligation, real-time sequencing, and nanopore sequencing.

[0049] II. Sample Preparation Cell-free DNA (cfDNA) is a mixture of DNA that varies in characteristics (e.g., size, sequence, abundance) and tissue of origin (e.g., maternal vs. fetal). For example, cfDNA obtained from a pregnant woman contains DNA of both maternal and fetal origin. The underlying driver of NIPS sensitivity when utilizing cfDNA in a given maternal plasma sample is the fetal fraction (FF). The fetal fraction includes the portion of the total cell-free DNA that is from the fetus or is derived from cell-free fetal DNA (cffDNA). In most samples, the FF value is between 1% and 30%, but in many cases the amount can be even lower.

[0050] The present disclosure provides sample preparations and methods of preparing samples from pregnant women (i.e., pregnant mothers or biological mothers) that can be used to improve sensitivity, specificity, and minimize noise when performing NIPS. In particular, sample preparation can rely on physical processing of a cfDNA sample obtained from the pregnant woman, in silico processing of sequencing reads generated from a cfDNA sample obtained from the pregnant woman, or a combination thereof.

[0051] A. Physical Enrichment of the Fetal Fraction Physical processing of cfDNA sample (e.g., blood) obtained from pregnant women by the method of the present disclosure can enrich the fetal fraction of cfDNA sample up to 3 times.In particular, fetal fraction can be enriched in the sample by size selection using a size cutoff that retains most of the fetal cell-free DNA fragments and removes some of the large cell-free maternal DNA fragments.For example, the cutoff can be set to retain cfDNA fragments that are less than about 150 nucleotides in length, about 155 nucleotides in length, about 160 nucleotides in length, about 165 nucleotides in length, about 170 nucleotides in length, about 175 nucleotides in length, or about 180 nucleotides in length.

[0052] In some embodiments, the method comprises the steps of: 75 nucleotides or less, 80 nucleotides or less, 85 nucleotides or less, 90 nucleotides or less, 95 nucleotides or less, 100 nucleotides or less, 105 nucleotides or less, 110 nucleotides or less, 115 nucleotides or less, 120 nucleotides or less, 125 nucleotides or less, 130 nucleotides or less, 135 nucleotides or less, 140 nucleotides or less, 145 nucleotides or less, 150 nucleotides or less, 155 nucleotides or less, 160 nucleotides or less, 165 nucleotides or less, 170 nucleotides or less, 175 nucleotides or less, 180 nucleotides or less, 195 nucleotides or less, 200 nucleotides or less, 205 nucleotides or less, 206 nucleotides or less, 207 nucleotides or less, 209 nucleotides or less, 300 nucleotides or less, 310 nucleotides or less, 311 nucleotides or less, 312 nucleotides or less, 313 nucleotides or less, 314 nucleotides or less, 315 nucleotides or less, 316 nucleotides or less, 317 nucleotides or less, 318 nucleotides or less, 319 nucleotides or less, 320 nucleotides or less, 325 nucleotides or less, 326 nucleotides or less, 327 nucleotides or less, 328 nucleotides or less, 329 nucleotides or less, 330 nucleotides or less, 335 nucleotides or less, 336 nucleotides or less, 337 nucleotides or less, 338 nucleotides or less, 339 can be used to select and isolate fragments that are 10 nucleotides or less, 215 nucleotides or less, 220 nucleotides or less, 225 nucleotides or less, 230 nucleotides or less, 235 nucleotides or less, 240 nucleotides or less, 245 nucleotides or less, 250 nucleotides or less, 255 nucleotides or less, 260 nucleotides or less, 265 nucleotides or less, 270 nucleotides or less, 275 nucleotides or less, 280 nucleotides or less, 285 nucleotides or less, 290 nucleotides or less, 295 nucleotides or less, 300 nucleotides or less, 305 nucleotides or less, 310 nucleotides or less, 311 nucleotides or less, 315 nucleotides or less, 320 nucleotides or less, or 325 nucleotides or less. In some embodiments, the target size can be 125 nucleotides or less, 130 nucleotides or less, 135 nucleotides or less, 140 nucleotides or less, 145 nucleotides or less, 150 nucleotides or less, 155 nucleotides or less, 160 nucleotides or less, 165 nucleotides or less, 170 nucleotides or less, 175 nucleotides or less, 180 nucleotides or less, 195 nucleotides or less, or 200 nucleotides or less. Regardless of the exact cutoff or target size, the goal of the process is to retain cffDNA with little or no loss and minimize or deplete cfmDNA.

[0053] This type of size-based exclusion can be performed using electrophoresis (e.g., gel electrophoresis or capillary electrophoresis) and other known methods that may utilize DNA-binding particles such as beads (e.g., AMPURE™ beads). In one embodiment, nucleic acid electrophoretic separation is used prior to recovery of the desired fragment length. While a variety of known electrophoretic processes can be used for this purpose, in one embodiment, a NIMBUS Select™ workstation with Ranger Technology™ can be used for high-throughput nucleic acid size selection. Other strategies for fragment size selection include electrophoresis on agarose cassettes (BluePippin, Sage Science) following the manufacturer's instructions for the "range" mode. Short fragments are eluted from the gel until the desired target size of the eluted DNA is obtained. Still other methods include, but are not limited to, solid support capture (e.g., affinity columns) such as antibody-coated spin columns, synchronous (or asynchronous) drag coefficient size selection (SCODA), solid-phase reversible immobilization size selection (e.g., using carboxylated magnetic beads), affinity chromatography processes, or a combination of PCR amplification with variable length amplicons and microchip separation.

[0054] The disclosed size based exclusion methods can enrich the fetal fraction in a cfDNA sample by at least 1.1x, 1.2x, 1.25x, 1.5x, 1.75x, 2x, 2.25x, 2.5x, 2.75x, 3x, 3.25x, 3.5x, 3.75x, 4x, 4.25x, 4.5x, 4.75x, 5x, 5.5x, 6x, 6.5x, 7x, 7.5x, 8x, 8.5x, 9x, 9.5x, 10x, 15x, 20x, 25x or more.

[0055] Thus, the present disclosure provides a method for size selection of cell-free fetal DNA (cffDNA) to enrich the fetal fraction in a DNA sample obtained from a pregnant woman, the method comprising subjecting a cell-free DNA (cfDNA) sample comprising cffDNA and cell-free maternal DNA (cfmDNA) to a size exclusion process.

[0056] B. In silico enrichment of the fetal fraction Additionally, the present disclosure provides in silico enrichment of cfDNA samples (e.g., blood, plasma, serum) obtained from pregnant women, which can further enrich the fetal fraction of the cfDNA sample. In particular, the disclosed in silico enrichment includes size analysis based on read length. For the purposes of this disclosure, "size analysis based on read length" is an in silico process that establishes a trajectory from a range of windows and is applied to sequencing read data. The established trajectory is based on the allelic balance (AB) observed across a set of FF levels. Thus, the FF levels are determined via in silico size selection from different windows, thus allowing for differentiation between maternal DNA and fetal DNA (cfmDNA and cffDNA, respectively). For example, the trajectory can show 55% AB at 10% FF, 60% AB at 15% FF, and 65% AB at 20% FF. This is an upward sloping trajectory, since AB increases as FF increases. Both the slope and offset (or intercept) of such trajectories are useful. For example, if cfmDNA is predominantly selected by a given window, the resulting AB will mostly reflect the maternal genotype, since the FF is as low as possible. A deviation in the AB indicates the fetal genotype, since more FFs are picked up by windows with smaller fragments. As a result, if the intercept is around 50% (meaning that for the variant, the mother is heterozygous), a trajectory with a negative slope suggests that the fetus has not inherited the particular maternal variant.

[0057] Understanding the allelic balance in a cfDNA sample improves the ability to focus on the desired sample fraction (e.g., FF for chromosomal aneuploidy and genetic variant analysis, or maternal fraction for carrier analysis). In some embodiments, moderate size selection in vitro (i.e., physical processing / size exclusion), followed by size-based moving window analysis, may provide the best results.

[0058] Once a sequence library has been prepared, the fetal fraction of the sequence library can be further processed or enriched using in silico moving window analysis. For purposes of the disclosed methods, a "window" is a selection or subsection of a sequence library that includes a particular size range of sequences. For example, a "window" can be 0-145 nucleotides, 0-150 nucleotides, 0-155 nucleotides, 0-160 nucleotides, 0-165 nucleotides, 0-170 nucleotides, 0-175 nucleotides, 0-180 nucleotides, 0-185 nucleotides, 0-190 nucleotides, 0-195 nucleotides, 0-200 nucleotides, 0-205 nucleotides, 0-210 nucleotides, 0-215 nucleotides, 0-220 nucleotides, 0-225 nucleotides, 25-30 nucleotides, 30-35 nucleotides, 40-45 nucleotides, 50-60 nucleotides, 60-70 nucleotides, 70-80 nucleotides, 80-90 nucleotides, 90-100 nucleotides, 100-110 nucleotides, 120-130 nucleotides, 130-140 nucleotides, 140-150 nucleotides, 150-160 nucleotides, 170-185 nucleotides, 180-190 nucleotides, 195-200 nucleotides, 190-195 nucleotides, 200-205 nucleotides, 210-220 nucleotides, 220-230 nucleotides, 250-260 nucleotides, 260-270 nucleotides, 270-280 nucleotides, 280-290 145 nucleotides, 25-150 nucleotides, 25-155 nucleotides, 25-160 nucleotides, 25-165 nucleotides, 25-170 nucleotides, 25-175 nucleotides, 25-180 nucleotides, 25-185 nucleotides, 25-190 nucleotides, 25-195 nucleotides, 25-200 nucleotides, 25-205 nucleotides, 25-210 nucleotides, 25-215 nucleotides, 25-220 nucleotides, 25-225 nucleotides, 50 ~145 nucleotides, 50~150 nucleotides, 50~155 nucleotides, 50~160 nucleotides, 50~165 nucleotides, 50~170 nucleotides, 50~175 nucleotides, 50~180 nucleotides, 50~185 nucleotides, 50~190 nucleotides, 50~195 nucleotides, 50~200 nucleotides, 50~205 nucleotides, 50~210 nucleotides, 50~215 nucleotides, 50~220 nucleotides, 50~225 nucleotides, 7 5-145 nucleotides, 75-150 nucleotides, 75-155 nucleotides, 75-160 nucleotides, 75-165 nucleotides, 75-170 nucleotides, 75-175 nucleotides, 75-180 nucleotides, 75-185 nucleotides, 75-190 nucleotides, 75-195 nucleotides, 75-200 nucleotides, 75-205 nucleotides, 75-210 nucleotides, 75-215 nucleotides, 75-220 nucleotides, 75-225 nucleotides,A window may encompass all of the sequences in a sequence library, be it 100-145 nucleotides, 100-150 nucleotides, 100-155 nucleotides, 100-160 nucleotides, 100-165 nucleotides, 100-170 nucleotides, 100-175 nucleotides, 100-180 nucleotides, 100-185 nucleotides, 100-190 nucleotides, 100-195 nucleotides, 100-200 nucleotides, 100-205 nucleotides, 100-210 nucleotides, 100-215 nucleotides, 100-220 nucleotides, 100-225 nucleotides, or any range therebetween. A window may be considered "ungated" if no specific maximum and minimum values ​​are set, and instead the window encompasses the entire sequence library. Figure 2 shows an example in which sequences in a sequence library are divided into four windows.

[0059] Thus, the disclosed methods of in silico enrichment can include size exclusion based on read length of sequences in at least two windows of a sequence library, thereby obtaining at least two fetal fraction enriched sequence libraries. In some embodiments, three, four, five, six, seven, eight, nine, ten, or more windows can be evaluated. In some embodiments, at least five, at least six, at least seven, or at least eight windows can be evaluated. In some embodiments, the windows are the same size (e.g., each window encompasses a set range of nucleotides, such as 0-100, 5-105, 10-110, etc.). In some embodiments, the windows are different sizes. For example, the size of each additional window can be increased while the minimum remains the same (e.g., a set of windows with size cutoffs of 0-145, 0-150, 0-155, 0-160, 0-165, 0-170, etc.). Comparing the allelic balance in each window allows the calculation of the allelic balance trajectory between various fetal fraction enriched sequence libraries. The trajectory is the change in the allelic balance percentage of any given gene sequence of interest over the observation window. The allelic balance trajectory can be calculated as the slope of the allelic balance in each observation window and can be visualized in several ways, as shown in Figure 3.

[0060] Additionally, libraries of cfm DNA sequences can be enriched by focused analysis of fragments between two sizes, or any size range therebetween, such as 100-200 nucleotides, 105-200 nucleotides, 110-200 nucleotides, 115-200 nucleotides, 120-200 nucleotides, 125-200 nucleotides, 130-200 nucleotides, 135-200 nucleotides, 140-200 nucleotides, 140-200 nucleotides, 145-200 nucleotides, 150-200 nucleotides, 155-200 nucleotides, 160-200 nucleotides, 165-200 nucleotides, 170-200 nucleotides, or 175-200 nucleotides. In some embodiments, the size range selected for enrichment can be from about 155 to about 200 nucleotides.

[0061] In some embodiments, at least two windows of the sequence library are selected from: (i) sequences that are 0 to 145 nucleotides, (ii) sequences that are 0 to 150 nucleotides, (iii) sequences that are 0 to 155 nucleotides, (iv) sequences that are 0 to 160 nucleotides, (v) sequences that are 0 to 165 nucleotides, (vi) sequences that are 0 to 168 nucleotides, (vii) sequences that are 0 to 170 nucleotides, (viii) sequences that are 0 to 175 nucleotides, (ix) sequences that are 0 to 180 nucleotides, (x) sequences that are 0 to 185 nucleotides, (xi) sequences that are 0 to 190 nucleotides, (xii) sequences that are 0 to 195 nucleotides, (xiii) sequences that are 0 to 200 nucleotides, and (xiv) non-gated. In some embodiments, at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, or at least ten windows are selected from: (i) sequences that are 0 to 145 nucleotides, (ii) sequences that are 0 to 150 nucleotides, (iii) sequences that are 0 to 155 nucleotides, (iv) sequences that are 0 to 160 nucleotides, (v) sequences that are 0 to 165 nucleotides, (vi) sequences that are 0 to 168 nucleotides, (vii) sequences that are 0 to 170 nucleotides, (viii) sequences that are 0 to 175 nucleotides, (ix) sequences that are 0 to 180 nucleotides, (x) sequences that are 0 to 185 nucleotides, (xi) sequences that are 0 to 190 nucleotides, (xii) sequences that are 0 to 195 nucleotides, (xiii) sequences that are 0 to 200 nucleotides, and (xiv) non-gated.

[0062] Enriching the fetal fraction of the in silico sequence library also includes identifying and separating cffDNA from cfmDNA by comparing sequence reads of cffDNA and cfmDNA in the first sequence library to a reference genome, demultiplexing sequence reads from the first library, removing duplicate sequences from the first sequence library, or a combination thereof.

[0063] For example, sample preparation may include an in silico binary alignment process, where the collected DNA sample can be computationally reconstructed by using overlaps between short sequencing reads. If a reference genome is available to which the sequencing reads can be aligned, genome reconstruction can be facilitated. Sequence alignment tools can be used to map the short reads stored in the file to the reference genome. Depth and variant processing can then be used to identify and isolate specific gene sequences to inform follow-up analyses that may be directed, for example, to the identification of specific chromosomal aneuploidies and / or genetic variants. In this manner, a very limited amount of initially collected cfDNA can be used to delineate specific portions of the collected DNA and assembled for use with specific assay detection.

[0064] The collected DNA samples can be computationally reconstructed by using the overlap between short sequencing reads. Thus, the DNA samples can be delineated in a first pass using a demultiplexer (e.g., demux), allowing the determination of unique molecular identifiers that may be necessary for the evaluation of specific screens (e.g., carrier, prenatal, etc.). Unique molecular identifiers (UMIs) (sometimes called molecular barcodes (MBCs)) are short sequences (e.g., tags) that are added to DNA fragments during sequencing library preparation protocols to identify desired DNA molecules that may be the target of a specific screen. These tags are added prior to any amplification and can be used to reduce errors and quantitative biases introduced by amplification.

[0065] Once tagged, an alignment process can be used to first align specific tagged DNA sequences to delineate the desired DNA sequences from each other. Then, duplication reduction (e.g., "deduping") can clean up any erroneous identifications and / or misalignments, and can include retaining the consensus sequence of the overlapping portions of the paired end reads. A realignment process can then be performed to generate a more robust delineation between the desired DNA sequence and the tagged DNA sequence.

[0066] Amplification can be used to isolate specific nucleic acid sequences that are of interest or desirable for subsequent screening. For example, in silico amplification can be achieved using computational tools to calculate theoretical polymerase chain reaction (PCR) results using a given set of primers (probes) to amplify DNA sequences from a sequenced DNA sample. After amplification, the quality of a particular read sequence can be improved by removing (e.g., trimming) partial (e.g., incomplete) sequences present at the beginning and end of the sequence. One exemplary but non-limiting method to achieve this is called paired-end (PE) trimming, which can include two input files (for forward and reverse reads) and four output files (for forward paired, forward unpaired, reverse paired, and reverse unpaired reads) to identify and remove partial sequences. Reconstruction of a useful DNA sample can be facilitated and stored in a ready-to-use file. Furthermore, the files can be delineated into different bins with respect to fragment length (in terms of number of nucleotides).

[0067] As part of the depth and variant processing, specific gene sequences stored in a file can be identified and isolated to inform follow-up analyses targeting specific chromosomal aneuploidies and / or causative gene variants. This file can be used during certain procedures to mitigate bias in the sample originally collected. The aforementioned in silico steps and computational preparations can optimize the DNA sample for specific DNA sequences for the specific purpose of a given test or screen.

[0068] The disclosed in silico processes can enrich the fetal fraction in a cfDNA sample by at least 1.1x, 1.2x, 1.25x, 1.5x, 1.75x, 2x, 2.25x, 2.5x, 2.75x, 3x, 3.25x, 3.5x, 3.75x, 4x, 4.25x, 4.5x, 5.75x, 5x, 5.5x, 6x, 6.5x, 7x, 7.5x, 8x, 8.5x, 9x, 9.5x, 10x, 15x, 20x, 25x or more.

[0069] Alternatively, if desired, the disclosed in silico processes can be used to enrich the maternal fraction of a sample by selecting larger fragments. In some embodiments, the disclosed in silico processes can enrich the maternal fraction in a cfDNA sample by at least 1.1 times, 1.2 times, 1.25 times, 1.5 times, 1.75 times, 2 times, 2.25 times, 2.5 times, 2.75 times, 3 times, 3.25 times, 3.5 times, 3.75 times, 4 times, 4.25 times, 4.5 times, 5.75 times, 5 times, 5.5 times, 6 times, 6.5 times, 7 times, 7.5 times, 8 times, 8.5 times, 9 times, 9.5 times, 10 times, 15 times, 20 times, 25 times, or more.

[0070] Thus, the present disclosure provides a method for in silico selection and enrichment of cffDNA, comprising sequencing a cell-free DNA (cfDNA) sample comprising cffDNA and cell-free DNA matrix (cfmDNA), and performing a size analysis based on read length, using a size-based moving window to establish a trajectory based on the allelic balance between cfmDNA and cffDNA to resolve the genotype of cfmDNA or cffDNA in a given sample. In some embodiments, such a method may further comprise identifying and separating cffDNA from cfmDNA by comparing sequence reads of cffDNA and cfmDNA to a reference genome, demultiplexing the sequence reads, and removing duplicate sequences.

[0071] C. Combining physical and in silico enrichment The above-mentioned sample preparation methods for enriching the fetal fraction of a given sample can be carried out individually or in combination. Prior to either physical enrichment or in silico enrichment, total cfDNA can be isolated from the maternal sample (e.g., blood, plasma, serum) by conventional means. For example, total cfDNA can be extracted from the clarified plasma obtained from the sample using the APOSTLE™ Cell-Free DNA Extraction kit. Other known methods and commercially available kits for cfDNA extraction can also be used, including but not limited to kits from Molzym GmbH&Co KG (Bremen, DE), Qiagen (Hilden, DE), Macherey-Nagel (Dueren, DE), Roche (Basel, CH), and Sigma (Deisenhofen, DE).

[0072] After physical and in silico enrichment, the fetal fraction used for further testing, screening, or analysis was 2%, 3%, 4%, 5%, 6%, 7%, 8%, 9%, 10%, 11%, 12%, 13%, 14%, 16%, 17%, 18%, 19%, 20%, 21%, 22%, 23%, 24%, 25%, 26%, 27%, 28%, 29%, 30%, 31%, 32%, 33%, 34%, 35%, 36%, 37%, 38%, 39% of the DNA sample. , 40%, 41%, 42%, 43%, 44%, 45%, 46%, 47%, 48%, 49%, 50%, 51%, 52%, 53%, 54%, 55%, 56%, 57%, 58%, 59%, 60%, 61%, 62%, 63%, 64%, 65%, 66%, 67%, 68%, 69%, 70%, 71%, 72%, 73%, 74%, 75%, 76%, 77%, 78%, 79%, 80%, 85%, 90%, 95%, 99%, or 100%.Additionally or alternatively, after physical enrichment and in silico enrichment, the fetal fraction may be between about 5% and 100%, between about 5% and about 95%, between about 5% and about 90%, between about 5% and about 85%, between about 5% and about 80%, between about 5% and about 75%, between about 10% and 100%, between about 10% and about 95%, between about 10% and about 90%, between about 10% and about 85%, between about 10% and about 80%, between about 10% and about 75%, between about 15% and 100%, between about 15% and about ... 5% to about 95%, about 15% to about 90%, about 15% to about 85%, about 15% to about 80%, about 15% to about 75%, about 20% to 100%, about 20% to about 95%, about 20% to about 90%, about 20% to about 85%, about 20% to about 80%, about 20% to about 75%, about 25% to 100%, about 25% to about 95%, about 25% to about 90%, about 25% to about 85%, about 25% to about 80%, About 25% to about 75%, about 30% to 100%, about 30% to about 95%, about 30% to about 90%, about 30% to about 85%, about 30% to about 80%, about 30% to about 75%, about 35% to 100%, about 35% to about 95%, about 35% to about 90%, about 35% to about 85%, about 35% to about 80%, about 35% to about 75%, about 40% to 100%, about 40% to about 95%, about 40% to about 90 %, about 40% to about 85%, about 40% to about 80%, about 40% to about 75%, about 45% to 100%, about 45% to about 95%, about 45% to about 90%, about 45% to about 85%, about 45% to about 80%, about 45% to about 75%, about 50% to 100%, about 50% to about 95%, about 50% to about 90%, about 50% to about 85%, about 50% to about 80%, and about 50% to about 75%.

[0073] Thus, the disclosure provides methods of preparing a cell-free DNA sample containing an enriched fetal fraction, comprising processing the cfDNA sample using size exclusion to retain cell-free fetal DNA (cffDNA) and remove cell-free maternal DNA (cfmDNA), in silico processing to identify and isolate cffDNA from cfmDNA, or a combination thereof.

[0074] III. Parallel Screening Methods The present disclosure provides a method for assessing or screening for chromosomal aneuploidies and genetic variants in a fetus utilizing only a single biological sample (e.g., blood, plasma, serum) from the biological mother of the fetus. Traditionally, testing for chromosomal aneuploidies and testing for genetic variants were performed separately and required multiple samples. In fact, screening for certain conditions further required that a biological sample be obtained from the biological father as well. The disclosed method serves to overcome these problems and provide a new and useful method to improve conventional non-invasive prenatal screening (NIPS).

[0075] The disclosed methods can include two parallel screens that utilize the same single cfDNA sample from the biological mother: a first screen to detect chromosomal aneuploidies and a second screen to detect genetic variants.

[0076] In the first screening, a specific subsection of the collected sample (e.g., a subsection of a smaller cfDNA fragment) can be used to optimize the fetal fraction and evaluate the presence or absence of chromosomal aneuploidy status. The presence or absence of chromosomal aneuploidy can be established by determining the trajectory that allows maternal and fetal DNA to be distinguished. In this manner, the disclosed screening can simultaneously evaluate fetal chromosomal aneuploidy and maternal chromosomal aneuploidy, which was not possible before. The first screening can additionally or alternatively rely on sequencing depth to determine whether chromosomal aneuploidy is present or absent in a given sample.

[0077] In the second screening, a specific subsection of the collected sample (e.g., a subsection of smaller cfDNA fragments) may be used to optimize the fetal fraction and minimize noise from extra genetic material. The subsections may then be used to detect various genetic variants, for example, by establishing trajectories to delineate relevant sample material from extra sample material. In each screening, an optimal swath of genetic samples containing an appropriate ratio of cell-free maternal DNA (cfmDNA) to cell-free fetal cffDNA may be used to detect with reasonable certainty the presence or absence of known chromosomal aneuploidies and genetic variants, without the need to rely on adjusting the individual focus of parallel screening towards one approach or the other.

[0078] The method can start by collecting a sample from a biological mother, typically through blood sampling, although other biological samples (e.g., plasma, serum, etc.) are contemplated. The sample includes cell-free DNA (cfDNA). cfDNA can include various freely circulating DNA, including circulating tumor DNA (ctDNA), cell-free mitochondrial DNA (cf mtDNA), cell-free maternal DNA (cfmDNA), and cell-free fetal DNA (cffDNA). Since the subject is a pregnant mother, a certain level of fetal DNA will also be present in the cfDNA sample. Furthermore, targeted DNA capture suitable for specific gene sequences can also be performed. Thus, both cfDNA and targeted capture aspects can be used for the purpose of the disclosed method.

[0079] In one aspect, the disclosure provides a method for parallel detection of chromosomal aneuploidies and the presence or absence of at least one genetic variant in a single maternal sample, the method comprising: (i) obtaining a biological sample from a pregnant woman, the biological sample comprising cell-free DNA (cfDNA); (ii) preparing a cfDNA library (e.g., by amplifying a target population of cfDNA fragments); (iii) sequencing the cfDNA library to prepare a sequence library; and (iv) detecting the presence or absence of chromosomal aneuploidies and the at least one genetic variant in the single maternal sample; The present invention provides a method for detecting the presence or absence of chromosomal aneuploidy and at least one genetic variant in a single maternal sample after (a) enriching a cfDNA library to increase the fetal fraction, (b) enriching a sequence library to increase the fetal fraction, or (c) performing a combination thereof, thereby increasing the fetal fraction of the single maternal sample by at least 1.5 times. In some embodiments, both cfDNA libraries are enriched to increase the fetal fraction, and the sequence library is enriched to increase the fetal fraction. The following sections provide further details on the processes involved in each form of enrichment.

[0080] (i).Biological sample: For the purpose of the disclosed method, the biological sample must contain cfDNA, including cffDNA.Examples of samples that can be obtained from biological mothers for use in the disclosed method include, but are not limited to, blood, serum, and plasma.

[0081] In some embodiments, nucleic acid extraction will be performed prior to the amplification of cfDNA in the sample and preparation of a cfDNA library or libraries. Various protocols for nucleic acid extraction can be used in the method of the present technology. Examples of commercially available nucleic acid purification kits include Apostle MiniMax Kit, Molzym GmbH&Co KG (Bremen, DE), Qiagen (Hilden, DE), Macherey-Nagel (Dueren, DE), Roche (Basel, CH), or Sigma (Deisenhofen, DE). Other systems for nucleic acid purification based on the use of polystyrene beads as a support material can also be used. Automated DNA extraction platforms such as QIAsymphony®, Hamilton® automation, or Biorobot® EZ1™ automation systems can also be used.

[0082] (ii) cfDNA library preparation cfDNA library preparation can be performed using known methods of amplification (e.g., xGen Prism Library Prep Kit (IDT™)), as well as PCR-free methods of library preparation, such as the COLLIBRI™, NEBNEXT®, and TRUSEQ™ kits manufactured by Illumina, the KAPA™ HyperPrep kit manufactured by Roche, and the MGI Easy kit manufactured by MG Tech. Optionally, cfDNA library preparation can include a step of end repair. cfDNA can contain overhangs of other damage to the ends of a given nucleic acid sequence, and end repair can convert such damaged or sheared DNA into blunt-ended molecules that are more easily ligated to adapters, tags, or barcodes. One or more ligation reactions can be implemented to attach adapters to nucleic acid sequences from the sample. Adapters are used both to facilitate amplification by providing uniform sequences to which primers can anneal, and to separate sequences of interest. The adaptors may be of a unique length (to allow for separation and isolation via electrophoresis), a unique sequence, or may contain other features to aid in isolation of the target nucleic acid sequence after amplification.

[0083] PCR-based methods are commonly used to generate amplified libraries prior to sequencing or analysis of a given nucleic acid sample; however, PCR is not required and one of skill in the art will be aware of PCR-free methods of library preparation as well. A variety of PCR methods utilizing commercially available reagents and polymerases can be used for the nucleic acid amplification portion of the library preparation (e.g., KAPA™ HiFi HotStart ReadyMix).

[0084] A cfDNA library can be prepared from the maternal sample using any of the approaches described herein or known to those skilled in the art. Optionally, the cfDNA library can be washed using known methods such as isolating the amplified fragments in the library using AMPURE beads, or other similar methods that allow removal of salts, unwanted macromolecules, and other debris from the sample.

[0085] Before sequencing cfDNA library, fetal fraction can be enriched as described herein.In addition, or alternatively, fetal fraction can be enriched in maternal sample before preparing cfDNA library.In brief, enriching fetal fraction of cfDNA library or maternal sample is a physical treatment of sample, which can include removing any DNA fragments from cfDNA library that are greater than about 150 nucleotides, about 155 nucleotides, about 160 nucleotides, about 165 nucleotides, about 170 nucleotides, about 175 nucleotides, about 180 nucleotides, about 185 nucleotides, about 190 nucleotides, about 195 nucleotides, or about 200 nucleotides in length.

[0086] This type of size-based exclusion can be performed using electrophoresis (e.g., gel electrophoresis or capillary electrophoresis) and other known methods that may utilize, for example, DNA-binding particles such as beads (e.g., AMPURE™ beads). In one embodiment, nucleic acid electrophoretic separation is used prior to recovery of the desired fragment length. A variety of known electrophoretic processes can be used for this purpose. For example, in one embodiment, a NIMBUS Select™ workstation with Ranger Technology™ can be used for high throughput nucleic acid size selection. In another embodiment, a BluePippin electrophoresis system can be used.

[0087] Previous methods of size-based exclusion have been used to enrich the fetal fraction of cfDNA libraries, but unlike those previous methods, the inventors have discovered that using a higher cutoff value, as described herein, can improve noise reduction when combined with additional in silico selection. Briefly, without being bound by theory, noise can be reduced because it retains a greater total number of cffDNA molecules through more permissive size selection. Traditionally, using a lower cutoff value was thought to be better because it would eliminate more maternal cfDNA. Figure 1 shows a comparison of the disclosed size exclusion process compared to traditional approaches. As shown in Figure 1, these more restrictive traditional methods also discarded non-trivial amounts of cffDNA. Thus, the disclosed approach of combining a more "permissive" size exclusion technique with additional in silico enrichment is an improvement that specifically addresses a key issue in the field of prenatal screening: enrichment of the fetal fraction without inadvertently or unnecessarily discarding cffDNA, which may be of limited supply as precious within a given sample.

[0088] The disclosed size based exclusion methods can enrich the fetal fraction in a cfDNA sample by at least 1.1x, 1.2x, 1.25x, 1.5x, 1.75x, 2x, 2.25x, 2.5x, 2.75x, 3x, 3.25x, 3.5x, 3.75x, 4x, 4.25x, 4.5x, 5.75x, 5x, 5.5x, 6x, 6.5x, 7x, 7.5x, 8x, 8.5x, 9x, 9.5x, 10x, 15x, 20x, 25x or more.

[0089] (iii) Sequencing of the Nucleic Acid Library The nucleic acid library that can be enriched for the fetal fraction can be sequenced using known sequencing methods (e.g., NovaSeq sequencers and flow cells, Illumina sequencers, pyrosequencing, reversible dye terminator sequencing, SOLiD sequencing, ion semiconductor sequencing, Helioscope single molecule sequencing, Ion Torrent™ (Life Technologies, Carlsbad, CA) amplicon sequencing system, 454™ GS FLX™ sequencing system, SMRT™ sequencing, etc.). In some embodiments, the cfDNA fragments in the nucleic acid library are sequenced from both ends (i.e., paired-end mode). In some embodiments, the cfDNA fragments in the nucleic acid library are sequenced at one end (i.e., single-end mode). In some embodiments, the cfDNA fragments in the nucleic acid library can be isolated or combined using a target capture method such as hybrid capture. Sequencing from both ends of each fragment allows the length of the fragment to be determined. In some embodiments, the resulting sequences can be used to map the cfDNA fragments.

[0090] In some embodiments, the disclosed methods can utilize target capture methods to sequence only specific fragments of interest. The fragments of interest can correspond, for example, to cfDNA encoding genes associated with a genetic disease, condition, or trait (i.e., genetic variants of interest) or cfDNA corresponding to specific chromosomes.

[0091] Once the cfDNA fragments in the nucleic acid library have been sequenced, the fetal fraction of the sequence library can be further enriched using in silico moving window analysis as described herein. For purposes of the disclosed methods, a "window" is a selection or subsection of a sequence library that includes a particular size range of sequences. For example, a "window" can be 0-145 nucleotides, 0-150 nucleotides, 0-155 nucleotides, 0-160 nucleotides, 0-165 nucleotides, 0-170 nucleotides, 0-175 nucleotides, 0-180 nucleotides, 0-185 nucleotides, 0-190 nucleotides, 0-195 nucleotides, 0-200 nucleotides, 25-145 nucleotides, 25-150 nucleotides, 25-155 nucleotides, 25-160 nucleotides, 25-170 nucleotides, 25-180 nucleotides, 25-190 nucleotides, 25-195 nucleotides, 25-20 ... nucleotides, 25-165 nucleotides, 25-170 nucleotides, 25-175 nucleotides, 25-180 nucleotides, 25-185 nucleotides, 25-190 nucleotides, 25-195 nucleotides, 25-200 nucleotides, 50-145 nucleotides, 50-150 nucleotides, 50-155 nucleotides, 50-160 nucleotides, 50-165 nucleotides, 50-170 nucleotides, 50-175 nucleotides, 50-180 nucleotides nucleotides, 50-185 nucleotides, 50-190 nucleotides, 50-195 nucleotides, 50-200 nucleotides, 75-145 nucleotides, 75-150 nucleotides, 75-155 nucleotides, 75-160 nucleotides, 75-165 nucleotides, 75-170 nucleotides, 75-175 nucleotides, 75-180 nucleotides, 75-185 nucleotides, 75-190 nucleotides, 75-195 nucleotides, 75-200 nucleotides In some embodiments, the disclosed methods may include all of the sequences in the sequence library that are 0 to 145 nucleotides, 100 to 150 nucleotides, 100 to 155 nucleotides, 100 to 160 nucleotides, 100 to 165 nucleotides, 100 to 170 nucleotides, 100 to 175 nucleotides, 100 to 180 nucleotides, 100 to 185 nucleotides, 100 to 190 nucleotides, 100 to 195 nucleotides, 100 to 200 nucleotides, or any range therebetween.0-146 nucleotides, 0-147 nucleotides, 0-148 nucleotides, 0-149 nucleotides, 0-150 nucleotides, 0-151 nucleotides, 0-152 nucleotides, 0-153 nucleotides, 0-154 nucleotides, 0-155 nucleotides, 0-156 nucleotides, -157 nucleotides, 0-158 nucleotides, 0-159 nucleotides, 0-160 nucleotides, 0-161 nucleotides, 0-162 nucleotides, 0-163 nucleotides, 0-164 nucleotides, 0-165 nucleotides, 0-166 nucleotides nucleotides, 0-167 nucleotides, 0-168 nucleotides, 0-169 nucleotides, 0-170 nucleotides, 0-171 nucleotides, 0-172 nucleotides, 0-173 nucleotides, 0-174 nucleotides, 0-175 nucleotides, 0-176 nucleotides, 0-177 nucleotides, 0-178 nucleotides, 0-179 nucleotides, 0-180 nucleotides, 0-181 nucleotides, 0-182 nucleotides, 0-183 nucleotides, 0-184 nucleotides, 0-185 nucleotides, 0-186 nucleotides, 0-187 nucleotides nucleotide, 0-188 nucleotides, 0-189 nucleotides, 0-190 nucleotides, 0-191 nucleotides, 0-192 nucleotides, 0-193 nucleotides, 0-194 nucleotides, 0-195 nucleotides, 0-196 nucleotides, 0-197 nucleotides, 0-198 nucleotides, 0-199 nucleotides, 0-200 nucleotides, 5-145 nucleotides, 5-146 nucleotides, 5-147 nucleotides, 5-148 nucleotides, 5-149 nucleotides, 5-150 nucleotides, 5-151 nucleotides, 5- 152 nucleotides, 5-153 nucleotides, 5-154 nucleotides, 5-155 nucleotides, 5-156 nucleotides, -157 nucleotides, 5-158 nucleotides, 5-159 nucleotides, 5-160 nucleotides, 5-161 nucleotides, 5-162 nucleotides, 5-163 nucleotides, 5-164 nucleotides, 5-165 nucleotides, 5-166 nucleotides, 5-167 nucleotides, 5-168 nucleotides, 5-169 nucleotides, 5-170 nucleotides, 5-171 nucleotides, 5-172 nucleotides,5-173 nucleotides, 5-174 nucleotides, 5-175 nucleotides, 5-176 nucleotides, 5-177 nucleotides, 5-178 nucleotides, 5-179 nucleotides, 5-180 nucleotides, 5-181 nucleotides, 5-182 nucleotides, 5-183 nucleotides, 5-184 nucleotides, 5-185 nucleotides, 5-186 nucleotides, 5-187 nucleotides, 5-188 nucleotides, 5-189 nucleotides, 5-190 nucleotides, 5-191 nucleotides, 5-192 nucleotides, 5-193 nucleotides tide, 5-194 nucleotides, 5-195 nucleotides, 5-196 nucleotides, 5-197 nucleotides, 5-198 nucleotides, 5-199 nucleotides, 5-200 nucleotides, 10-145 nucleotides, 10-146 nucleotides, 10-147 nucleotides, 10-148 nucleotides, 10-149 nucleotides, 10-150 nucleotides, 10-151 nucleotides, 10-152 nucleotides, 10-153 nucleotides, 10-154 nucleotides, 10-155 nucleotides, 10-156 nucleotides, -157 nucleotides nucleotides, 10-158 nucleotides, 10-159 nucleotides, 10-160 nucleotides, 10-161 nucleotides, 10-162 nucleotides, 10-163 nucleotides, 10-164 nucleotides, 10-165 nucleotides, 10-166 nucleotides, 10-167 nucleotides, 10-168 nucleotides, 10-169 nucleotides, 10-170 nucleotides, 10-171 nucleotides, 10-172 nucleotides, 10-173 nucleotides, 10-174 nucleotides, 10-175 nucleotides, 10-176 nucleotides tide, 10-177 nucleotides, 10-178 nucleotides, 10-179 nucleotides, 10-180 nucleotides, 10-181 nucleotides, 10-182 nucleotides, 10-183 nucleotides, 10-184 nucleotides, 10-185 nucleotides, 10-186 nucleotides, 10-187 nucleotides, 10-188 nucleotides, 10-189 nucleotides, 10-190 nucleotides, 10-191 nucleotides, 10-192 nucleotides, 10-193 nucleotides, 10-194 nucleotides, 10-195 nucleotides,10-196 nucleotides, 10-197 nucleotides, 10-198 nucleotides, 10-199 nucleotides, 10-200 nucleotides, 15-145 nucleotides, 15-146 nucleotides, 15-147 nucleotides, 15-148 nucleotides, 15-149 nucleotides, 15-150 nucleotides, 15-151 nucleotides, 15-152 nucleotides, 15-153 nucleotides, 15-154 nucleotides, 15-155 nucleotides, 15-156 nucleotides, -157 nucleotides tide, 15-158 nucleotides, 15-159 nucleotides, 15-160 nucleotides, 15-161 nucleotides, 15-162 nucleotides, 15-163 nucleotides, 15-164 nucleotides, 15-165 nucleotides, 15-166 nucleotides, 15-167 nucleotides, 15-168 nucleotides, 15-169 nucleotides, 15-170 nucleotides, 15-171 nucleotides, 15-172 nucleotides, 15-173 nucleotides, 15-174 nucleotides, 15-175 nucleotides, 15-176 nucleotides, 15-177 nucleotides, 15-178 nucleotides, 15-179 nucleotides, 15-180 nucleotides, 15-181 nucleotides, 15-182 nucleotides, 15-183 nucleotides, 15-184 nucleotides, 15-185 nucleotides, 15-186 nucleotides, 15-187 nucleotides, 15-188 nucleotides, 15-189 nucleotides, 15-190 nucleotides, 15-191 nucleotides, 15-192 nucleotides, 1 Two or more (e.g., 2, 3, 4, 5, 6, 7, 8, 9, or 10 or more) windows encompassing fragments of a size range selected from 5 to 193 nucleotides, 15 to 194 nucleotides, 15 to 195 nucleotides, 15 to 196 nucleotides, 15 to 197 nucleotides, 15 to 198 nucleotides, 15 to 199 nucleotides, 15 to 200 nucleotides, or any range therebetween (e.g., 2, 3, 4, 5, 6, 7, 8, 9, or 10 or more). In some embodiments, the disclosed methods utilize a window of 0 to about 145 nucleotides, 0 to about 150 nucleotides, 0 to about 155 nucleotides, 0 to about 160 nucleotides,At least eight windows including size ranges from 0 to about 165 nucleotides, 0 to about 168 nucleotides, 0 to about 175 nucleotides, and 0 to about 190 nucleotides may be utilized. In some embodiments, the disclosed methods may utilize eight windows including size ranges from 0 to 145 nucleotides, 0 to 150 nucleotides, 0 to 155 nucleotides, 0 to 160 nucleotides, 0 to 165 nucleotides, 0 to 168 nucleotides, 0 to 175 nucleotides, and 0 to 190 nucleotides.

[0092] In some embodiments, the disclosed methods comprise a method for isolating a nucleic acid sequence from about 20 to about 145 nucleotides, about 20 to about 150 nucleotides, about 20 to about 155 nucleotides, about 20 to about 160 nucleotides, about 20 to about 165 nucleotides, about 20 to about 170 nucleotides, about 20 to about 175 nucleotides, about 20 to about 180 nucleotides, about 20 to about 185 nucleotides, about 20 to about 190 nucleotides, about 20 to about 195 nucleotides, about 20 to about 200 nucleotides, about 25 to about 145 nucleotides, about 25 to about 150 nucleotides, about 25 to about 155 nucleotides, about 25 to about 160 nucleotides, about 25 to about 165 nucleotides, about 25 to about 170 nucleotides, about 25 to about 175 nucleotides, about 25 to about 180 nucleotides, about 25 to about 185 nucleotides, about 25 to about 190 nucleotides, about 25 to about 195 nucleotides, about 25 to about 200 nucleotides, about 50 to about 145 nucleotides, about 50 to about 150 nucleotides, about 50 to about 155 nucleotides, about 50 to about 160 nucleotides, about 50 to about 165 nucleotides, about 50 to about 170 nucleotides, about 50 to about 175 nucleotides, about 50 to about 18 0 nucleotides, about 50 to about 185 nucleotides, about 50 to about 190 nucleotides, about 50 to about 195 nucleotides, about 50 to about 200 nucleotides, about 75 to about 145 nucleotides, about 75 to about 150 nucleotides, about 75 to about 155 nucleotides, about 75 to about 160 nucleotides, about 75 to about 165 nucleotides, about 75 to about 170 nucleotides, about 75 to about 175 nucleotides, about 75 to about 180 nucleotides, about 75 to about 185 nucleotides, about 75 to about 190 nucleotides, about 75 to about 195 nucleotides, about 75 to about 200 nucleotides nucleotides, about 100 to about 145 nucleotides, about 100 to about 150 nucleotides, about 100 to about 155 nucleotides, about 100 to about 160 nucleotides, about 100 to about 165 nucleotides, about 100 to about 170 nucleotides, about 100 to about 175 nucleotides, about 100 to about 180 nucleotides, about 100 to about 185 nucleotides, about 100 to about 190 nucleotides, about 100 to about 195 nucleotides, about 100 to about 200 nucleotides, or any range therebetween (e.g., 2, 3, 4, 5, 6, 7, 8, 9,Two or more (e.g., 2, 3, 4, 5, 6, 7, 8, 9, or 10 or more) windows encompassing fragments in a size range of 10 or 15 (e.g., 10 or 15 or more).

[0093] For purposes of the disclosed methods, the windows used for subsequent analysis and trajectory calculations can be of different sizes (i.e., each window encompasses a different range of fragment sizes, such as 0-145, 0-150, 0-155, etc.) or the windows can be the same size (i.e., each window encompasses a different fragment but spans a set size range, such as 0-145, 5-150, 10-155, etc.). A window can be considered "ungated" if no specific maximum and minimum values ​​are set; instead, the window encompasses the entire sequence library. Figure 2 shows an example of how sequences in a sequence library can be divided into six different windows.

[0094] As described above, enriching the fetal fraction of the sequence library is a form of in silico enrichment, which may include size exclusion based on the read length of sequences in at least two windows of the sequence library, thereby obtaining at least two fetal fraction enriched sequence libraries. Comparing the allelic balance in each window allows the calculation of the allelic balance trajectory between various fetal fraction enriched sequence libraries. The allelic balance trajectory is the change over the observation window of the allelic balance percentage of any given gene sequence of interest. The allelic balance trajectory can be calculated as the slope of the allelic balance in each observation window, and can be visualized in several ways, as shown in FIG. 3. For example, the banded pattern in the top panel of FIG. 3 shows the divergence of allelic balance over multiple observation windows, or the allelic balance trajectory can be visualized as a Gaussian mixture model (GMM). It should be appreciated that each window (e.g., 0-145, 0-150, 0-155, etc.) has an associated fetal fraction that is distinct from the other windows, and this fetal fraction value can serve as the X-axis of a trajectory plot, as shown in Figure 3 (top panel). In other words, the type of trajectory plot shown in Figure 3 (top panel) provides a visualization of allelic balance versus fetal fraction, with points along the X-axis (i.e., fetal fraction axis) provided by the selection of different windows.

[0095] Regardless of how the allele balance data is visualized, it can be utilized to identify heterozygous and homozygous mutations or markers of interest in a cfDNA sequence library. For example, the allele balance can be converted into a banded pattern plot in which the y-axis displays the percentage of cfDNA in a sample that has a given allele of a particular gene or nucleic acid sequence of interest (e.g., 0%, 10%, 20%, 30%, 40%, 50%, 60%), and the x-axis displays the wild-type sequence or different changes in the gene or nucleic acid of interest that correspond to mutations / variants associated with a particular disease, condition, or trait (e.g., different known mutations in the CFTR gene associated with cystic fibrosis).

[0096] As an example, in a sample or window with a 20% fetal fraction, a 10% band on the y-axis may indicate that it corresponds to a fetus that is a carrier from the DNA of the biological father (or in some instances may represent a de novo mutation in the fetus). A 40% band on the y-axis within this window corresponds to a fetus that is negative (i.e., homozygous reference) for the mutation / variant in the gene or sequence of interest. A 50% band on the y-axis corresponds to a fetus that is a carrier from the DNA of the biological mother, or, if both the mother and father carry the same mutation / variant (i.e., alt allele), the fetus may have the paternal alt allele and the maternal reference allele. Thus, a 50% band may indicate that the fetus and the mother each have one alt allele. A 60% band on the y-axis corresponds to a fetus that is homozygous alt (i.e., the fetus is positive) for the mutation / variant in the gene or sequence of interest. Therefore, analyzing the allele balance across multiple windows of sequence libraries (i.e., multiple fetal fraction enriched sequence libraries) provides a new and useful method to establish the absence of genetic variants / mutations from maternal samples containing cfDNA without the need for any additional samples. Furthermore, as a result of the enrichment provided by moving window analysis, noise and background are significantly reduced, which allows robust detection even in samples with very small amounts of cffDNA (e.g., less than 5% of total cfDNA). Additionally, it should be noted that if the window or sample does not have a 20% fetal fraction, the aforementioned bands may shift or move and may not be accurate at 10%, 40%, 50%, and 60%, respectively.

[0097] Although at least two windows are required to determine the allele balance trajectory, the number of windows that can be evaluated for the purposes of the disclosed methods is not particularly limited and can include multiple additional windows. Thus, in some embodiments, at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, or at least ten windows of the first sequence library are evaluated, thereby obtaining at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, or at least ten fetal fraction enriched sequence libraries, respectively, from which cffDNA sequences can be identified and isolated. In some embodiments, at least two windows of the sequence library are selected from: (i) sequences that are 0 to 145 nucleotides, (ii) sequences that are 0 to 150 nucleotides, (iii) sequences that are 0 to 155 nucleotides, (iv) sequences that are 0 to 160 nucleotides, (v) sequences that are 0 to 165 nucleotides, (vi) sequences that are 0 to 170 nucleotides, (vii) sequences that are 0 to 175 nucleotides, (viii) sequences that are 0 to 180 nucleotides, (ix) sequences that are 0 to 190 nucleotides, (x) sequences that are 0 to 195 nucleotides, (xi) sequences that are 0 to 200 nucleotides, and (xii) non-gated. In some embodiments, at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, or at least ten windows are selected from: (i) sequences that are 0 to 145 nucleotides, (ii) sequences that are 0 to 150 nucleotides, (iii) sequences that are 0 to 155 nucleotides, (iv) sequences that are 0 to 160 nucleotides, (v) sequences that are 0 to 165 nucleotides, (vi) sequences that are 0 to 170 nucleotides, (vii) sequences that are 0 to 175 nucleotides, (viii) sequences that are 0 to 180 nucleotides, (ix) sequences that are 0 to 190 nucleotides, (x) sequences that are 0 to 195 nucleotides, (xi) sequences that are 0 to 200 nucleotides, and (xii) non-gated.

[0098] FIG. 4 provides an overview of one exemplary embodiment of the process of in silico enrichment of the fetal fraction in a sequence library, followed by identification of chromosomal aneuploidies and genetic variants in parallel from a single sample using bioinformatics algorithms and post-processing, which may also be referred to herein as "callers."

[0099] A. Sample Processing and Computational Pipeline Generally, sample processing steps for carrying out the disclosed methods of parallel assessment of chromosomal aneuploidies and genetic variants can be carried out as described above in Section II ("Sample Preparation"), with further features now being developed.

[0100] The disclosed methods may include a computational pipeline that converts sequence data from the sequence library into useful output, including a determination of whether chromosomal aneuploidies or any genetic variants are present in the cffDNA. Optionally, additional useful output that may be provided includes, but is not limited to, a determination of fetal sex and other basic fetal statistics.

[0101] The computational pipeline may include a binary alignment map (BAM) process in which the collected DNA sample may be computationally reconstructed using short sequencing reads. If a reference genome is available to which the sequencing reads can be aligned, genome reconstruction may be facilitated. A sequence alignment tool may be used to map the short reads stored in the file to the reference genome. This generates a BAM file, and the specific gene sequence may be addressed in the next step.

[0102] The computational pipeline may also include depth and variant processing, during which specific genetic sequences may be identified and isolated to inform follow-up analysis targeting specific chromosomal aneuploidies and / or genetic variants. Based on the amount of initial DNA collected, specific portions of the collected DNA may be delineated and, optionally, assembled for use with analysis and detection of specific sequences of interest. Once delineated in the depth and variant processing step, specific callers and post-processing may be used to identify and assemble output information regarding chromosomal aneuploidies, genetic variants, and any other output into a results report. Results are generally reported, delivered, or communicated to the mother, father, the physician overseeing the pregnancy (i.e., the mother's obstetrician-gynecologist), or a combination thereof.

[0103] The depth of the DNA sample in the BAM file can be assessed using certain bioinformatics algorithms (i.e., "calling procedures"; described below). The caller used can determine the presence or absence of both chromosomal aneuploidies and genetic variants of interest. That is, these two goals can be achieved together (e.g., in a parallel manner) using the same prepared and processed BAM file. Thus, chromosomal aneuploidies can be detected using a chromosomal aneuploidy caller program, while other genetic variants can be performed in parallel using dedicated caller programs. Certain aspects of these computational steps are discussed in more detail below.

[0104] It should be understood that the present disclosure described above can be implemented in the form of control logic using computer software in a modular or integrated manner. Based on the disclosure and teachings provided herein, a person skilled in the art will know and understand other ways and / or methods for implementing the present disclosure using hardware and combinations of hardware and software.

[0105] Any of the software components, processes, or functions described in this application may be implemented as software code executed by a processor using any suitable computer language, such as, for example, Python, R, assembly language Java, JavaScript, C, C++, or Perl, using, for example, conventional or object-oriented techniques. The software code may be stored as a series of instructions, or commands, on a computer-readable medium, such as a random access memory (RAM), a read-only memory (ROM), a magnetic medium, such as a hard drive or a floppy disk, or an optical medium, such as a CD-ROM. Any such computer-readable medium may be present on or within a single computing device, or may be present on or within different computing devices within a system or network.

[0106] B. Detection of chromosomal aneuploidies For purposes of this disclosure, chromosomal aneuploidies that may be assessed or detected using the disclosed methods include, but are not limited to, monosomies (e.g., Turner syndrome), trisomies (e.g., Down syndrome, Edwards syndrome, Patau syndrome, trisomy 13, trisomy 18, trisomy 21), tetrasomy, polysomy X and / or Y, microdeletions and microduplications (such as chromosome 22q11.2 deletion syndrome), and pentasomy.

[0107] The present disclosure provides systems and methods for detecting chromosomal aneuploidies, either alone or in parallel with genetic variants / mutations of interest, that determine whether a chromosomal aneuploidy is present or absent in a given sample depending on sequencing depth. For the purposes of this disclosure, "depth" is defined as the ratio of the number of sequencing reads that overlap the site of interest to the size of the library, or the average number of times each base is measured in the library.

[0108] The depth observed in any given library prepared from maternal cfDNA sample correlates with fetal fraction, maternal copy number, and fetal copy number.If chromosomal aneuploidy (e.g., trisomy) exists, the depth in the target chromosome should be different from the sample with 23 chromosomes in a defined and predictable manner.For example, in trisomy, the depth in the target chromosome (e.g., chromosome 21) will increase compared to background.Figure 5 illustrates the principle underlying this measure.

[0109] In general, when detecting chromosomal aneuploidy (whether fetal or maternal) in a maternal sample containing some fraction of fetal cfDNA (e.g., fetal fraction), the presence of chromosomal aneuploidy can be identified based on the shift of detectable chromosomal aneuploidy regions or chromosomal aneuploidy chromosomes compared to known non-chromosomal aneuploidy regions or chromosomes. That is, depending on the actual fetal fraction, the analysis of each fragment (e.g., Equation 1 below) will result in a plottable result of cfDNA pregnancy depth versus cfDNA density. This shift can be statistically calculated or visualized, as shown in the center panel of Figure 5, where the background depth represents a comparator or aggregate of samples without chromosomal aneuploidy, and the shifted target depth represents samples containing trisomy, thus indicating the presence of chromosomal aneuploidy in the fetus (assuming that the pregnant mother does not exhibit said chromosomal aneuploidy). This deviation can be detected using a normalized distribution curve, and will become more pronounced as the fetal fraction of the sample increases through the enrichment process described herein.

[0110] In some embodiments, a depth call plot (shown in the bottom panel of FIG. 5) can be used to visualize and quantify the shift. As shown in the bottom panel of FIG. 5, the depth of a given sample (i.e., the shaded region) can be determined to fit within one of four known copy number (CN) curves (e.g., CN=1, CN=2, CN=3, and CN=4). In this exemplary illustration, the shaded distribution fits within the CN=3 curve, thus indicating the presence of a chromosomal aneuploidy (i.e., trisomy) with three copies of a chromosome. Various processing steps can be used to improve the distribution plot results and suppress noise in the data during analysis.

[0111] In some embodiments, detecting the presence or absence of chromosomal aneuploidy may include calculating a depth trajectory. A depth trajectory is the change over an observation window of read depth for any given gene sequence of interest. A depth trajectory may be calculated as a gradient of depth versus fetal fraction over an observation window, and may be visualized in several ways, as shown in FIG. 8. A depth trajectory in which the fetal fraction increases but decreases would indicate that the fetus has fewer copies of a gene (or chromosome) than the mother. A depth trajectory in which the fetal fraction increases but remains constant would indicate that the fetus and the mother have the same copy number of a gene (or chromosome). And a depth trajectory in which the fetal fraction increases but increases would indicate that the fetus has more copies of a gene (or chromosome) than the mother. It should be noted that while depth trajectories are useful for determining chromosome numbers for the purpose of detecting the presence or absence of chromosomal aneuploidy, depth trajectories can also be used to detect the presence or absence of certain gene variants, such as copy number abnormalities.

[0112] During the analysis of chromosome depth for any given sample, it may be necessary to account for and normalize for GC-bias. GC content (or guanine-cytosine content) is the percentage of nitrogenous bases on a DNA molecule that are either guanine or cytosine (out of four different possibilities, including adenine and thymine). High GC content can skew the results and result in high levels of noise. For example, in the context of Figure 5C, increased noise will broaden the width of the data bands and the corresponding copy number hypotheses (black lines), and the wider these distributions become, the more difficult it will be to accurately interpret the true copy number levels. Correct normalization reduces depth discrepancies in high-noise samples, thereby reducing the effects of GC bias and improving chromosomal aneuploidy calling.

[0113] Furthermore, as shown in FIG. 6, a three-fold normalization control of the variation caused by 1) GC bias, 2) sample background, and 3) hybridization probe capture (if appropriate, i.e., in embodiments utilizing hybrid probes) can be used across the sampling data to improve the distribution plot of the sampling data. As provided in FIG. 6, the top set of distribution plots shows the raw depth data without normalization, the middle set of distribution plots shows the improved distribution plots after using GC bias normalization, and the bottom set of distribution plots is even more improved after achieving the second (sample background) and third (hybridization probe capture) normalization data processing steps. Thus, the three-fold normalization control can improve the distribution plot of the sampling data and can be useful for certain disclosed embodiments or certain samples. Once normalized, these distribution plots can be compared to the model's expected values ​​to draw conclusions about the presence or absence of chromosomal aneuploidy, as illustrated in FIG. 7.

[0114] FIG. 7 shows that the normalized depth plot fits model predictions of the occurrence of chromosomal aneuploidy, which can be used to decipher the assembled and optionally normalized sample distribution. A depth-fit model can be assembled using conventional known chromosomal aneuploidy distributions for use in a comparison step to decipher whether the actual assembled and optionally normalized distribution matches one or more of the assembled known models. As shown in FIG. 7, the normalized depth distributions shown in gray can be set against known distribution curves reflecting one, two, or three copies (in that order, from left to right) for chromosomes 13, 18, 21, and X. A particular curve fit can be determined using maximum likelihood to select the most likely fetal copy number. The maximum likelihood fit provides a match for a particular call, so that a conclusion can be drawn regarding the presence or absence of chromosomal aneuploidy in the analyzed sample.

[0115] Based on the expected difference in depth that would be observed if a chromosomal aneuploidy were present in a sample, the chromosomal aneuploidy caller can be designed to select the pair of maternal and fetal copy numbers that produces the highest likelihood of chromosomal aneuploidy on a normal distribution. To this end, the following equation was developed to determine the depth of a given chromosomal aneuploidy:

number

[0116] This caller demonstrated both very high sensitivity and specificity for detecting chromosomal aneuploidies of autosomes and sex chromosomes, as well as fetal gender call values. The following examples provide further details regarding the performance of the chromosomal aneuploidy caller.

[0117] After completion of the screening disclosed herein, a physician may choose to perform further evaluations, such as extended aneuploidy analysis (EAA), which analyzes even more numbered chromosome pairs to provide further insight into the health of the pregnancy. Thus, in some embodiments, the disclosed methods of determining the presence or absence of chromosomal aneuploidy may further include EAA.

[0118] C. Genetic Variant Detection Generally, the genetic variants (e.g., genetic mutations) detected as part of the disclosed methods are genetic variants, markers, or mutations associated with a particular genetic or inherited disease, condition, or trait. Genetic variants can include single nucleotide variations (SNVs), pathogenic or non-pathogenic single nucleotide polymorphisms (SNPs), insertions and deletions (indels), substitution mutations, or single gene copy number variants.

[0119] A genetic variant may be associated with two or more diseases, conditions, or traits. A genetic variant may appear as a variant in a polynucleotide, such as at least about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30, 40, 50 or more sequence differences between wild-type (i.e., non-mutated or non-associated with a disease or condition) genes or loci. Non-limiting examples of types of genetic variants that can be detected using the disclosed methods include, but are not limited to, single nucleotide polymorphisms (SNPs), deletion / insertion polymorphisms (DIPs), minute copy number variants (CNVs), short tandem repeats (STRs), restriction fragment length polymorphisms (RFLPs), single sequence repeats (SSRs), variable number tandem repeats, (VNTRs), random amplified polymorphic DNA (RAPDs), amplified fragment length polymorphisms, retrotransposon-based insertion polymorphisms, sequence-specific amplified polymorphisms, and inherited epigenetic modifications (e.g., DNA methylation).

[0120] For purposes of the disclosed methods, at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 125, 150, 175, 200, 225, 250, 275, 300, 325, 350, 375, 400, 425, 450, 460, 470, 480, 490, 500, 510, 520, 530, 540, 550, 560, 570, 580, 590, 600, 610, 620, 630, 640, 650, 660, 670, 680, 690, 700, 710, 720, 730, 740, 750, 760, 770, 780, 790, 800, 810, 820, 830, 840, 850, 860, 870, 880, 890, 900, 910, 920, 930, 940, 950, 960, 970, 980, 990, 1000, 1010, 1020, 1030, 1040, 1050, 1060, 1070, 1080, 109 The presence or absence of 75, 500, 525, 550, 575, 600, 625, 650, 675, 700, 725, 750, 775, 800, 825, 850, 875, 900, 925, 950, 975, or 1000 or more different genetic variants can be detected in a single assay in parallel with detecting the presence or absence of chromosomal aneuploidy. In some embodiments, the methods are capable of detecting the presence or absence of at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 105, 110, 115, 120, 125, 130, 135, 140, 145, 150, 155, 160, 165, 170, 175, 180, 185, 190, 195, or 200 or more genetic variants associated with a disease, condition, or trait in parallel.

[0121] In general, the presence of a genetic variant of the type detected by the disclosed methods is associated with about, less than, or more than about a 1%, 5%, 10%, 15%, 20%, 25%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, 100%, 200%, 300%, 400%, 500% or more increase in risk of having or developing a disease, condition, or trait. In some embodiments, the presence of the genetic variant increases, less than, or more than about a 1-fold, 2-fold, 3-fold, 4-fold, 5-fold, 6-fold, 7-fold, 8-fold, 9-fold, 10-fold, 25-fold, 50-fold, 100-fold, 500-fold, 1000-fold, 10000-fold or more increase in risk of having or developing a disease, condition, or trait. In some embodiments, the presence of a genetic variant is about, or less than about, about 0.1, 0.05, 10 -3 , 10 -4 , 10 -5 , 10 -6 , 10 -7 , 10 -8 , 10 -9 , 10 -10 , 10 -11 , 10 -12 , 10 -13 , 10 -14 , 10 -15 The term "p-value" refers to a statistically significant increase in the risk of having or developing a disease, condition, or trait by any statistically significant amount, such as an increase having a p-value of 0.1 or less.

[0122] For purposes of this disclosure, genetic diseases that may be evaluated or detected by determining the presence or absence of a genetic variant include, but are not limited to, 21-hydroxylase deficiency, ABCC8-associated hyperinsulinism, ARSACS, achondroplasia, achromatopsia, adenosine monophosphate deaminase 1, agenesis of corpus callosum with neuropathy, alkaptonuria, alpha-1-antitrypsin deficiency, alpha-mannosidosis, alpha-sarcoglycanopathy, alpha-sarcopenia ... racemia, angiotensin II receptor I type Alzheimer's, apolipoprotein E genotype Alzheimer's, argininosuccinic aciduria, aspartylglycosaminuria, ataxia with vitamin E deficiency, ataxia-telangiectasia, autoimmune polyendocrine syndrome type 1, BRCA1 hereditary breast / ovarian cancer, BRCA2 hereditary breast / ovarian cancer, Bardet-Biedl syndrome, Best vitelliform macular dystrophy, beta-sarcoglycanopathy, beta-thalassemia, Biotinidase deficiency, Blau syndrome, Bloom syndrome, CFTR-related disorders, CLN3-related neuronal ceroid-lipofuscinosis, CLN5-related neuronal ceroid-lipofuscinosis, CLN8-related neuronal ceroid-lipofuscinosis, Canavan disease, carnitine palmitoyltransferase IA deficiency, carnitine palmitoyltransferase II deficiency, cartilage-hair hypoplasia, cavernous vascular malformation, choroideremia, Cohen syndrome, congenital cataracts, facial dysmorphism, and congenital neuropathy Glycosylation disorder type Ia, congenital glycosylation disorder type Ib, congenital Finnish nephrotic syndrome, Crohn's disease, cystinosis, DFNA9 (COCH), diabetes mellitus and hearing loss, early-onset primary dystonia (DYTI), Herlitz-Pearson junctional epidermolysis bullosa, FANCC-associated Fanconi anemia, FGFR1-associated craniosynostosis, FGFR2-associated craniosynostosis, FGFR3-associated craniosynostosis, factor V Leiden thrombophilia, factor V R2 factor mutation thrombophilia, factor XI deficiency, factor XIII deficiency, familial adenomatous polyposis, familial dysautonomia, familial hypercholesterolemia type B, familial Mediterranean fever, free sialic acid storage disorder, frontotemporal dementia with parkinsonism-17, fumarase deficiency, GJB2-related DFNA3 nonsyndromic hearing loss and deafness,GJB2-related DFNB1 nonsyndromic hearing loss and deafness, GNE-related myopathy, galactosemia, Gaucher disease, glucose-6-phosphate dehydrogenase deficiency, glutaric acidemia type 1, glycogen storage disease type 1a, glycogen storage disease type Ib, glycogen storage disease type II, glycogen storage disease type III, glycogen storage disease type V, Gracil syndrome, HFE-related hereditary hemochromatosis, Halder AIM, hemoglobin S beta-thalassemia, hereditary fructose intolerance, hereditary pancreatitis, hereditary thymine-uraciluria, hexosaminidase A deficiency, hidrotic ectodermal dysplasia 2, cystathionine beta synthase deficiency Causes homocystinuria, hyperkalemic periodic paralysis type 1, hyperornithinemic-hyperammonemic-homocitrinemia syndrome, primary type 1 hyperoxaluria, primary type 2 hyperoxaluria, achondroplasia, hypokalemic periodic paralysis type 1, hypokalemic periodic paralysis type 2, hypophosphatasia, infantile myopathy and lactic acidosis (fatal and non-fatal forms), isovaleric acidemia, Krabbe disease, LGMD2I, Leber's hereditary optic neuropathy, French-Canadian Leigh encephalopathy, long-chain 3-hydroxyacyl-CoA dehydrogenase deficiency, M ELAS, MERRF, MTHFR deficiency, MTHFR thermolabile variants, MTRNR1-associated hearing loss and deafness, MTTS1-associated hearing loss and deafness, MYH-associated polyposis, Maple Syrup Urine Disease Type 1A, Maple Syrup Urine Disease Type 1B, McCune-Albright syndrome, Medium-chain acyl-Coenzyme A dehydrogenase deficiency, Macrocephalic leukoencephalopathy with subcortical cysts, Metachromatic leukodystrophy, Mitochondrial cardiomyopathy, Mitochondrial DNA-associated Leigh encephalopathy and NARP, Mucolipidosis type IV, Mucopolysaccharidosis type I, Mucopolysaccharidosis type IIIA, Mucopolysaccharidosis syndrome type VII, multiple endocrine neoplasia type 2, muscle-eye-brain disease, neurological phenotype nemaline myopathy, Niemann-Pick disease due to sphingomyelinase deficiency, Niemann-Pick disease type C1, Nijmegen chromosomal instability syndrome, PPT1-associated neuronal ceroid-lipofuscinosis, PROP1-associated pituitary hormone deficiency, Pallister-Hall syndrome, paramyotonia congenita, Pendred syndrome, peroxisomal bifunctional enzyme deficiency, pervasive developmental disorder, phenylalanine hydroxylase deficiency, plasminogen activator inhibitor 1, autosomal recessive polycystic kidney disease,These include prothrombin G20210A thrombophilia, pseudovitamin D deficiency rickets, pyknotic dysostosis, autosomal recessive retinitis pigmentosa Bothnia type, Rett syndrome, chondrodysplasia punctata type 1, short-chain acyl-CoA dehydrogenase deficiency, Shwachman-Diamond syndrome, Sjögren-Larsson syndrome, Smith-Lemli-Opitz syndrome, spastic paraplegia 13, sulfate transporter-related skeletal dysplasia, TFR2-related hereditary hemochromatosis, TPP1-related neuronal ceroid-lipofuscinosis, thanatophoric skeletal dysplasia, transthyretin amyloidosis, tricyclic enzyme deficiency, tyrosine hydroxylase deficiency DRD, hypertyrosinemia type 1, Wilson's disease, X-linked juvenile retinoschisis, cystic fibrosis, spinal muscular atrophy (SMA), hemoglobinopathies, and Zellweger syndrome spectrum.

[0123] For the purposes of the disclosed methods, the identification or detection of genetic variants can be performed using the in silico moving window analysis described herein to establish a trajectory based on allelic balance (when evaluating genetic variants involving SNPs, Indels, or other point mutations) or depth (when evaluating genetic variants involving copy number changes) over the analyzed window. This analysis can be particularly useful for detecting recessive conditions, traits, or diseases. As described above, this process can include size exclusion based on the read length of sequences in at least two windows of the sequence library, thereby obtaining at least two fetal fraction enriched sequence libraries. Comparing the allelic balance in each window allows the calculation of the allelic balance trajectory between the various fetal fraction enriched sequence libraries. The allelic balance trajectory is the change over the observation window in the percentage of allelic balance of any given genetic sequence of interest. The allelic balance trajectory can be calculated as the slope of allelic balance versus fetal fraction over the observation window and can be visualized in several ways, as shown in FIG. 3.

[0124] Allele balance trajectories can be used to identify heterozygous and homozygous mutations in cfDNA libraries. For example, a single point in the trajectory can be converted into a band pattern plot based on the allele balance in a given window, where the y-axis displays the percentage of cfDNA in the sample that has a given allele of a particular gene or nucleic acid sequence of interest (e.g., 0%, 10%, 20%, 30%, 40%, 50%, 60%), and the x-axis displays the wild-type sequence or the different alleles of the gene or nucleic acid of interest (i.e., reference alleles or alt alleles) that correspond to mutations / variants associated with a particular disease, condition, or trait (e.g., different known mutations in the CFTR gene associated with cystic fibrosis). If the fetal fraction in a window or sample is, for example, 20%, then the 10% band on the y-axis corresponds to fetuses that are carriers from the DNA of the biological father or new mutations in the fetus. The 40% band on the y-axis corresponds to a fetus that is negative (i.e., homozygous reference) for the mutation / variant of the gene or sequence of interest and whose mother is heterozygous (i.e., carrier). The 50% band on the y-axis corresponds to a fetus that is a carrier from the biological mother's DNA, or a carrier of the biological father's DNA if both the mother and father are carriers with the same alt allele. The 60% band on the y-axis corresponds to a fetus that is homozygous positive for the mutation / variant in the gene or sequence of interest. As mentioned above, the bands discussed above (i.e., 10%, 40%, 50%, and 60%) are not fixed and their positions will vary based on the fetal fraction. For example, if the fetal fraction were instead 10% (as opposed to 20% in the example above), the band values ​​would change from 10%, 40%, 50%, and 60% to 5%, 45%, 50%, and 55%, respectively.

[0125] The allele balance trajectory incorporates this static information from each of the observation windows and will necessarily have different fetal fractions. Thus, the trajectory may depend on a window containing the 20% fetal fraction mentioned above, a second window containing a 10% fetal fraction, and optionally, one, two, three, four, five, six, seven, eight, nine, or ten or more windows containing varying fetal fractions.

[0126] Additionally or alternatively, the specific caller of the genetic variant of interest may rely on copy number assessment (as described above with respect to chromosomal aneuploidy), depth analysis, or other forms of detection known in the art. For example, depth trajectories can be used to detect the presence or absence of copy number variants, such as SMA1, RHD, HBA1, and HBA2 copy number variants, all of which are associated with specific genetic diseases. In some embodiments, the depth trajectory may have a negative slope (indicating fewer copies in the fetus), a nearly flat slope (indicating the same copy number between the fetus and the mother), or a positive slope (indicating more copies in the fetus), and such slopes may be based on 1, 2, 3, 4, 5, 6, 7, 8, 9, or 10 or more windows that include varying fetal fractions.

[0127] In some embodiments, a caller of a particular state may depend on detecting the presence or absence of a "diffbase". In some embodiments, a caller of a particular state may depend on detecting the presence or absence of a substitution (e.g., SNV) from a wild type sequence. In some embodiments, a caller of a particular state may depend on detecting the presence or absence of a single nucleotide polymorphism (SNP). In some embodiments, a caller of a particular state may depend on detecting the presence or absence of one or more insertions or deletions (INDELs). When multiple SNVs, diffbases, SNPs, or combinations thereof are associated with a given state, pooling or merging detection signals across even a small number of SNVs, diffbases, SNPs, INDELs, or combinations thereof (e.g., <3, <4, <5, <6, <7, <8, <9, <10, <11, <12, <13, <14, <15) can provide improved separation between genotypes. Thus, in some embodiments, the caller of a particular status may depend on the detection of the presence or absence of 1, 2, 3, 4, 5, 6, 7, 8, 9, or 10, 11, 12, 13, 14, or 15 or more SNVs, diffbases, SNPs, INDELs, or combinations thereof.

[0128] For example, in detecting alpha thalassemia, the disclosed methods may utilize a caller that detects the presence or absence of double cis mutations in HBA1 and HBA2, which are the most common cause of the condition. Thus, the caller can detect a consensus copy number signal obtained from multiple probes in a region of interest, such as a double deletion region.

[0129] Thus, the use of the disclosed method allows calling of gene variants of interest with a single sample and in parallel with the detection of chromosomal aneuploidy.In fact, this method can even identify whether a fetus is homozygous or heterozygous for a given gene variant of interest.Furthermore, in some embodiments where the mother and father have different alt alleles, it can be determined whether the fetus has acquired a particular variant from the mother, the father, or both.This is a new and useful way to establish the presence or absence of gene variants / mutations from maternal samples.

[0130] D. Noise reduction As described above and further shown in the examples, the disclosed method and system can significantly reduce noise in cfDNA data, improving the performance of assays used to detect genetic variants and chromosomal aneuploidies.Due to the low level of cffDNA in most biological samples obtained from pregnant women, high levels of background noise from conventional processing and detection methods can make samples unusable, uninterpretable, or both.Therefore, the disclosed method of noise reduction represents a new and useful method to improve conventional non-invasive prenatal screening (NIPS).

[0131] Additionally, the disclosure provides a method of reducing background noise from redundant genetic material in non-invasive prenatal screening (NIPS), comprising: (i) obtaining a biological sample from a pregnant woman, where the biological sample comprises cell-free DNA (cfDNA); and (ii) processing the cfDNA used for NIPS, where processing comprises enriching the biological sample for cell-free fetal DNA (cffDNA), in silico processing of the cfDNA, or a combination thereof.

[0132] In some embodiments, methods for reducing noise include both enriching a biological sample for cell-free fetal DNA (cffDNA) and in silico processing of cfDNA.

[0133] For the purpose of reducing noise, enrichment of biological sample for cffDNA can include any of the disclosed methods of physical isolation or enrichment of fetal fraction.For example, in some embodiments, enrichment of biological sample for cffDNA can include obtaining a biological sample comprising cell-free DNA (cfDNA) from a pregnant woman, where cfDNA comprises cffDNA and cell-free maternal DNA (cfmDNA), extracting cfDNA from the biological sample, and subjecting the extracted cfDNA to a size exclusion process, where the size exclusion process has a cutoff size of about 150 nucleotides, about 155 nucleotides, about 160 nucleotides, about 165 nucleotides, about 170 nucleotides, about 175 nucleotides, or about 180 nucleotides, thereby generating nucleic acid enriched in cffDNA.

[0134] Similarly, for the purpose of reducing noise, in silico processing can include any of the disclosed methods of analyzing sequence libraries or sequence library data to focus any analysis of genetic variants or chromosomal aneuploidies on the fetal fraction of the sample.For example, in some embodiments, in silico processing can include: sequencing a cfDNA sample that includes cell-free fetal (cffDNA) and cell-free maternal DNA (cfmDNA) to prepare a sequence library; performing a read length-based analysis, in which the allele balance of the nucleic acid sequence of interest is established in at least two windows of the sequence library; and establishing a trajectory based on the allele balance of the at least two windows, where the trajectory indicates the percentage of alleles present in the sample that includes the nucleic acid sequence of interest.

[0135] Noise reduction may further include normalization to control for GC-bias, sample background, hybridization probe capture, or a combination thereof. In general, normalization may be "median normalization". In other words, probe read depth may be divided by the median across probes with similar GC content, and then divided by the quartile mean across samples and probes with an estimated copy number of 2 in mothers and fetuses.

[0136] Hybridization probe capture can be problematic because variant-containing DNA fragments and overlapping capture probes are not captured efficiently, reducing the allelic balance of the mutant alleles. However, capture bias is often reproducible, and in such cases it can be learned and corrected for using the following formula:

number

[0137] Corrections and normalization for hybridization probe capture can aid in variant calling more generally, but are particularly useful in ensuring correct indel calling.

[0138] The following examples are given to illustrate the disclosed sample preparations and methods, however, it should be understood that the invention is not limited to the specific embodiments or details set forth in these examples. EXAMPLES

[0139] Example 1 - Performance of chromosomal aneuploidy caller The disclosed chromosomal aneuploidy caller is based on the sequence read depth described above.To establish the feasibility of this approach, 110 feasibility samples were analyzed and compared against a standard accepted chromosomal aneuploidy detection system (Myriad PREQUEL™ Prenatal Screen).The detectable chromosomal aneuploidies in the samples and their respective control calls are shown in the following table: [Table 1]

[0140] The analysis method based on the depth of disclosure provided the following results: ●For autosome +22q Sensitivity = 100% (CI: 89.95-100%) ○Specificity=99.75%(CI:98.59~99.96%) One false-positive mosaic monosomy 21 call In sex chromosome aneuploidy Sensitivity = 100% (CI: 63.06-100%) ○Specificity=100%(CI:96.41~100%) ● Fetal gender call ○ 100% agreement with control studies

[0141] Only one of 110 samples failed at a low depth (0.9% rerun rate).

[0142] Example 2 - Performance of SNV / Indel (i.e., genetic variant) callers To validate the performance of the SNV / indel caller system disclosed herein, fifteen (15) contrived mixtures from five prenatal pairs were used. The sensitivity and specificity of the genetic regions of interest (ROIs) alone and in combination with a set of SNVs known to have high variability within the population (i.e., dbSNP) are shown in the table below: [Table 2]

[0143] This initial performance was established without the use of the physical enrichment process described herein, and it is expected that enriching the fetal fraction and optimizing the variant filter parameters will further improve performance.

[0144] Additionally, the disclosed single-gene SNV / indel caller met the performance requirements for five unique cfDNA samples with FFs ranging from 5.8 to 16%. The results are shown in the table below. [Table 3]

[0145] In this performance evaluation, the best performance was observed when both enrichment and bias correction based on physical size exclusion were implemented together.

[0146] Example 3 - SMA Caller Performance Analysis Spinal muscular atrophy (SMA) is a genetically inherited condition that is commonly included in prenatal screening. However, due to the high degree of homology between the SMN1 and SMN2 genes, SMA calling is difficult. These genes differ at only a few positions (most notably exon 7), and SMA carrier / affected status depends only on SMN1 copy number.

[0147] The disclosed system evaluated the presence or absence of multiple bases (up to 44 diffbases) to ensure a correct call. As shown in the table below, the SMA caller was highly accurate, sensitive, and specific. [Table 4]

[0148] For the purposes of this evaluation, carrier fetuses were considered healthy.

[0149] Example 4 - Alpha Thalassemia Caller Performance Analysis Alpha thalassemia is a blood disorder that reduces the production of hemoglobin. It is a genetically inherited condition that is commonly included in prenatal screening. The disclosed system assessed the presence or absence of double cis mutations in HBA1 and HBA2, which are the most common cause of the condition. More specifically, consensus copy number signals were obtained from multiple probes in the double deletion region. As shown in the table below, the Alpha Thalassemia caller was highly accurate, sensitive, and specific. [Table 5]

[0150] For the purposes of this evaluation, carrier fetuses were considered healthy.

[0151] Example 5 - RhD Caller Performance Analysis If a pregnant woman is D(-) and the fetus is D(+), hemolytic disease may result when maternal blood is exposed to fetal blood. This condition is commonly included in prenatal screening. The most common cause of RhD(-) is a whole-gene deletion for RHD. Therefore, we developed a caller based on 221 reliable diffbases to assess copy number. As shown in the table below, the RhD caller was highly accurate, sensitive, and specific. [Table 6]

[0152] ***** All patents and publications mentioned in this specification are indicative of the level of those skilled in the art to which this disclosure pertains. All patents and publications are herein incorporated by reference to the same extent as if each individual publication was specifically and individually indicated to be incorporated by reference.

[0153] The present technology is not limited with respect to the specific embodiments described in this application, which are intended as single illustrations of individual aspects of the technology. As will be apparent to those skilled in the art, many modifications and variations of the present technology can be made without departing from its spirit and scope. In addition to those listed herein, functionally equivalent methods and devices within the scope of the present technology will be apparent to those skilled in the art from the foregoing description. Such modifications and variations are intended to fall within the scope of the present technology. It is understood that the present technology is not limited to specific methods, reagents, compounds, compositions, or systems, which may, of course, vary. It is also understood that the terminology used herein is for the purpose of describing specific embodiments only, and is not intended to be limiting.

Claims

1. 1. A method for preparing a biological sample containing an enriched fetal fraction, comprising: (a-1) obtaining a biological sample containing cell-free DNA (cfDNA) from a pregnant woman; (b-1) extracting cfDNA from the biological sample; (c-1) preparing a library of cfDNA fragments to obtain a cfDNA library; (d-1) separating the cfDNA fragments in the cfDNA library by size to retain only cfDNA fragments that are less than about 150 nucleotides, about 155 nucleotides, about 160 nucleotides, about 165 nucleotides, about 170 nucleotides, about 175 nucleotides, about 180 nucleotides, about 185 nucleotides, about 190 nucleotides, about 195 nucleotides, or about 200 nucleotides in length; (e-1) sequencing the retained cfDNA fragments to obtain a first sequence library; (f-1) identifying (i) sequences of cell-free fetal DNA (cffDNA) and (ii) sequences of cell-free maternal DNA (cfmDNA) present in at least two windows of the first sequence library based on read length; (g-1) isolating sequences of the cffDNA from each of the at least two windows of the sequence library, thereby obtaining at least two fetal fraction enriched sequence libraries; or (a-2) obtaining a biological sample containing cell-free DNA (cfDNA) from a pregnant woman; (b-2) extracting cfDNA from the biological sample; and (c-2) separating the cfDNA fragments in the extracted sample from (b-2) to retain only the cfDNA fragments that are less than about 150 nucleotides in length, about 155 nucleotides in length, about 160 nucleotides in length, about 165 nucleotides in length, about 170 nucleotides in length, about 175 nucleotides in length, about 180 nucleotides in length, about 185 nucleotides in length, about 190 nucleotides in length, about 195 nucleotides in length, or about 200 nucleotides in length; (d-2) preparing a cfDNA library from the isolated cfDNA fragments from (c-2); (e-2) sequencing the cfDNA library to obtain a first sequence library; (f-2) identifying (i) sequences of cell-free fetal DNA (cffDNA) and (ii) sequences of cell-free maternal DNA (cfmDNA) present in at least two windows of the first sequence library based on read length; (g-2) isolating sequences of the cffDNA from each of the at least two windows of the sequence library, thereby obtaining at least two fetal fraction enriched sequence libraries.

2. 2. The method of claim 1, wherein separating the cfDNA fragments enriches the fetal fraction in the biological sample by about 1.1 fold, about 1.2 fold, about 1.3 fold, about 1.4 fold, about 1.5 fold, about 1.6 fold, about 1.7 fold, about 1.8 fold, about 1.9 fold, or about 2.0 fold.

3. 3. The method of claim 1 or 2, wherein isolating the cffDNA sequences from the at least two windows of the first sequence library enriches the fetal fraction in the biological sample by about 1.1 fold, about 1.2 fold, about 1.3 fold, about 1.4 fold, about 1.5 fold, about 1.6 fold, about 1.7 fold, about 1.8 fold, about 1.9 fold, about 2.0 fold, about 2.1 fold, about 2.2 fold, about 2.3 fold, about 2.4 fold, about 2.5 fold, about 2.6 fold, about 2.7 fold, about 2.8 fold, about 2.9 fold, about 3.0 fold, about 3.1 fold, about 3.2 fold, about 3.3 fold, about 3.4 fold, or about 3.5 fold.

4. 4. The method of claim 1, wherein separating the cfDNA fragments comprises electrophoresis.

5. The method according to any one of claims 1 to 4, wherein at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, or at least ten windows of the first sequence library are evaluated to identify and isolate cffDNA sequences, thereby obtaining at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, or at least ten fetal fraction enriched sequence libraries, respectively.

6. 6. The method of any one of claims 1 to 5, further comprising identifying and separating cffDNA from cfmDNA by comparing sequence reads of cffDNA and cfmDNA in the first sequence library to a reference genome, demultiplexing sequence reads from the first library, removing duplicate sequences from the first sequence library, or a combination thereof.

7. The method of any one of claims 1 to 6, further comprising evaluating the at least two fetal fraction enriched sequence libraries for the presence of one or more genetic mutations.

8. The one or more genetic mutations are associated with 21-hydroxylase deficiency, ABCC8-associated hyperinsulinism, ARSACS, achondroplasia, achromatopsia, adenosine monophosphate deaminase 1, agenesis of corpus callosum with neuropathy, alkaptonuria, alpha-1-antitrypsin deficiency, alpha-mannosidosis, alpha-sarcoglycanopathy, alpha-thalassemia, angiotensin II receptor type I Alzheimer's, apolipoprotein E Genetic Alzheimer's disease, argininosuccinic aciduria, aspartylglycosaminuria, ataxia with vitamin E deficiency, ataxia-telangiectasia, autoimmune polyendocrine syndrome type 1, BRCA1 hereditary breast / ovarian cancer, BRCA2 hereditary breast / ovarian cancer, Bardet-Biedl syndrome, Best vitelliform macular dystrophy, beta-sarcoglycanopathy, beta-thalassemia, biotinidase deficiency, Blau syndrome, Bloom syndrome , CFTR-related disorders, CLN3-related neuronal ceroid-lipofuscinosis, CLN5-related neuronal ceroid-lipofuscinosis, CLN8-related neuronal ceroid-lipofuscinosis, Canavan disease, carnitine palmitoyltransferase IA deficiency, carnitine palmitoyltransferase II deficiency, cartilage-hair hypoplasia, cavernous vascular malformation, choroideremia, Cohen syndrome, congenital cataracts, facial dysmorphisms, and neurological disorders, congenital glycosylation disorder type Ia, Congenital glycosylation disorder type Ib, Congenital Finnish nephrotic syndrome, Crohn's disease, cystinosis, DFNA9 (COCH), diabetes mellitus and hearing loss, Early-onset primary dystonia (DYTI), Herlitz-Pearson junctional epidermolysis bullosa, FANCC-associated Fanconi anemia, FGFR1-associated craniosynostosis, FGFR2-associated craniosynostosis, FGFR3-associated craniosynostosis, Factor V Leiden thrombophilia, Factor V R2 factor mutation thrombophilia, factor XI deficiency, factor XIII deficiency, familial adenomatous polyposis, familial dysautonomia, familial hypercholesterolemia type B, familial Mediterranean fever, free sialic acid storage disorder, frontotemporal dementia with parkinsonism-17, fumarase deficiency, GJB2-related DFNA3 non-syndromic hearing loss and deafness, GJB2-related DFNB1 non-syndromic hearing loss and deafness, GNE-related myopathy, galactosemia, Gaucher disease,Glucose-6-phosphate dehydrogenase deficiency, glutaric acidemia type 1, glycogen storage disease type 1a, glycogen storage disease type 1b, glycogen storage disease type II, glycogen storage disease type III, glycogen storage disease type V, Graeschl syndrome, HFE-associated hereditary hemochromatosis, Halder's AIM, hemoglobin S beta-thalassemia, hereditary fructose intolerance, hereditary pancreatitis, hereditary thymine-uraciluria, hexosaminidase A deficiency, hidrotic ectodermal dysplasia 2, homocystinuria caused by cystathionine beta synthase deficiency, hyperkalemic periodic paralysis type 1, hyperornithinemiemia-hyperammonemia Myopathy-homocitrinemia syndrome, primary hyperoxaluria type 1, primary hyperoxaluria type 2, achondroplasia, hypokalemic periodic paralysis type 1, hypokalemic periodic paralysis type 2, hypophosphatasia, infantile myopathy and lactic acidosis (fatal and non-fatal forms), isovaleric acidemia, Krabbe disease, LGMD2I, Leber's hereditary optic neuropathy, French-Canadian Leigh encephalopathy, long-chain 3-hydroxyacyl-CoA dehydrogenase deficiency, MELAS, MERRF, MTHFR deficiency, MTHFR thermolabile variants, MTRNR1-related hearing loss loss and hearing loss, MTTS1-associated hearing loss and hearing loss, MYH-associated polyposis, maple syrup urine disease type 1A, maple syrup urine disease type 1B, McCune-Albright syndrome, medium-chain acyl-coenzyme A dehydrogenase deficiency, macrocephalic leukoencephalopathy with subcortical cysts, metachromatic leukodystrophy, mitochondrial cardiomyopathy, mitochondrial DNA-associated Leigh encephalopathy and NARP, mucolipidosis type IV, mucopolysaccharidosis type I, mucopolysaccharidosis type IIIA, mucopolysaccharidosis type VII, multiple endocrine neoplasia type 2, muscle-eye-brain disease, neurological phenotype nemaline myopathy, Niemann-Pick disease due to sphingomyelinase deficiency, Niemann-Pick disease type C1, Nijmegen chromosomal instability syndrome, PPT1-associated neuronal ceroid-lipofuscinosis, PROP1-associated pituitary hormone deficiency, Pallister-Hall syndrome, paramyotonia congenita, Pendred syndrome, peroxisomal bifunctional enzyme deficiency, pervasive developmental disorder, phenylalanine hydroxylase deficiency, plasminogen activator inhibitor 1, autosomal recessive polycystic kidney disease, prothrombin G20210A thrombophilia, pseudovitamin D deficiency rickets, pyknotic dysostosis,8. The method according to claim 7, which causes at least one condition selected from autosomal recessive retinitis pigmentosa Bothnia type, Rett syndrome, chondrodysplasia punctata type 1, short-chain acyl-CoA dehydrogenase deficiency, Shwachman-Diamond syndrome, Sjogren-Larsson syndrome, Smith-Lemli-Opitz syndrome, spastic paraplegia 13, sulfate transporter-related skeletal dysplasia, TFR2-related hereditary hemochromatosis, TPP1-related neuronal ceroid-lipofuscinosis, thanatophoric skeletal dysplasia, transthyretin amyloidosis, trifunctional protein deficiency, tyrosine hydroxylase deficiency DRD, hypertyrosinemia type 1, Wilson's disease, X-linked juvenile retinoschisis, cystic fibrosis, spinal muscular atrophy (SMA), hemoglobinopathies, and Zellweger syndrome spectrum.

9. 9. The method of any one of claims 1 to 8, further comprising evaluating the biological sample comprising cfDNA for the presence of chromosomal aneuploidy.

10. 10. The method of claim 9, wherein the chromosomal aneuploidy is selected from monosomy, trisomy, tetrasomy, pentasomy, microdeletion, microduplication, and mosaic versions of monosomy, trisomy, tetrasomy, and pentasomy.

11. 1. A method for detecting in parallel the presence or absence of a chromosomal aneuploidy and the presence or absence of at least one genetic variant in a single maternal sample, comprising: (i) obtaining a biological sample from a pregnant woman, the biological sample comprising cell-free DNA (cfDNA); (ii) preparing a cfDNA library; (iii) sequencing the cfDNA library to generate a sequence library; (iv) detecting the presence or absence of said chromosomal aneuploidy and the presence or absence of said at least one genetic variant in said single maternal sample; 4. The method of claim 1, further comprising: detecting the presence or absence of said chromosomal aneuploidy and the presence or absence of said at least one genetic variant in said single maternal sample after (a) enriching said cfDNA library to increase the fetal fraction, (b) enriching said sequence library to increase the fetal fraction, or (c) a combination thereof, thereby increasing the fetal fraction of said single maternal sample by at least 1.1 fold, at least 1.2 fold, at least 1.3 fold, at least 1.4 fold, or at least 1.5 fold.

12. The method of claim 11 , wherein the biological sample is blood or plasma.

13. 13. The method of claim 11 or 12, wherein the cfDNA library is enriched to increase the fetal fraction and the sequence library is enriched to increase the fetal fraction.

14. 14. The method of any one of claims 11-13, wherein enriching the fetal fraction of the cfDNA library comprises removing any DNA fragments from the cfDNA library that are greater than about 150, 155, 160, 165, 170, 175, or 180 nucleotides in length.

15. 15. The method of claim 14, wherein removing the DNA fragments from the cfDNA library comprises electrophoresis.

16. The method according to any one of claims 11 to 15, wherein enriching the fetal fraction of the sequence library comprises size exclusion based on read length of sequences in at least two windows of the sequence library, thereby obtaining at least two fetal fraction enriched sequence libraries.

17. The method of claim 16, wherein at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, or at least ten windows of the first sequence library are evaluated to identify and isolate cffDNA sequences, thereby obtaining at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, or at least ten fetal fraction enriched sequence libraries, respectively.

18. 18. The method of claim 16 or 17, wherein the at least two windows of the sequence library are selected from: (i) sequences that are 0-145 nucleotides, (ii) sequences that are 0-150 nucleotides, (iii) sequences that are 0-155 nucleotides, (iv) sequences that are 0-160 nucleotides, (v) sequences that are 0-165 nucleotides, (vi) sequences that are 0-168 nucleotides, (vii) sequences that are 0-170 nucleotides, (viii) sequences that are 0-175 nucleotides, (ix) sequences that are 0-180 nucleotides, (x) sequences that are 0-185 nucleotides, (xi) sequences that are 0-190 nucleotides, (xii) sequences that are 0-195 nucleotides, (xiii) sequences that are 0-200 nucleotides, and (xiv) non-gated.

19. 19. The method of any one of claims 16-18, wherein enriching the fetal fraction of the sequence library further comprises identifying and separating cffDNA from cfmDNA by comparing sequence reads of cffDNA and cfmDNA in the first sequence library to a reference genome, demultiplexing sequence reads from the first library, removing duplicate sequences from the first sequence library, or a combination thereof.

20. 20. The method of any one of claims 11 to 19, wherein detecting the presence or absence of the at least one genetic variant comprises determining an allele balance for each allele in the sample that encodes the at least one genetic variant in each of the at least two fetal fraction enriched sequence libraries, and generating an allele balance trajectory for each allele based on the allele balance in each of the at least two fetal fraction enriched sequence libraries, a depth trajectory based on the depth of the at least two fetal fraction enriched sequence libraries, or a combination of an allele balance trajectory and a depth trajectory.

21. 21. The method of any one of claims 11 to 20, wherein detecting the presence or absence of a chromosomal aneuploidy comprises analyzing the sequence depth of at least one sequence corresponding to the chromosome of interest in the sequence library.

22. 22. The method of claim 21, wherein the sequence depth of the at least one sequence corresponding to the chromosome of interest is fitted to a model of expected depth of the chromosome of interest.

23. The sequencing depth is calculated using the formula: [0010] During the ceremony, d ｐ is the depth of pregnancy, f is the fetal fraction, c ｍ is the maternal copy number, d ｂ is the background depth, c ｆ The method of claim 21 or 22, wherein is the fetal copy number.

24. The method of any one of claims 21 to 23, wherein the sequencing depth is normalized to control for GC-bias, sample background, hybridization probe capture, or a combination thereof.

25. 25. The method of any one of claims 11 to 24, wherein the method comprises detecting the presence or absence of said chromosomal aneuploidy selected from monosomy, trisomy, tetrasomy, polysomy X, polysomy Y, microdeletion, microduplication, pentasomy, and combinations thereof.

26. The at least one genetic variant is associated with 21-hydroxylase deficiency, ABCC8-associated hyperinsulinism, ARSACS, achondroplasia, achromatopsia, adenosine monophosphate deaminase 1, agenesis of corpus callosum with neuropathy, alkaptonuria, alpha-1-antitrypsin deficiency, alpha-mannosidosis, alpha-sarcoglycanopathy, alpha-thalassemia, angiotensin II receptor type I Alzheimer's, apolipoprotein A, pulmonary fibrosis ... Protein E genotype Alzheimer's, Argininosuccinic aciduria, Aspartylglycosaminuria, Ataxia with Vitamin E deficiency, Ataxia-telangiectasia, Autoimmune polyendocrine syndrome type 1, BRCA1 hereditary breast / ovarian cancer, BRCA2 hereditary breast / ovarian cancer, Bardet-Biedl syndrome, Best vitelliform macular dystrophy, Beta-sarcoglycanopathy, Beta-thalassemia, Biotinidase deficiency, Blau syndrome, Bloom's syndrome Syndrome, CFTR-related disorders, CLN3-associated neuronal ceroid-lipofuscinosis, CLN5-associated neuronal ceroid-lipofuscinosis, CLN8-associated neuronal ceroid-lipofuscinosis, Canavan disease, carnitine palmitoyltransferase IA deficiency, carnitine palmitoyltransferase II deficiency, cartilage-hair hypoplasia, cavernous vascular malformation, choroideremia, Cohen syndrome, congenital cataracts, facial dysmorphisms, and neurological disorders, congenital glycosylation disorder Ia type I, congenital glycosylation disorder type Ib, congenital nephrotic syndrome, Finnish type, Crohn's disease, cystinosis, DFNA9 (COCH), diabetes mellitus and hearing loss, early-onset primary dystonia (DYTI), Herlitz-Pearson junctional epidermolysis bullosa, FANCC-associated Fanconi anemia, FGFR1-associated craniosynostosis, FGFR2-associated craniosynostosis, FGFR3-associated craniosynostosis, factor V Leiden thrombophilia, factor V R2 factor mutation thrombophilia, factor XI deficiency, factor XIII deficiency, familial adenomatous polyposis, familial dysautonomia, familial hypercholesterolemia type B, familial Mediterranean fever, free sialic acid storage disorder, frontotemporal dementia with parkinsonism-17, fumarase deficiency, GJB2-related DFNA3 non-syndromic hearing loss and deafness, GJB2-related DFNB1 non-syndromic hearing loss and deafness, GNE-related myopathy, galactosemia, Gaucher disease,Glucose-6-phosphate dehydrogenase deficiency, glutaric acidemia type 1, glycogen storage disease type 1a, glycogen storage disease type 1b, glycogen storage disease type II, glycogen storage disease type III, glycogen storage disease type V, Graeschl syndrome, HFE-associated hereditary hemochromatosis, Halder's AIM, hemoglobin S beta-thalassemia, hereditary fructose intolerance, hereditary pancreatitis, hereditary thymine-uraciluria, hexosaminidase A deficiency, hidrotic ectodermal dysplasia 2, homocystinuria caused by cystathionine beta synthase deficiency, hyperkalemic periodic paralysis type 1, hyperornithinemiemia-hyperammonemia Myopathy-homocitrinemia syndrome, primary hyperoxaluria type 1, primary hyperoxaluria type 2, achondroplasia, hypokalemic periodic paralysis type 1, hypokalemic periodic paralysis type 2, hypophosphatasia, infantile myopathy and lactic acidosis (fatal and non-fatal forms), isovaleric acidemia, Krabbe disease, LGMD2I, Leber's hereditary optic neuropathy, French-Canadian Leigh encephalopathy, long-chain 3-hydroxyacyl-CoA dehydrogenase deficiency, MELAS, MERRF, MTHFR deficiency, MTHFR thermolabile variants, MTRNR1-related hearing loss loss and hearing loss, MTTS1-associated hearing loss and hearing loss, MYH-associated polyposis, maple syrup urine disease type 1A, maple syrup urine disease type 1B, McCune-Albright syndrome, medium-chain acyl-coenzyme A dehydrogenase deficiency, macrocephalic leukoencephalopathy with subcortical cysts, metachromatic leukodystrophy, mitochondrial cardiomyopathy, mitochondrial DNA-associated Leigh encephalopathy and NARP, mucolipidosis type IV, mucopolysaccharidosis type I, mucopolysaccharidosis type IIIA, mucopolysaccharidosis type VII, multiple endocrine neoplasia type 2, muscle-eye-brain disease, neurological phenotype nemaline myopathy, Niemann-Pick disease due to sphingomyelinase deficiency, Niemann-Pick disease type C1, Nijmegen chromosomal instability syndrome, PPT1-associated neuronal ceroid-lipofuscinosis, PROP1-associated pituitary hormone deficiency, Pallister-Hall syndrome, paramyotonia congenita, Pendred syndrome, peroxisomal bifunctional enzyme deficiency, pervasive developmental disorder, phenylalanine hydroxylase deficiency, plasminogen activator inhibitor 1, autosomal recessive polycystic kidney disease, prothrombin G20210A thrombophilia, pseudovitamin D deficiency rickets, pyknotic dysostosis,25. The method according to any one of claims 11 to 24, which is associated with a disease selected from autosomal recessive retinitis pigmentosa Bothnia type, Rett syndrome, chondrodysplasia punctata type 1, short-chain acyl-CoA dehydrogenase deficiency, Shwachman-Diamond syndrome, Sjogren-Larsson syndrome, Smith-Lemli-Opitz syndrome, spastic paraplegia 13, sulfate transporter-related skeletal dysplasia, TFR2-related hereditary hemochromatosis, TPP1-related neuronal ceroid-lipofuscinosis, thanatophoric skeletal dysplasia, transthyretin amyloidosis, tricyclic enzyme deficiency, tyrosine-hydroxylase deficiency DRD, hypertyrosinemia type 1, Wilson's disease, X-linked juvenile retinoschisis, cystic fibrosis, spinal muscular atrophy (SMA), hemoglobinopathies, and Zellweger syndrome spectrum.

27. 1. A method of enriching a biological sample for cell-free fetal DNA (cffDNA), comprising: obtaining a biological sample comprising cell-free DNA (cfDNA) from a pregnant woman, the cfDNA comprising cffDNA and cell-free maternal DNA (cfmDNA); extracting the cfDNA from the biological sample; and subjecting the extracted cfDNA to a size exclusion process, wherein the size exclusion process has a cutoff size of about 150 nucleotides, about 155 nucleotides, about 160 nucleotides, about 165 nucleotides, about 170 nucleotides, about 175 nucleotides, or about 180 nucleotides, thereby generating a sample enriched in cffDNA.

28. 1. A method for in silico processing of cell-free DNA (cfDNA), comprising: sequencing a cfDNA sample comprising cell-free fetal (cffDNA) and cell-free maternal DNA (cfmDNA) to prepare a sequence library; performing a read length based analysis, wherein an allelic balance of nucleic acid sequences of interest is established in at least two windows of the sequence library; and establishing a trajectory based on the allelic balance of the at least two windows.

29. 1. A method for reducing background noise from redundant genetic material in non-invasive prenatal screening (NIPS), comprising: (i) obtaining a biological sample from a pregnant woman, the biological sample comprising cell-free DNA (cfDNA); (ii) processing the cfDNA used for NIPS, wherein processing comprises enriching the biological sample for cell-free fetal DNA (cffDNA), in silico processing of the cfDNA, or a combination thereof.

30. 30. The method of claim 29, wherein processing comprises both enriching the biological sample for cell-free fetal DNA (cffDNA) and in silico processing of the cfDNA.

31. 31. The method of claim 29 or 30, wherein said enriching said biological sample for cell-free fetal DNA (cffDNA) comprises the method of claim 27.

32. 32. The method of any one of claims 29 to 31, wherein the in silico processing of the cfDNA comprises the method of claim 28.

33. 33. The method of any one of claims 29 to 32, further comprising normalization to control for GC-bias, sample background, hybridization probe capture, or a combination thereof.

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