Methods and devices for diagnosing and managing detection of parental disease alleles of genetic disease

Abstract

A method of assigning a parent to a haplotype, a sub-haplotype, or an allele associated with a haplotype. A chromosomal length haplotype of the genome is generated and a differential methylation status of at least one imprinted differential methylation region (iDMR) associated with each autosomal is determined. The differential methylation status of at least one iDMR is used to assign a parent for each chromosome length haplotype.

Description

[0001] Citation of Related Applications

[0002] This application claims priority to, and claims the benefit of for purposes of the United States of America, U.S. Provisional Patent Application No. 63 / 340,712, filed on May 11, 2022, the entire contents of which are incorporated herein by reference for all purposes. Technical Field

[0003] Some embodiments relate to methods or apparatus for determining the parent-of-origin of a haplotype, subhaplotype, or allele in a mammalian subject. Some embodiments relate to methods or apparatus for determining the parent-of-origin of a haplotype, subhaplotype, or allele in a mammalian subject without using parental sequence information. Background Art

[0004] Although phasing is usually defined as the task of distinguishing alleles from maternal and paternal homologs, in practice, most current phasing methods completely ignore parental information. Instead, chromosomes are described as: a series of subchromosomal phase blocks, each of which consists of alleles grouped into two haplotypes (for diploids); or a chromosome-length phase block with no parent-of-origin assigned (PofO). In this sense, true phase information is essentially unavailable for current genomic methods that do not include sequence data from at least one parent of the immediate child. 1-3 .

[0005] A notable exception to this paradigm is the parental information provided by consistent DNA methylation differences between maternally inherited alleles and paternally inherited alleles at imprinted differentially methylated regions (iDMRs). Such iDMRs reliably suppress expression of either the maternal or paternal alleles and, crucially, can be detected, for example, by nanopore sequencing (Oxford Nanopore Technologies) using the unique ionic current signature of 5-methyl-cytosine. 4-7 Long nanopore sequence reads can be used to call both sequence variation and DNA methylation to detect genome-wide allele-specific methylation 6,7 Despite the fact that phasing using nanopore reads achieves megabase-scale phase blocks, whole-chromosome haplotypes are not available, each chromosome is represented by several phase blocks, and there may be switches between paternal and maternal origins of blocks along the chromosome. 6 .

[0006] In contrast, some phasing techniques lack parental information but produce phase blocks spanning centromeres, repeat regions, and runs of homozygosity. 8,9 For example, single-cell Strand-seq is a method that can sequence the parental DNA template strand in a single daughter cell cultured in the presence of BrdU following one round of DNA replication. 10 Reads from the Watson template strand are mapped to the reference genome in negative orientation, and reads from the Crick template strand are mapped in positive orientation, meaning that alleles that occur at a frequency of 50% for a given chromosome and are covered by reads with different orientations belong to different homologs. By sequencing multiple cells, this approach enables the construction of global, chromosome-length haplotypes. 8 Because Strand-seq phase blocks are typically sparse (i.e., they do not phase all single nucleotide variants (SNVs)), Strand-seq is often used as a scaffold onto which reads or subchromosome phase blocks from other sequencing technologies are combined, effectively phasing them relative to each other along the entire length of a given chromosome. 11,12 .

[0007] Determining the PofO of germline variants can aid in clinical genetics. Genetic testing can inform patients about their genetic risk for disease. However, predicting which side of the family an autosomal variant comes from is a key limitation of current technology. Failure to determine the parent of origin (PofO) of a variant (i.e., whether it is maternally or paternally inherited) can lead to uncertain patient management and ineffective cascade genetic testing (CGT).

[0008] The impact of missing PofO information on clinical genetics practice can be addressed within a framework that takes into account the patient, family, and health care system. Considering the impact of missing PofO information on the patient, genes with PofO effects can be considered. There are genes that require parental segregation of pathogenic / likely pathogenic variants (PVs) to explain disease risk. This is the most straightforward use of determining PofO. When inherited through the father, the germline PVs in SDHD exhibit a PofO effect and predispose to a high lifetime risk of paraganglioma and pheochromocytoma. 68,69. Accurate prediction of maternal PV inheritance allows patients to avoid lifelong annual disease screening requiring high-resolution magnetic resonance imaging, biochemical testing, and specialist evaluation. These impose direct and indirect costs on patients and the healthcare system. Without knowledge of the PofO, patients may be advised to continue screening or given unclear instructions, leading to noncompliance with screening. Unnecessary and expensive procedures or missed tumors may result. When parents are deceased, cannot be contacted, or refuse genetic testing, variants may not be isolated via parental CGT. At this point, PofO assignment enables immediate improvement in the patient's cancer risk assessment.

[0009] Given the impact of missing PofO information on screening recommendations, management recommendations that rely on phenotypic segregation may be considered. Screening for pancreatic ductal adenocarcinoma (PDAC) is recommended for BRCA1, BRCA2, MLH1, MSH2, ATM, PALB2 PV carriers with PDAC in a close relative on the same family side as the PV 70 . However, the separation is often unknown. When the parent is deceased, tissue testing can separate the PV, however in the case of PDAC, there is often limited or no archival tissue. Pancreatic cancer screening is invasive (e.g., alternating upper gastrointestinal endoscopy and magnetic resonance imaging (MRI) annually), which is stressful, burdensome for patients, and costly to the system. Predicting non-separating PofO assignments could potentially free PV carriers from PDAC screening, or make it available to them if a finding predicts separation from PV.

[0010] Given the impact of missing PofO information on variant curation, variant curation guidelines that take into account phenotypic segregation within families could be considered. Upgrading or downgrading variants from "variant of uncertain significance" (VUS) can impact cancer screening / surveillance and risk-reducing interventions (e.g., surgery or medical treatment). VUS are commonly encountered in 40% of index cases tested by multigene panel or genomic approaches. Currently, ACMG / AMP guidelines use phenotypic information and variant segregation to guide variant classification. 15 Determining the phase or whether the variant is cis or trans to other PVs can also be used to classify variants. 15 Suspected variants of uncertain significance that do not enable pathogenic or benign variant classification may benefit from alternative evidence, such as haplotype typing and segregation prediction, to support variant classification.

[0011] Given the impact of missing PofO information on the family, the risk of recurrence of a pathogenic variant in another pregnancy can be considered. In the case of a de novo variant, the PofO assignment can inform the risk of recurrence of a pathogenic variant in another pregnancy, depending on the PofO assignment and whether the predicted parent of the variant in question is a parent of the current pregnancy / embryo / fetus or child in question. For example, if the de novo pathogenic variant is predicted to be of paternal origin, there may be a risk of gonadal mosaicism for that pathogenic variant in a subsequent pregnancy. However, if the subsequent pregnancy is to the same mother but a different father, there will be no significant risk for the pathogenic variant and amniocentesis or other investigations may not be needed to evaluate for the pathogenic variant. In addition, identification of parents who may be potential mosaics for a pathogenic variant can also indicate that they are at increased risk for cancer; these individuals may benefit from increased cancer screening.

[0012] Given the impact of missing PofO information on the elucidation of PV risk estimates, one can consider the pre-assigned risk assigned to parents carrying autosomal dominant PVs prior to segregation testing to be typically around 50% each. Accurate prediction of PV PofO changes the pre-assigned risk estimates for family members carrying the PV. When the chance of one parent carrying the PV moves from 50% to approximately 100%, the other parent and their collateral relatives move from 50% to approximately 0%, which typically brings them back into the group risk for cancer. The chance of a second-degree relative (e.g., aunt) from one side of the family that segregates the PV moves from 25% to approximately 50%. In some jurisdictions, individuals with a 50% chance of carrying a PV in a highly penetrant breast cancer susceptibility gene (e.g., BRCA1) may be entered into annual breast MRI screening until their genetic status is clarified. Given the lack of PV segregation in most second-degree relatives, the potential to change the medical management of second-degree relatives by using PofO predictions in probands is highly significant.

[0013] Considering the impact of missing PofO information on cascade genetic testing (CGT), the pre-assigned risk of carrying an autosomal dominant PV assigned to parents before separation testing (usually about 50% each) and the current poor cascade genetic testing rates can be considered. The use of PofO predictions for CGT within the entire family can have the most significant impact on the healthcare system. For example, it is estimated that less than 10% of the predicted 300,000 Canadians with hereditary cancer (HC) susceptibility have been identified in the last 25 years, and there is still a high unmet public service need to improve CGT rates. Hereditary breast and ovarian cancer (HBOC) is associated with a significantly increased lifetime risk of breast cancer, ovarian cancer, prostate cancer, and pancreatic cancer due to germline PVs in BRCA1 or BRCA2. Lynch syndrome (LS) is susceptible to colon cancer and endometrial cancer due to germline PVs in MLH1, MSH2, MSH6, or PMS2. These are the two most common hereditary cancer syndromes. The Centers for Disease Control and Prevention (CDC), Office of Genomics and Precision Public Health (OGPPH) considers HBOC and LS to have "significant potential for a positive impact on public health based on available evidence-based guidelines and recommendations" with the identification of these hereditary cancer syndromes having therapeutic and targeted cancer risk reduction utility in patients and family members. Genetic testing for these and other hereditary cancer syndromes has been available since the mid-1990s. Despite the potential for early prevention or detection of cancer, uptake of CGT remains poor, with no relative participation in CGT in half of the families identified. 20,71 Lower rates were also consistently observed among minority groups. 20 , which may be driven by limited intrafamilial communication, lack of access to relatives, or a disrupted family structure. Challenges faced by CGT include identifying cancer predisposing PVs outside the context of a known family history of cancer, such as in universal screening for Lynch syndrome. 19,71 , Germline Multigene Panel Testing for Hereditary Cancer 72 Tumor testing for indications of targeted therapies 73 This unmet potential requires the development of new strategies and technologies to increase the identification of at-risk family members. The group is developing guidelines to implement patient-sanctioned direct contact of relatives (PSDCoR), which has been shown to effectively increase CGT rates. 74. PSDCoR will significantly increase the number of patients of all ethnicities who experience CGT, necessitating the investment of additional resources to actively identify at-risk family members. Offering CGT to parents is the first step in identifying at-risk collateral relatives, however, parental samples are often not available. The use of accurate PofO predictions could significantly reduce the cost of CGT, decrease the time to diagnosis, and improve risk assessment by determining in advance which side of the family is at risk. Although concerns about cost often focus on the actual molecular testing, significant resources may be expended on pre-test genetic counseling for multiple family members on both sides of the family until segregation is confirmed. Direct and indirect financial and emotional burdens are also associated with CGT of a patient's family members. Accurate PofO predictions would halve the number of family members requiring CGT, double the mutation detection rate, and achieve these results at a lower cost per family. For n=20 32 Given the average family size, accurate PofO predictions will be critical to achieving the scale of testing generated from the PSDCoR in an efficient and cost-effective manner. Low-cost genetic testing of all relatives can be considered a competitor to parent-of-origin genomic analysis (POAga), however, if segregation is not possible, reliance on testing of both members of the family may place an undue burden on the partner who is not actually at risk, increase clinical infrastructure costs, tie up limited genetic counseling resources, and potentially delay CGT for the at-risk partner by diluting risk perception and reducing the sense of urgency for both members of the family. Accurate PofO predictions can save money, improve risk assessment, and save time in diagnosis.

[0014] Given the impact of missing PofO information on the healthcare system, better identification of PV carriers and cancer prevention or early detection at a more treatable stage have the potential to alleviate significant economic pressures on an overburdened healthcare system. Population genetic testing (PGT) can be viewed as a replacement for CGT, however, the demand for CGT depends on how quickly and equally PGT can be implemented. Given the poor uptake of CGT in HC families at increased risk for cancer, PGT for individuals at average risk may be more challenging. It is unclear how quickly adoption and saturation of PGT will occur. Of note, these two approaches are not mutually exclusive. Even with PGT, clinicians will ensure that relatives within the HC family have been tested. Family members may opt out of PGT due to low perceived risk or may have limited access to PGT (e.g., more than 20% of Canadians are recent immigrants). Additionally, ethnic minorities have the worst CGT rates. 20Therefore, it may be necessary to adopt a PSDCoR approach for HC and other families at risk for genetic diseases rather than relying on those at risk families to participate in or undergo PGT. Accurate prediction of PofO by POAga is of great value to the healthcare system because the timeline for adoption of PGT is several years. If 70% of first-, second-, and third-degree relatives are tested before PGT is adopted, the POD will be more accurate. 32 , then POAga-facilitated PSDCoR of individuals with PV identified during peridiagnostic cancer genetic testing could achieve real gains in improving CGT rates and identifying all HC PVs in a population within 10 years. Finally, once PGT is established, POAga will remain critical to ensure that those at highest risk are identified and to redefine the risk of family members who refuse PGT and CGT to best inform their medical management. The use of POAga is necessary to realize the full potential of precision healthcare, now and in the future.

[0015] In summary, accurate assignment of variant PofO can aid clinical genetics in multiple ways, including: screening for variants, determining the risk of recurrence in future pregnancies of an apparently de novo variant identified in a conceptus, fetus, or child, effectively screening relatives for genetic disease, predicting segregation of risk for cancer or other disease susceptibility to guide management recommendations, and assessing disease risk when the pathogenic variant has a PofO effect (i.e., when a patient's disease risk depends on which parent it is inherited from (e.g., hereditary paraganglioma-pheochromocytoma syndrome caused by pathogenic variants in SDHD, SDHAF2, and MAX) 13-17 Cascade genetic testing is used for pathogenic variants associated with diseases such as hereditary cancer or other actionable Mendelian disease risks, with the goal of early prevention or detection of cancer in family members, or intervention for their disease or disease risk. 18 In cases where PofO information does not exist because a parent cannot be contacted, is deceased, or refuses genetic testing, cascade genetic testing must be offered to both sides of the family until segregation is confirmed. This can be costly and burdensome for patients and families, exacerbating the already low uptake rate of cascade genetic testing. 19,20 Eliminating the need to test one partner of the family is a clear benefit and the major clinical utility of defining the PofO of the pathogenic variant, and more broadly, establishing chromosome length haplotypes with accurate parental segregation of genomic variation has broad applications.

[0016] Improved methods for determining the parentage of alleles associated with various disorders or diseases are generally desired, particularly methods that do not require sequence data from family members.

[0017] The foregoing examples of the related art and limitations associated therewith are intended to be illustrative and not exclusive. Other limitations of the related art will become apparent to those skilled in the art after reading the specification and studying the drawings. Summary of the invention

[0018] The following embodiments and aspects thereof are described and illustrated in conjunction with systems, tools and methods, which are intended to be exemplary and illustrative, but not limiting in scope. In various embodiments, one or more of the above-mentioned problems have been reduced or eliminated, while other embodiments are directed to other improvements.

[0019] One aspect provides a method for assigning parental origin to a haplotype, a subhaplotype, or an allele associated with the haplotype. Generate a chromosome length haplotype of a genome. Determine the differential methylation state of at least one imprinted differential methylation region (iDMR) associated with each autosome of the subject. Associate the determined differential methylation state of at least one iDMR with each chromosome length haplotype to assign parental origin to each chromosome length haplotype including a subhaplotype and an allele associated with the haplotype. In some aspects, the step of generating a chromosome length haplotype is performed by using at least one sequencing method. In some aspects, rather than assembling a chromosome length haplotype, a partial haplotype is generated.

[0020] In one aspect, the chromosome length haplotype is determined by performing a first sequencing method and a second sequencing method, the first sequencing method enabling determination of long-range phase information, the second sequencing method enabling accurate determination of at least short sequence reads and assigning those short sequence reads to haplotypes. Chromosome length haplotypes are generated using the results from the first sequencing method and the second sequencing method. Optionally, the second sequencing method is used to determine the methylation state of at least one iDMR associated with each autosome of the subject, and the determined methylation state of at least one iDMR is used to assign a parental source to each chromosome length haplotype. In some aspects, the results of the first sequencing method and the second sequencing method are iteratively used to generate chromosome length haplotypes.

[0021] In some aspects, the method is used to assign parentage to a haplotype, subhaplotype, or allele associated with a haplotype by evaluating the methylation state of an iDMR associated with each haplotype in a pair of haplotypes of the subject using only a sample obtained from the subject, i.e., without reference to parental or family sequence data.

[0022] In addition to the exemplary aspects and embodiments described above, further aspects and embodiments will become apparent by reference to the drawings and study of the following detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Exemplary embodiments are illustrated with reference to the accompanying drawings. The embodiments and drawings disclosed herein are intended to be considered illustrative rather than restrictive.

[0024] Figure 1 Exemplary embodiments of methods for determining the parentage of a haplotype, subhaplotype, or alleles associated with the haplotype are shown.

[0025] Figure 2 A second exemplary embodiment of a method for determining the parentage of a haplotype, subhaplotype, or allele associated with the haplotype is shown.

[0026] Figure 3A A third example embodiment for determining the parentage of a haplotype, subhaplotype, or allele associated with the haplotype is shown.

[0027] Figure 3B A fourth example embodiment for determining the parentage of a haplotype, subhaplotype, or allele associated with the haplotype is shown.

[0028] Figure 4 An overview of the phasing method used to determine parentage in one example embodiment is shown. The top figure shows the workflow including the input. The middle figure shows the combination of Strand-seq and nanopore reads used to construct chromosome length haplotypes without the assignment of each homolog relative to its parentage. The lower part of the figure shows how iDMRs can be used to distinguish between maternal and paternal homologs to assign parentage to each chromosome length haplotype.

[0029] Figure 5 A comparison of nanopore-only phasing and Strand-seq phasing is shown. The left panel (a) shows subchromosomal nanopore phase blocks on chromosome 1. The right panel (b) shows the chromosome-level haplotypes obtained for chromosome 1 by phasing nanopore detected variants using Strand-seq.

[0030] Figure 6 Shown are the paternal and maternal iDMRs (running upwards) and CpG methylation at the paternal iDMR (running downwards) in one example.

[0031] Figure 7 Shown are per-chromosome results for parentage assignment of het-SNVs in each of the five genomes examined in one example. DETAILED DESCRIPTION

[0032] In the following description, specific details are set forth to provide a more thorough understanding to those skilled in the art. However, in order to avoid unnecessarily obscuring the present disclosure, well-known elements may not be shown or described in detail. Therefore, the specification and drawings are to be regarded as illustrative rather than restrictive.

[0033] Hundreds of loci in the human genome have alleles that are differentially methylated according to their parental origin. DNA methylation at these imprinted loci (called imprinted differential methylation regions or iDMRs) usually shows very little variation between tissues, individuals and populations. The inventor has demonstrated that such loci can be used to distinguish maternal homologs and paternal homologs of all autosomes without the need for parental DNA. The inventor has integrated the methylation state of iDMRs determined using methylation detection sequencing technology (nanopore sequencing in the exemplary embodiment) with the long-range phase information obtained using another sequencing technology (Strand-seq in the exemplary embodiment) to determine the parental origin of chromosome length haplotypes for both DNA sequence and DNA methylation. The inventor has demonstrated that the overlap between SNVs in iDMRs and SNVs defined by sequencing strategies (such as Strand-seq) that can determine long-range phase information enables complete haplotype typing or phasing of parental origin to be performed without analyzing parental genomes for the first time. The inventor has confirmed that parental origin is determined in five father-mother-child trios from a variety of global populations. The inventor's method correctly infers the parentage of all autosomes with a low mismatch error rate. Because the method can determine whether the genetic disease allele can be derived from the mother or the father, the method can be used for diagnosis, treatment and management of genetic diseases. Because the method can also determine whether any autosomal variation can be derived from the mother or the father, the method can also be used to discover, investigate and explore genetic ancestry, genetic traits, pharmacogenomics variations and forensic research.

[0034] Some embodiments provide methods and apparatus for assigning parentage of autosomal alleles using parental imprinting and chromosome length haplotypes.In one aspect, the invention provides methods and apparatus for assigning greater than 99% of autosomal alleles to parentage.

[0035] Some embodiments provide methods and apparatus for distinguishing alleles from maternal homologs and paternal homologs to resolve the haplotypes of parental chromosomes in a diploid genome. In one aspect, the invention provides methods and apparatus for resolving haplotypes of the human genome. In alternative aspects, the invention provides methods and apparatus for resolving haplotypes of other mammals and diploid genomes.

[0036] The present invention provides a method for determining whether a genetic disease allele can be derived from the mother or father of a subject for diagnosis and management of genetic diseases. Understanding parental separation is of great significance for variant screening, understanding disease penetrance, management impact on patients and parents, and promoting centralized cascade genetic testing. Such diseases include, but are not limited to, hereditary syndromes, such as hereditary cancer syndromes that exhibit parent-of-origin effects, including hereditary paraganglioma-pheochromocytoma syndrome associated with SDHD, SDHAF2, and MAX, and other hereditary cancer syndromes such as hereditary breast and ovarian cancer, Lynch syndrome, etc. Diseases also include, but are not limited to, operable cancer and non-cancer related diseases, and include, but are not limited to, those diseases identified by the American College of Medical Genetics and Genomics (ACMG) recommendations for reporting incidental findings in clinical exome and genome sequencing. Some embodiments of the present invention may be applied to any Mendelian disease or trait. These can be found in Online Mendelian Inheritance in ... It is a continuously updated catalog of human genes and genetic conditions and traits. Some embodiments of the invention can be applied to pharmacogenomic variants and ancestral markers. Some embodiments of the invention can reveal somatic mutational signatures passed from either parent to a child that may affect the child's risk of cancer or disease, and can infer ongoing mutational processes in the parent that may be associated with the parent's own cancer risk or disease state.

[0037] In one aspect, the invention provides a method for identifying the parental origin of an allele in the case of limited or no access to parental and / or family DNA samples. In another aspect, the invention provides a method for identifying the parental origin of an allele that may be mosaic in a parent, which may inform a parent who may be negative for the allele or a pathogenic or likely pathogenic variant about recurrence risk or disease risk.

[0038] In one aspect, the invention provides a method for identifying, validating and screening complex genetic traits in mammals and diploid genomes. In one aspect, the invention provides a method for genotyping livestock for animal breeding.

[0039] In another aspect, the invention provides a method for characterizing the ancestral background from either parent.

[0040] In another aspect, the present invention enables improved haplotype typing for use in pharmacogenomic assessments, HLA haplotype typing, cancer vaccines, forensics, and therapy using allele-specific inhibition. For example, parent-of-origin effects have been observed in association with HLA loci. 58,59 Similarly, HLA haplotypes may be important for predicting the efficacy of cancer vaccines and / or cancer therapeutics, and thus aid in the selection of cancer vaccines and / or cancer therapeutics. 60

[0041] On the other hand, the invention provides insight into the complete set of variation inherited from either parent, allowing inferences to be made about the mutational processes that occurred in either parent.

[0042] In another aspect, the present invention facilitates the discovery of novel variants and genes by isolating alleles / variants / haplotypes that have a parental or familial disease phenotype.

[0043] On the other hand, the method can be used to provide information on comprehensive risk assessment for Mendelian traits by accurately resolving all autosomal variation, polygenic risk, and genetic and epigenetic modifiers inherited from both parents. On the other hand, the method can be used to provide information on comprehensive risk assessment for complex traits by accurately resolving all autosomal variation, polygenic risk, and genetic and epigenetic modifiers inherited from both parents.

[0044] Some embodiments of the invention provide methods and apparatus that integrate megabase-level phase blocks from nanopore sequencing with long-range phase information (e.g., Strand-seq data) in DNA from sequencing technologies that can only produce sparse phase blocks using imprinted differentially methylated regions (iDMRs) based on their parental origin to provide maternal and paternal homologs of all autosomes. In one aspect of the invention, nanopore sequencing is performed using a nanopore PromethION instrument. Alternative sequencing techniques may be selected by those skilled in the art.

[0045] refer to Figure 1 , illustrates a general method 100 for determining the parentage of a haplotype, subhaplotype, or allele in a diploid genome. In step 102, a haplotype is obtained in any suitable manner, such as by a suitable long-range sequencing technology capable of resolving haplotypes (e.g., Strand-seq sequencing, Hi-C sequencing, 3C-Seq sequencing, 10X sequencing, continuous long read (CLR) sequencing, high-fidelity (HiFi) sequencing, Pore-C sequencing, or other suitable technologies now known or later developed, or combinations thereof), or by sequencing haploid cells. In some embodiments, the long-range sequencing technology used is capable of resolving haplotypes along the entire length of the chromosome.

[0046] In step 104, for each haplotype, e.g., for each autosome, information about the methylation state of an imprinted differentially methylated region (iDMR) is determined. The methylation state of an iDMR can be determined in any suitable manner, e.g., using nanopore sequencing, single molecule real-time (SMRT) sequencing, bisulfite sequencing, or other techniques (whether now known or later developed) that allow determination of the methylation state of an iDMR, or a combination thereof.

[0047] In some embodiments, two different sequencing methods are used to determine the long-range phase information for each haplotype and the methylation status of the iDMR associated with each haplotype in steps 102 and 104. In other embodiments, if future developments in sequencing technology produce a single sequencing technology that is capable of determining both the long-range phase information and the methylation status of the iDMR on each haplotype, steps 102 and 104 may be performed using the single sequencing technology.

[0048] In some embodiments, one sequencing technology is used to help obtain haplotypes in step 102 and determine the methylation state of the iDMRs associated with each haplotype in step 104. For example, in one embodiment, nanopore sequencing is used to help obtain two haplotypes of a diploid genome in step 102, and nanopore sequencing is used to determine the methylation state of multiple iDMRs associated with each haplotype in step 104. However, because nanopore sequencing cannot provide long-range phase information, nanopore sequencing is combined with another sequencing technology (e.g., Strand-seq, Hi-C, 3C-Seq, 10X, continuous long reads (CLR), high fidelity (HiFi), Pore-C, or other sequencing technologies that allow determination of long-range phase information) in step 102 to obtain complete haplotypes.

[0049] In step 106 , the parentage of each haplotype is assigned based on the methylation status of the iDMR of each haplotype, for example, each autosome.

[0050] In some embodiments, in step 102, rather than performing long-range sequencing, a sequence of haploid cells (e.g., sperm or eggs), a subdiploid cell population, or a single chromosome may be sequenced. 57 The constructed haplotype is sequenced to produce an accurate haplotype to which the sequence data from step 104 can be phased. In some embodiments where the haplotype is constructed from a haploid cell such as a sperm or egg, the somatic tissue cells (e.g., blood) of the parent from which such haploid cells are obtained can be sequenced in step 104 to infer the parentage from the autosomal alleles of such parent, since the haploid cell (e.g., sperm or egg) itself will be imprinted during its formation.

[0051] refer to Figure 2 , illustrates a second embodiment of a general method 200 for determining the parentage of a haplotype, subhaplotype, or allele of a diploid genome. In step 202, a sample from a subject is subjected to a first sequencing method that allows determination of long-range phase information for each haplotype of the diploid genome. Any suitable sequencing technology that allows determination of long-range phase information, whether now known or later developed, can be used in step 202, for example, Strand-seq, Hi-C, 3C-Seq, 10X, continuous long reads (CLR), high fidelity (HiFi), Pore-C, or a combination thereof.

[0052] In step 204, for each haplotype, e.g., for each autosome, information about the methylation state of an imprinted differentially methylated region (iDMR) is determined. The methylation state of an iDMR can be determined in any suitable manner, e.g., using nanopore sequencing, single molecule real-time (SMRT) sequencing, bisulfite sequencing, or other techniques (whether now known or later developed) that allow determination of the methylation state of an iDMR, or a combination of such techniques.

[0053] In some embodiments, two different sequencing methods are used to determine the long-range phase information for each haplotype and the methylation status of the iDMR associated with each haplotype in steps 202 and 204. In other embodiments, if future developments in sequencing technology produce a single sequencing technology that is capable of determining both the long-range phase information and the methylation status of the iDMR on each haplotype, steps 202 and 204 may be performed using the single sequencing technology.

[0054] In step 206, chromosome length haplotypes are generated using the sequence information obtained in steps 202 and 204. In some embodiments, the sequence information obtained in steps 202 and 204 is iteratively used to phase heterozygous variations such as single nucleotide variations (SNVs) and / or indels to obtain chromosome length haplotypes.

[0055] In step 208, using the results of the methylation status of the iDMR associated with each chromosome length haplotype (eg, autosome) determined in step 206, a parentage is assigned to each chromosome length haplotype.

[0056] refer to Figure 3A, illustrates a third illustrative embodiment of a specific embodiment of a method 300 for determining the parentage of a haplotype, subhaplotype, or allele. In step 302, a first sequencing method is performed on the sample, which allows for determination of long-range phase information for each haplotype of a diploid genome. For example, the first sequencing method can produce sparse chromosome-level haplotypes. Any suitable sequencing technology that allows for determination of long-range phase information can be used in step 302, whether now known or later developed, for example, Strand-seq, Hi-C, 3C-Seq, 10X, continuous long reads (CLR), high fidelity (HiFi), Pore-C, or a combination thereof.

[0057] In step 304, the sample is subjected to a second sequencing method that allows determination of the methylation state of the iDMR associated with a particular DNA sequence. The methylation state of the iDMR can be determined in any suitable manner, such as using nanopore sequencing, single molecule real-time (SMRT) sequencing, bisulfite sequencing, or other techniques (whether now known or later developed) that allow determination of the methylation state of the iDMR, or a combination of such techniques. The second sequencing method generates unphased variations (e.g., SNVs and / or indels) in 306.

[0058] In step 308, the variation obtained using the second sequencing method is phased to one of two haplotypes, HP1 or HP2, using the long-range phase information of the variation determined by the first sequencing method. In some embodiments, the variation phased in step 308 is a SNV. In some embodiments, the variation phased in step 308 is an indel, and the SNPs flanking the indel are used to assign the indel to a haplotype. In some embodiments, the variation phased in step 308 is both a SNV and an indel.

[0059] In some embodiments, in step 310, all variations (eg, both SNVs and indels) identified by the first sequencing method are re-phased to each haplotype, ie, HP1 or HP2, using the phased variations obtained in step 308.

[0060] In step 312 , sequence reads obtained using the second sequencing method are secondarily phased using all phased variations (eg, both SNVs and indels) obtained in step 310 to generate densely phased variations in 314 .

[0061] In step 316, the per-read methylation information from each read obtained by the second sequencing method is integrated into its phase information from 314. Thus, the differential methylation information obtained at each iDMR is phased to each read in either of the two haplotypes (i.e., HP1 or HP2) to calculate the methylation frequency of each haplotype at each iDMR site. The methylation frequency is then used in 318 to assign each haplotype to its parentage based on the methylation status of the known iDMR.

[0062] refer to Figure 3B , a fourth exemplary embodiment 350 of a method for determining the parentage of a haplotype, subhaplotype, or allele is illustrated. In 352, a first sequencing method capable of determining long-range phase information is performed. In 354, a second sequencing method is performed that is capable of accurately determining at least short sequence reads and assigning those short sequence reads to haplotypes in 358. In some embodiments, the second sequencing method also provides information about the methylation state of the short sequence reads. In 360, variants (e.g., SNPs and / or indels) can optionally be iteratively phased to improve the phase information obtained in 362.

[0063] At 366, the differential methylation state of at least one iDMR for each autosome of the subject is determined, for example using information obtained from a second sequencing method. At 368, the parentage of each chromosome length haplotype is assigned based on the differential methylation state of at least one iDMR for each autosome of the subject.

[0064] In some embodiments, the method is used to assign parentage to a haplotype, subhaplotype, or allele by assessing the methylation status of an iDMR associated with each of a pair of haplotypes for the subject using only a sample obtained from the subject.

[0065] In some embodiments, the method is used to trigger cascade genetic testing by: selecting an undesirable subhaplotype or allele or a subhaplotype or allele of interest; determining the parents associated with the undesirable subhaplotype or allele or the subhaplotype or allele of interest based on the determination of the parentage of the undesirable subhaplotype or allele or the subhaplotype or allele of interest; and performing cascade genetic testing on family members of the parents associated with the undesirable subhaplotype or allele or the subhaplotype or allele of interest. In some such embodiments, the undesirable allele is SDHD, SDHAF2, MAX, BRCA1, BRCA2, MLH1, MSH2, MSH6, PMS2, EPCAM, ATM, PALB2, TP53, APC, ACTA2, ACTC1, ACVRL1, AIP, ALK, ANKRD26, APOB, ARMC5, ATP7B, ATR, AXIN2, BAG3, BAP1, BARD1, BLM, BMPR1A, BRIP1, BTD, BUB1B, CACNA1S , CASQ2, CASR, CDC73, CDH1, CDK4, CDKN1B, CDKN1C, CDKN2A, CEBPA, CFTR, CHEK1, CHEK2, COL3A1, CPA1, CTC1, CTNNA1, CTRC, C YLD, DDB2, DDX41, DES, DICER1, DIS3L2, DKC1, DLST, DROSHA, DSC2, DSG2, DSP, EGFR, EGLN1, ENG, ERCC1, ERCC2, ERCC3, ERCC4, ERCC5, ETV6, EXT1, EXT2, EZH2, FAM175A, FAN1, FANCA, FANCB, FANCC, FANCD2, FANCE, FANCF, FANCG, FANCI, FANCL, FANCM, FB N1, FH, FLCN, FLNC, FOCAD, GAA, GALNT12, GATA2, GLA, GPC3, GREM1, HFE, HNF1A, HOXB13, HRAS, KCNH2, KCNQ1, KIF1B, KIT, LDLR , LMNA, LZTR1, MC1R, MEN1, MET, MITF, MLH3, MRE11, MRE11A, MSH3, MUTYH, MYBPC3, MYH11, MYH7, MYL2, MYL3, NBN, NF1, NF2, NHP 2. NOP10, NTHL1, OTC, PAX5, PALLD, PCSK9, PDGFRA, PHOX2B, PIK3CA, PKP2, POLD1, POLE, POLH, POT1, PRKAG2, PRKAR1A, PRSS1,PTCH1, PTCH2, PTEN, RAB43, RABL3, RAD1, RAD50, RAD51C, RAD51D, RB1, RBM20, RECQL, RECQL4, RECQL5, REST, RET, RINT1, RP E65, RPS20, RUNX1, RYR1, RYR2, SAMD9, SAMD9L, SCN5A, SDHA, SDHB, SDHC, SLC45A2, SLX4, SMAD3, SMAD4, SMARCA4, SMARCB1, Alleles of SMARCE1, SPINK1, SRP72, STK11, SUFU, TERC, TERT, TGFBR1, TGFBR2, TINF2, TMEM127, TMEM43, TNNC1, TNNI3, TNNT2, TP53I3, TPM1, TRDN, TRIP13, TSC1, TSC2, TTN, TTR, TYR, VHL, WRAP53, WRN, WT1, XPA, XPC, or XRCC2, or any other gene currently known or later determined to be associated with a genetic disorder. A list of currently known actionable genetic disorders is available, for example, from the American College of Medical Genetics and Genomics. A list of currently known pathogenic variants in such genes is available, for example, from ClinVar, e.g., in ACTC1, ACVRL1, APC, APOB, ATP7B, BAG3, BMPR1A, BRCA1, BRCA2, BTD, CACNA1S, CASQ2, COL3A1, DES, DSC2, DSG2, DSP, ENG, FBN1, FLNC, GAA, GLA, HFE, HNF1A, KCNH2, KCNQ1, LDLR, LMNA, MAX, MEN1, MLH1, MSH2, MSH6, MUTYH, MYBPC3, MYH1, and MYS. 1. MYH7, MYL2, MYL3, NF2, OTC, PALB2, PCSK9, PKP2, PMS2, PRKAG2, PTEN, RB1, RBM20, RET, RPE65, RYR1, RYR2, SCN5A, SDHAF2, SDHB, SDHC, SDHD, SMAD3, SMAD4, STK11, TGFBR1, TGFBR2, TMEM127, TMEM43, TNNC1, TNNI3, TNNT2, TP53, TPM1, TRDN, TSC1, TSC2, TTN, TTR, VHL and WT1. ,

[0066] In some embodiments, the undesired allele is a founder mutation commonly associated with a genetic disorder, such as BRCA1 187delAG, BRCA15385 insC (also described as BRCA15382insC), BRCA2 6174delT.

[0067] In some embodiments, if the allele is a mutant form of succinate dehydrogenase complex D subunit (SDHD) and the parent is the father, the method further comprises periodically assessing the subject for paraganglioma or pheochromocytoma and / or performing cascade genetic testing. In some embodiments, if the allele is a mutant form of SDHD and the parent is the mother, the method further comprises performing cascade genetic testing.

[0068] In some embodiments, if the allele is a mutant form of succinate dehydrogenase complex assembly factor 2 (SDHAF2) and the parent is the father, the method further comprises periodically evaluating the subject for paraganglioma and / or performing cascade genetic testing.

[0069] In some embodiments, if the allele is a mutant form of the MYC-associated factor X gene (MAX) and the parent is the father, the method further comprises periodically evaluating the subject for pheochromocytoma and / or performing cascade genetic testing.

[0070] In some embodiments, if the allele is a mutant form of ATM, BRCA1, BRCA2, MLH1, MSH2, MSH6, EPCAM, PMS2, PALB2, TP53, CHEK2, BRIP1, RAD51C, RAD51D, or MUTYH, and the parent is determined to be a parent with a family history of pancreatic cancer, the method further comprises initiating pancreatic cancer screening for the subject at age 50 or 10 years younger than the earliest pancreatic cancer diagnosed in the family, whichever is earlier.

[0071] In some embodiments, the method further comprises determining whether two or more variants are cis- or trans-phased based solely on a sample obtained from the subject (i.e., without reference to a sample obtained from either parent of the subject). In some aspects, knowing whether a variant is cis- or trans-phased can provide information about risk assessment. 15

[0072] In some embodiments, if the allele is a mutant form of the succinate dehydrogenase complex (including A, B, C, or D subunits) and the pathogenic or likely pathogenic variant in SDHD is determined to be paternally inherited (i.e., the parent of origin is the father), the subject is regularly evaluated for paraganglioma or pheochromocytoma and / or cascade genetic testing is initiated. If the allele is a mutant form of the succinate dehydrogenase complex (including A, B, C, or D subunits) and the pathogenic or likely pathogenic variant in SDHD is determined to be maternally inherited (i.e., the parent of origin is the mother), tumor screening may not be required in asymptomatic carriers and only cascade genetic testing may be initiated.

[0073] In some embodiments, the method can be used to detect the parentage of the abnormal methylation pattern of one or both of the determined haplotypes. For example, constitutive methylation of genes is associated with certain diseases, such as MLH1, MSH2, BRCA2, and many other genes are associated with cancer or disorders involving abnormal methylation. 61-64

[0074] In some embodiments, the method further comprises predicting a similar pharmacogenomic outcome for a parent carrying a desired or undesirable haplotype, subhaplotype, or allele detected in the subject, wherein the parent has been determined by assessing the parentage of the desired or undesirable haplotype, subhaplotype, or allele detected in the subject.

[0075] In some embodiments, the method further includes determining whether the allele is an HLA haplotype, subhaplotype, or allele associated with a disease risk with a known parent-of-origin effect, and if it is determined that the haplotype, subhaplotype, or allele is an HLA haplotype, subhaplotype, or allele associated with a disease risk with a known parent-of-origin effect, periodically monitoring the subject's disease risk based on the known parent-of-origin effect.

[0076] In some embodiments, the allele is an HLA haplotype, subhaplotype, or allele, and the method further comprises selecting a cancer vaccine or cancer therapy for a parent of the subject based on determining that the parent is the parent of the HLA haplotype, subhaplotype, or allele in the subject.

[0077] In some embodiments, the method further includes comparing a DNA sequence obtained from a sample at the crime scene with haplotype, subhaplotype or allele information and parentage information obtained from the subject, and if the DNA sequence obtained from the sample at the crime scene matches a DNA sequence from one of the haplotypes, subhaplotypes or alleles of the subject, concluding that the identified parentage of the subject is associated with the crime scene.

[0078] In some embodiments, wherein the haplotype, subhaplotype, or allele comprises an apparent de novo germline variation and wherein a biological parent of the subject tested negative for the germline variation, the method further comprises determining the parentage of the de novo germline variation to identify a parent at potential risk for the recurrence of the germline variation or determining the risk of the parent for postzygotic somatic mosaicism indicating a risk of the parent developing the disease.

[0079] In some embodiments, the method further comprises comparing haplotypes from two individuals with corresponding mutations to identify the ancestral mutation by identifying the ancestral haplotype.

[0080] In some embodiments, the method further comprises administering therapy to a parent of a parental source determined to be an undesirable haplotype, subhaplotype, or allele, wherein the therapy optionally comprises antisense oligonucleotides, allele-specific targeting constructs, or allele-specific gene editing targeting an undesirable allele. In some embodiments, the undesirable allele comprises a mutation associated with a disorder having a dominant negative effect, optionally associated with SNV, copy number variation (CNV), indel, methylation, or a triplicate repeat.

[0081] In some embodiments, the methods can be used to provide improved structural variation characterization of haplotypes, including parentage structural variation information. 65,66

[0082] In some embodiments, the method can be used to determine the parentage of a haplotype, which can be used, for example, to determine risk haplotypes, determine ancestry, or examine complex traits.

[0083] In some embodiments, the method can be used to avoid the need for genotyping based on father-mother-child trios or sequencing based on father-mother-child trios to phase haplotypes for use in studies to determine parental effects, as currently required. 67

[0084] A specific embodiment of the present invention implements the following steps:

[0085] a) Obtain nanopore long reads and single-cell Strand-seq data from a subject. Use the nanopore data to call variants, phase some of them with Strand-seq in an inversion-aware manner. Then use these phased variants to phase the nanopore reads, which are used to phase more variants and DNA methylation in an iterative manner.

[0086] b) Strand-seq and Nanopore reads were combined to construct chromosome length haplotypes without examining DNA methylation, but the assignment of each homolog (i.e., chromosome length haplotype) to haplotype 1 or 2 was random relative to its parent of origin (PofO).

[0087] c) The DNA methylation status of iDMRs was used to identify PofO for each chromosomal homologue.

[0088] In various other embodiments, an apparatus and / or a kit for performing the aforementioned method is provided. For example, in one embodiment, a device is provided, the device having a first sequencing device for performing a first sequencing technology to provide long-range phase information, a second sequencing device for performing a second sequencing technology to provide a differential methylation state of at least one iDMR associated with a haplotype, a sub-haplotype, or an allele, and a processor for analyzing the long-range phase information and the differential methylation state of at least one iDMR to determine the parentage of a haplotype, a sub-haplotype, or an allele associated with the haplotype. In one embodiment, a kit is provided, the kit having instructions for performing a first sequencing technology to provide long-range phase information, instructions for performing a second sequencing technology to provide a differential methylation state of at least one iDMR associated with a haplotype, a sub-haplotype, or an allele, and instructions for combining the long-range phase information and the differential methylation state of at least one iDMR to determine the parentage of a haplotype, a sub-haplotype, or an allele associated with the haplotype.

[0089] Example

[0090] Further embodiments are described with reference to the following examples, which are intended to be illustrative and not limiting in scope.

[0091] Example 1.0 Parentage detection and chromosome-level sequencing using long-read DNA methylation sequencing and Strand-seq Haplotype

[0092] The inventors have integrated nanopore sequencing for detecting methylation with long-range phase information in Strand-seq data to determine the parentage (PofO) of chromosome length haplotypes for both DNA sequence and DNA methylation in five father-mother-son trios with different genetic backgrounds. The parentage of all autosomes was correctly inferred, with an average mismatch error rate of 0.31% for SNVs and 1.89% for indels. Because the inventors' method can determine whether a genetic disease allele is derived from the mother or the father, it is reasonable to predict that implementation of the method will improve the diagnosis, treatment, and management of many genetic diseases.

[0093] The examples herein demonstrate that when nanopore methylation and iDMRs are integrated with Strand-seq chromosome length haplotypes, alleles along the full length of each autosome can be assigned to either the maternal or paternal homologue ( Figure 4 ). The method does not require parental sequence data (father-mother-child triad information) or SNP linkage analysis, but relies on the fact that all human autosomes have at least one imprinted differentially methylated region. The only input required is a sample of fresh whole blood or other culturable living cells. The inventors targeted the genomes from the Genome in a Bottle Consortium (GIAB), the Human Genome Structural Variation Consortium (HGSVC), and the 1000 Genomes Project (1KGP). 21-23 Five gold standard parent-mother-child trios validated PofO assignments of heterozygous SNVs and indels. The described approach has the potential to transform cascade genetic testing and improve genetic disease screening by targeting sequencing efforts for selected family members to track pathogenic variants throughout the family.

[0094] Example 1.1 - Nanopore and Strand-seq for chromosome-level haplotype typing

[0095] The inventors used five human genomes to demonstrate the described methods, including NA12878, HG002 and HG005 from GIAB, HG00733 from HGSVC and NA19240 from 1KGP. 21-23 For all samples, the inventors used nanopore sequencing data with 24-38X coverage depth and 42-220 Strand-seq libraries with 2.78-9.46X combined coverage depth per sample. Nanopore raw signals were base-called and mapped to the human reference genome GRCh38, and SNVs and indels ("variants") were called from nanopore reads using Clair3. 24 .

[0096] Although nanopore reads can be used alone to phase nearly all called variants for each sample, the resulting phase blocks are relatively short (N50 M±SD=4.85±3.66 Mb; “M” mean, “SD” standard deviation) and do not span entire chromosomes ( Figure 5 ).

[0097] Therefore, the inventors first applied inversion-aware Strand-seq phasing to Nanopore SNVs and constructed sparse chromosome-length haplotypes. Strand-seq phased 61.03%-95.02% of common heterozygous SNVs between the ground truth and the Nanopore call set, with a mismatch error rate of 0.14%-1.36% (number of incorrectly phased variants / number of all phased variants), with a single phase block spanning each chromosome (Table 1; Figure 5 ). Nanopore reads (fraction of successfully phased reads with at least MAPQ 20 M±SD=71%±9.6%) were then phased using Strand-seq phased SNVs, which were then used to re-phase all variants and achieve dense chromosome-level haplotypes containing almost all heterozygous SNVs and most indels (Table 1). Combining Strand-seq and Nanopore in this way allowed the inventors to phase 99.37%-99.91% of heterozygous SNVs and 96.29%-98.77% of heterozygous indels present in both the true value and Nanopore call sets, with mismatch error rates of 0.07%-0.54% for SNVs and 1.33%-2.43% for indels (Table 1).

[0098] Figure 5 A comparison of nanopore-only phasing and Strand-seq phasing is shown. Figure (a) shows that the subchromosome nanopore phase blocks on chromosome 1 contain >99% of called SNVs and >96% of called indels. However, using nanopore-only phasing for PofO assignment results in M±SD=42.37%±7.13% PofO errors per chromosome for SNVs and M±SD=42.82%±6.83% for indels. This is because any phase switching between phase blocks means that PofO is effectively randomly assigned to any phase block. Indels and SNVs are phased using WhatsHap v1.2.1 with the option --indel--ignore-read-group. Figure (b) shows that, in contrast, phasing of nanopore-detected variations using Strand-seq results in chromosome-level haplotypes with consistent PofO on each haplotype, as shown herein for chromosome 1.

[0099] Figure 4An overview of the PofO phasing method used in this embodiment is shown. The upper figure shows that the input of the workflow is nanopore long reads and data from a single-cell Strand-seq library. Nanopore data is used to call variations, and some of the variations are phased with Strand-seq in an inversion-aware manner. These phased variations are then used to phase the nanopore reads, which are used to phase more variations and DNA methylation. Finally, the DNA methylation state of the iDMR is used to identify the PofO of each chromosome homolog. The figure below shows that without checking DNA methylation, Strand-seq and nanopore reads can be combined to construct chromosome length haplotypes, but each homolog (i.e., chromosome length haplotype) is assigned to HP1 or HP2 (haplotype 1 or haplotype 2) relative to its PofO is random (upper part of the bottom figure). However, as shown in the lower part of the figure, iDMR can be used to distinguish between maternal homologs and paternal homologs. Lollipops mark the positions of all 149 maternal iDMRs (methylated on the maternal homolog) and all 56 paternal iDMRs used in this study. See Table 2 for iDMR names and positions shown relative to cytobands.

[0100] Table 1. Phasing of heterozygous variants and comparison with the true call set .

[0101]

[0102]

[0103] Table 2. List of currently known iDMRs .

[0104]

[0105]

[0106]

[0107]

[0108]

[0109]

[0110]

[0111]

[0112]

[0113] Example 1.2 - PofO detection using iDMR

[0114] PofO-specific DNA methylation at iDMRs provides a unique source of information for determining the PofO of homologs represented by chromosome-length haplotypes without using parental sequence data. The inventors assembled a list of 205 iDMRs from previous genome-wide studies. 25-29 . Chromosome X was ignored as it has no known iDMRs. The inventors combined DNA methylation information from phased nanopore reads with known PofO information of imprinted intervals to assign PofO to each homolog. On average, 6 iDMRs (median = 5; SD = 5.8; range 1-32) were used for PofO assignment for each chromosome, and each chromosome was assigned to its parental origin with a mean confidence score of 96.3% (median = 99.2%; SD = 6.4%; range 60.7%-100%) (see Figure 6 , which shows representative data of CpG methylation data used to make PofO assignments in HG0002; parallel data were obtained for HG005, HG00733, NA12878, and NA19240, but not shown). On average, 6.9% of iDMRs conflicted with most PofO assignments. However, because iDMRs are weighted according to the degree of differential methylation in each sample, conflicting iDMRs account for only 2.5% of the PofO contribution (x as described in Example 1.4 - Materials and Methods below).

[0115] Figure 6 CpG methylation at paternal and maternal iDMRs for parentage assignment in HG002 is shown. Maternal methylated iDMRs extend upward and paternal methylated iDMRs extend downward. Bars represent the fraction of CpGs with methylation differences ≥ 0.35 (differential methylation) between haplotypes (HP1-HP2 of haplotype 1 and HP2-HP1 of haplotype 2) at each iDMR of each haplotype.

[0116] In this study, the inventors examined 220 autosomal homologs of 5 individuals (5 individuals × 22 autosomes × 2 ploidy) and compared the inferred PofO with the PofO assigned to the parent-mother-child triplet in the true phased variant call set. All 220 homologs were correctly assigned PofO, that is, the chromosome length haplotype was correctly identified as maternal or paternal, and there were almost no phasing errors (chromosome-level mismatch error rate of SNVs: M ± SD = 0.34% ± 0.53%, range 0.03%-4.86% ( Figure 7 ); for indel: M±SD=1.93%±0.58%, range 0.98%-5.35%). Figure 7Per-chromosome results of PofO assignments of heterozygous SNVs in each of five individuals are shown for chromosomes 1-22 represented from left to right. PofO can be assigned to all homologs. Bars represent from left to right (i) all heterozygous SNVs in the truth; (ii) shared het-SNVs between the truth and nanopore; (iii) het-SNVs assigned PofO, and (iv) het-SNVs assigned the correct PofO. A small fraction of variants with incorrect PofO are sporadic phasing errors in Strand-seq or nanopore data.

[0117] To further confirm that PofO phasing extracts reliable parental information, the inventors calculated the Mendelian error rate between the inferred parental haplotypes of each child and the true variant genotypes of their parents. For the HG005 genome, the Mendelian error rate for maternal-mother and paternal-father comparisons is low (M±SD=0.27%±2.69%; calculated for non-overlapping bins of 1000 variations), while the Mendelian error rate for maternal-father and paternal-mother comparisons is high (indicating misassigned PofO; M±SD=25.75%±14.14%). For maternal-mother and paternal-father comparisons, the highest average error rate for any chromosome is 2.29% for chromosome 8 in HG002. This is less than one-eighth of the lowest average error rate for any chromosome in maternal-father and paternal-mother comparisons (19.69% for chromosome 21 in NA12878), indicating that PofO assignment is correct for all chromosomes. Similar results were obtained for the Mendelian error rates of the HG002, HG00733, NA19240, and NA12878 genomes.

[0118] Example 1.3 - Analysis of experimental data

[0119] Experiments described herein show that PofO can be assigned to chromosome homologs represented by chromosome-length haplotypes of SNVs and indels without the use of parental sequence data. Long nanopore reads provide DNA sequence information as well as PofO information in the form of DNA methylation differences between maternal and paternal alleles at known iDMRs. Strand-seq libraries provide sparse global haplotype information, which phases variants and nanopore reads to reconstruct individual homologs. The PofO ( Figure 4 ).

[0120] PofO phasing has the potential to address immediate clinical needs in the diagnosis and management of genetic disease. These clinical needs include improving variant screening and estimates of disease penetrance by co-segregating variants to each parent of families with and without the associated disease phenotype, determining which parent may be at risk for mosaicism in the case of a de novo variant, and establishing appropriate screening recommendations for pathogenic variants in genes with known PofO effects (such as observed in the cases of SDHD and SDHAF2). In addition, PofO phasing has considerable advantages over current clinical testing in facilitating cascade genetic testing, thereby providing opportunities for actionable interventions for genetic disease. 31 Contact, counseling, and testing of relatives are significant logistical and economic burdens for patients and the healthcare system, particularly given adult-onset disorders, in which parental testing is often not possible. Cascade genetic testing may be hampered by limited intrafamily communication and disrupted family structures, and has lower uptake rates among minority groups. 20 PofO phasing is designed to enable a centralized approach to cascade genetic testing across the family, thereby achieving the goal of optimal cascade genetic testing rates. 32 Importantly, the ability of PofO phasing to infer the pathogenic variant status of an affected parent with a high degree of certainty may even highlight the responsibility of alerting at-risk individuals to actionable genomic findings that may be sought primarily or secondarily throughout the genetic testing process. Similar issues are already common in clinical genetics in the case of obligate carriers, but since the approach requires testing only one individual to reconstruct the full genomic contribution from each parent, the unprecedented scale raises ethical questions if PofO phasing is integrated into mainstream clinical genetic testing.

[0121] The inventors used a well-validated set of known iDMRs. These iDMRs were reported in at least two studies or confirmed in 179 WGBS datasets from 119 blood samples and 60 tissue samples. Using this set of iDMRs, the inventors were able to assign PofOs to all tested samples in all autosomes. Although the paternal or maternal origin of methylation at an iDMR is consistent whenever only one allele is methylated, imprinted methylation can be variable in the sense that the two parental alleles can have similar amounts of DNA methylation in some tissues and individuals 27,33. This can result in the inability to assign PofO in some chromosomes in some individuals. However, with the exception of chromosome 17, which has a single iDMR, and chromosome 2, which has two iDMRs, all autosomes have at least three iDMRs, which should enable the assignment of PofO even in the presence of limited inter-individual and inter-tissue variability. In principle, this redundancy also makes PofO assignment more robust with respect to epimutations and genomic imprinting disorders that may alter DNA methylation at the iDMRs. 34 .

[0122] In addition, in several iDMRs in some samples, such as maternally methylated TRPC3 on chromosome 4 in NA12878, the inventors detected hypermethylation on an allele reported as unmethylated. This explains the low confidence scores of the PofO assignments for several chromosomes, such as chromosome 4 in NA12878, which had the lowest confidence score (60.7%). Such differences may be due to inaccuracies in methylation calls or phasing of nanopore reads, or may reflect random allelic DNA methylation. Improvements to the current iDMR list will potentially reduce such errors in the future. DNA methylation-based (classical) imprinting has been described in all placental mammals, and genomic maps of iDMRs have been established for many species, particularly mice and primates. 7,35-37 Therefore, the methods described herein can potentially be extended to other mammals.

[0123] Even when homologs are assigned the correct PofO overall, local phasing errors may result in incorrect PofO assignments for some variants. The chromosome length haplotypes constructed in the examples described herein are highly accurate, however, with an average mismatch error rate of 0.31% for SNVs and 1.89% for indels. Although the inventors identified only 61.3% of indels in the ground truth dataset, this reflects a limitation of current nanopore technology and can be improved by adding short Illumina reads. 24,38 The inventors observed rare switching errors of SNVs and indels, mainly occurring at centromeres and inversions (e.g., inversions on chromosome 8 in HG002 resulted in the largest mismatch error rate; Figure 7 ), but these typically contain little variation. Phasing errors at centromeres can be due to misaligned reads in repeat sequences, while errors at inversions are due to changes in sequence orientation that destroy the directional information used for phasing Strand-seq 8 Inversion-related phasing errors can be partially resolved using the new StrandPhaseR function that rephases variants within known inversions 39This is necessary when iDMRs fall within inversions where they may support the wrong PofO if phasing is not correct (e.g., iDMRs RIMBP3 and CDRT15P6), or when the gene of interest falls within an inversion (e.g., PMS2 in inversion chr7:5850673-6795880).

[0124] The sequencing cost for PofO phasing is relatively low, with as little as 24X nanopore and 3X Strand-seq coverage used in the examples described herein. The DNA methylation information underlying PofO assignments is robust and can be easily extracted from nanopore sequence data, while previously rare Strand-seq libraries can now be produced in large quantities (>1000) at reduced cost. 40 In principle, genomic regions with similar ancestry across distant relatives could also be exploited to partially assign PofO using large SNV datasets using sex chromosomes or parental ethnicity, but such bioinformatic approaches would require that the parents differ greatly in genetic background and would be affected by well-known ethnic biases in genomic datasets. 41 Given the ease and accuracy of PofO phasing, the current lack of alternatives to father-mother-child triads for extracting PofO information from genomic data, and the significant clinical applications of this approach, PofO phasing has the potential to become a routine component of genomic analysis.

[0125] Example 1.4 - Overview of Experimental Techniques

[0126] Nanopore sequencing and data

[0127] The inventors sequenced native DNA from Ashkenazi Jewish descendants (GM24385 or HG002) at 32× coverage on a nanopore PromethION instrument using the library preparation and sequencing protocol described previously. 6 In addition to HG002, the inventors also used the public nanopore data of HG005, HG00733, NA12878 and NA19240. The original nanopore fast5 files of HG005 and HG00733 were downloaded from the Human Pangenome Reference Consortium. 42 ; NA12878 obtained from Jain et al. 2018 43 ; and NA19240 from De Coster et al. 2019 44For HG002, HG005, and NA12878, paternal and maternal variant data and true phased variants were obtained by GIAB v4.2.1, and for NA19240 and HG00733, parental phased variants were obtained from 1KGP shapeit2 v2a 22,23 .

[0128] Nanopore data analysis

[0129] Base calling and mapping: Nanopore signal-level data were base called using guppy basecaller version 6.0.1 from Oxford Nanopore Technologies and the super accuracy model (dna_r9.4.1_450bps_sup) with default settings. Base called Nanopore reads were mapped to the human reference genome (GRCh38) using minimap2 version 2.24 with the --MD and -L options selected. 45 .

[0130] Variant calling: After alignment, variants were called from the Nanopore alignment data using Clair3 version 0.1-r10 with trained model r941_sup_g5014 and default settings 24 High-quality variant calls from Clair3 (labeled as “PASS” by the software) were then used for Strand-seq phasing (see next section).

[0131] Methylation calling: To call DNA methylation and obtain per-read CpG methylation information from nanopore data, we used nanopolish version 13.3 with default settings. 5 The per-read methylation call data were then preprocessed for downstream analysis and PofO phasing using NanoMethPhase v1.0 with the --callThreshold 1.5 parameter. 6,46 .

[0132] Strand-seq data processing, phasing, and inversion correction

[0133] The inventors obtained 45 public Strand-seq libraries of HG005 and 66 public Strand-seq libraries of HG002 from GIAB 22,47 , and obtained 230 libraries of HG00733 and 234 libraries of NA19240 from HGSVC 21 The inventors used 96 high-depth OP-Strand-seq libraries of NA12878 (clusters 5 and 6) described previously 40 .

[0134] We trimmed adapters from paired-end FASTQ files and removed short reads (<30 bp) and low-quality bases (<15) using Cutadapt 48 The inventors used Bowtie2 to align the reads to the GRCh38 human reference genome and discarded reads with a MAPQ less than 10 or not mapped to chromosomes 10-22, X, and Y 49 We used Picard (Broad Institute of MIT and Harvard, available on GitHub) to mark duplicate reads and then ran ASHLEYS QC with default settings and window sizes of 5,000,000, 2,000,000, 1,000,000, 800,000, 600,000, 400,000, and 200,000 to discard libraries with Strand-seq quality scores below 0.5. 50 .

[0135] The inventors ran BreakpointR (commit 58cce0b09d01040892b3f6abf0b11caeb403d3f5 of BreakpointR from daewoooo of the Department of Genome Sciences at the University of Washington, available on GitHub) with background set to 0.1, chr set to autosomes, and maskRegions set to the blacklist previously described 30,51 . The inventors used 8Mb bins because they were found to make it easier to connect phasing across difficult regions (such as inversions) and prevent large switching errors. The inventors used the function exportRegions with default settings to identify genomic regions suitable for phasing using Watson and Crick reads. The inventors phased bi-allelic heterozygous SNVs called from nanopore data for each sample using StrandPhaseR with num.iterations set to 3, splitPhasedReads and assume.biallelic set to TRUE, R v4.0.5 and dependency rlang v1.0.1 or higher (commit bb19557235de3d82092abdc11b3334f615525b5b from the develop branch of StrandPhaseR by daewoooo at the Department of Genome Sciences at the University of Washington, available on GitHub) 11 .

[0136] Inversions destroy the directional phase information of Strand-seq. The inventors used the R package InvertypeR (commit a5fac3b6b8264db28de1a997ad0bc062badea883 from InvertypeR / commits / main by vincent-hanlon, available on GitHub) to call inversions for each sample. 51 Briefly, we created a pair of combined files for each sample using Nanopore SNVs and added the genomic coordinates chr8:8231088-12039415 in the blacklist to ensure correct representation of common large inversions at those coordinates. We genotyped a catalog of published inversion coordinates using adjustment_method set to "all" and priors as described previously, as well as a list of emerging sample-specific strand switches identified by running BreakpointR three times on the combined files with different bin sizes. 30,51 For the latter, we used prior probabilities of 0.9, 0.05, and 0.05 for the reference, heterozygous, and homozygous genotypes, respectively. We combined inversions with a posterior probability higher than 0.95 from both call sets by discarding any inversions from the catalog call set that intersected with the de novo call set (bedtoolsintersect -vrf 0.1). We did not remove misoriented reference contigs that behaved as homozygous inversions in all samples, as they perturb phasing in the same way as inversions.

[0137] The function correctInvertedRegionPhasing in the StrandPhaseR package switches the phase of heterozygous SNVs within homozygous inversions and rephases SNVs within heterozygous inversions 39 We used sample-specific inversion calls larger than 10 kb as well as nanopore sample-specific SNV positions, with recall.phased and assume.biallelic set to TRUE, het.genotype set to "lenient", lookup.bp set to 1000000, background set to 0.1, and lookup.blacklist set to the blacklist above. The resulting chromosome-length, inversion-corrected SNV haplotypes were used to phase the nanopore reads relative to each other.

[0138] iDMR, chromosome-level haplotype and PofO detection

[0139] Validation of iDMR

[0140] The inventors collected a list of previously reported iDMRs from five previous genome-wide studies25-29 iDMRs that overlapped between 2 or more studies were merged. This resulted in 102 merged iDMRs and 326 iDMRs reported only in a single study. The inventors previously investigated imprinted methylation genome-wide using 12 nanopore sequenced cell lines and their parent-mother-child triplet sequencing information from 1KGP. 29 . The inventors examined 326 iDMRs from the above-mentioned individual studies using the same cell lines. For each CpG site with coverage > 4 within the iDMR, the methylation frequency (the fraction of reads methylated at the CpG) was calculated. The inventors then calculated the difference between the average methylation frequencies of the paternal and maternal alleles for each iDMR in each cell line. Ninety-four iDMRs with a |methylation difference| ≥ 0.25 between alleles and a PofO conflict between any of the 12 cell lines and the corresponding previous study were excluded. To further validate the remaining 232 iDMRs reported in individual studies, the inventors used WGBS datasets of 119 blood samples from 87 individuals in the Blueprint consortium and 60 tissue samples from 29 tissue types in the ENCODE and RoadMap consortia. 52-54 . Only one allele is methylated at the iDMR, so the combined methylation frequency of the two alleles from these regions is expected to be about 50% (partially methylated). Therefore, the inventors checked the partial methylation at 232 iDMRs in the WGBS dataset. For each WGBS sample, the inventors used CpGs with at least 5 mapped WGBS reads, and counted the number of CpGs with partial methylation (methylation frequency in the mapped reads between 0.35-0.65) at each iDMR. If an iDMR has at least 5 CpGs in the WGBS sample and more than 60% of the CpGs show partial methylation, the iDMR is considered partially methylated. Of the 232 iDMRs, 129 iDMRs were excluded because they were partially methylated in less than two blood or tissue samples or in less than 5% of the blood or tissue samples in which the iDMRs could be examined (i.e., the iDMR had at least 5 CpGs with coverage ≥5). Overall, the inventors collected a list of 205 known iDMRs, 102 of which were reported in multiple studies and 103 (out of 326) were reported in a single study, the most confirmed of which are listed in Table 2.

[0141] Determination of parentage

[0142] The inventors then integrated several steps to detect chromosome-level haplotypes, whose PofO are as follows:

[0143] 1. Strand-seq phasing shows sparse chromosome-level haplotypes. Phased SNVs from Strand-seq are used to phase nanopore reads to HP1 or HP2 haplotypes. Each read is marked with a phased SNV using a minimum mapping quality of 20 and a base quality of 7. If a read has at least one phased SNV from HP1 and the ratio (number of SNVs from HP1 mapped to the read / all phased SNVs mapped to the read) ≥ 0.75, the read is marked as HP1, and vice versa.

[0144] 2. All variants (SNVs and indels) were then rephased to each haplotype using the phased nanopore reads from step 1. At least 2 phased reads were required to support a variant to assign it as HP1 or HP2.

[0145] 3. All phased variants from step 2 are then used to perform secondary phase on the nanopore reads with the conditions mentioned in step 1.

[0146] 4. Extract the per-read methylation information of each nanopore read at the known iDMR and integrate it into its phase information from step 3. This enables the inventors to phase each CpG methylation in each read to HP1 or HP2 and calculate the methylation frequency (number of methylated reads / number of all reads) of each haplotype at each CpG site. The methylation frequency is then used to assign the haplotype to its PofO for each sample as follows:

[0147] At each of the 205 known iDMRs, the inventors counted CpGs (differentially methylated) with a methylation frequency difference of ≥0.35 between haplotypes. The inventors then calculated the contribution of iDMR to PofO detection for each haplotype as follows:

[0148] x=ma / n

[0149] where m is the average methylation frequency of the haplotype, a is the number of differentially methylated CpGs supporting the PofO of the haplotype, and n is the number of all CpGs at the iDMR. Only those with more than 10 detected CpGs and |a (HP1) -a (HP2) | iDMRs that account for at least 10% of all detected CpGs are considered for PofO assignment. For example, for a maternally methylated iDMR with 20 CpGs and an average methylation frequency of 0.8 at HP1 and 0.3 at HP2, if 12 CpGs show ≥0.35 methylation in HP1 compared to HP2 and 2 CpGs show ≥0.35 methylation in HP2 compared to HP1, then:

[0150] x is x=0.8×12 / 20 for HP1 as the female parent and HP2 as the male parent and x is x=0.3×2 / 20 for HP1 as the male parent and HP2 as the female parent.

[0151] On each chromosome of each haplotype that is either maternal or paternal, the value of X = ∑x will be:

[0152]

[0153] where k is the number of iDMRs considered for the chromosome. If the X of HP1 as the mother (which is the same as the X of HP2 as the father) is greater than the X of HP2 as the mother (which is the same as the X of HP1 as the father), then HP1 is of maternal origin and HP2 is of paternal origin, and vice versa. In addition, if, for example, HP1 is assigned as the maternal homolog and HP2 is assigned as the paternal homolog, the inventors calculated the confidence score of the PofO assignment as X (HP1母本) / (X (HP1母本) +X (HP2母本) ) or X (HP2父本) / (X (HP2父本) +X (HP1父本) ).

[0154] 5-Finally, use the results from step 4 to assign the phased variants from step 2 their PofO.

[0155] All steps are integrated into a workflow and tool, PatMat, and instructions are provided on GitHub (available as PatMat from vahidAK).

[0156] Mendel's error

[0157] To validate the PofO assignments, we calculated the frequency of a Mendelian error between the PofO assigned haplotypes and the parental genotypes. We obtained the genotypes of the parents of HG002 and HG005 from GIAB (v4.2.1), the genotypes of the parents of HG00733 and NA19240 from 1KGP (v2a), and the genotypes of the parents of NA12878 from Byrska-Bishop et al. 2021 22,23,47,55. For each parent-child pair, the inventors checked the loci at which they found a phased heterozygous genotype for the child and a heterozygous or homozygous alternative genotype for the parent. When the child had the maternal reference allele and the mother was a homozygous alternative, the inventors called this a Mendelian error (the paternal allele for the child and the father's genotype were similar). The inventors did this for 1000 non-overlapping bins of variants and calculated the error rate as the number of such Mendelian errors divided by the number of variants examined. The inventors plotted the resulting error rates on chromosomes using RIdeogram 56 .

[0158] Although a number of exemplary aspects and embodiments have been discussed above, those skilled in the art will recognize certain modifications, permutations, additions and sub-combinations thereof. It is therefore intended that the following appended claims and claims introduced thereafter be interpreted as including all such modifications, permutations, additions and sub-combinations consistent with the broadest interpretation of the entire specification.

[0159] For example, although various exemplary embodiments have been described with reference to the determination of chromosome length haplotypes, and it is not believed that the determination of chromosome length haplotypes will yield the most accurate results, in alternative embodiments a smaller portion of the sequence data may be collected to produce at least a partial haplotype, as long as at least a few iDMR-associated variations are captured to allow determination of the haplotype of the allele of interest and the methylation state of the iDMR associated with the allele, thereby determining the parentage of the allele.

[0160] References

[0161] The following references are related to the subject matter described herein. Each of the following references is incorporated herein by reference in its entirety.

[0162] 1.Zheng,GXYet al.Haplotyping germline and cancer genomes with high-throughput linked-read sequencing.Nat.Biotechnol.34,303-311(2016).

[0163] 2. Wenger, AM et al. Accurate circular consensus long-read sequencing improves variant detection and assembly of a human genome. Nat. Biotechnol. 37, 1155-1162 (2019).

[0164] 3.Cheng,H.et al.Haplotype-resolved assembly of diploid genomeswithout parental data.Nat.Biotechnol.(2022)doi:10.1038 / s41587-022-01261-x.

[0165] 4.H.,L.A.et al.Detection and mapping of 5-methylcytosine and5-hydroxymethylcytosine with nanopore MspA.Proc.Natl.Acad.Sci.110,18904-18909(2013).

[0166] 5.Simpson,J.T.et al.Detecting DNA cytosine methylation using nanoporesequencing.Nat.Methods 14,407(2017).

[0167] 6.Akbari,V.et al.Megabase-scale methylation phasing using nanoporelong reads and NanoMethPhase.Genome Biol.22,68(2021).

[0168] 7.Gigante,S.et al.Using long-read sequencing to detect imprinted DNAmethylation.Nucleic Acids Res.47,e46-e46(2019).

[0169] 8. D.et al.Direct chromosome-length haplotyping by single-cell sequencing.Genome Res.26,1565-1574(2016).

[0170] 9.Selvaraj,S.,R Dixon,J.,Bansal,V.&Ren,B.Whole-genome haplotypereconstruction using proximity-ligation and shotgunsequencing.Nat.Biotechnol.31,1111-1118(2013).

[0171] 10.Falconer,E.et al.DNA template strand sequencing of single-cellsmaps genomic rearrangements at high resolution.Nat.Methods 9,1107-1112(2012).

[0172] 11.Porubsky,D.et al.Dense and accurate whole-chromosome haplotypingof individual genomes.Nat.Commun.8,1293(2017).

[0173] 12.Porubsky,D.et al.Fully phased human genome assembly withoutparental data using single-cell strand sequencing and longreads.Nat.Biotechnol.39,302-308(2021).

[0174] 13.Van Der Mey,A.L.,Maaswinkel-Mooy,P.,Cornelisse,C.,Schmidt,P.&VanDe Kamp,J.P.GENOMIC IMPRINTING IN HEREDITARY GLOMUS TUMOURS:EVIDENCE FOR NEWGENETIC THEORY.Lancet 334,1291-1294(1989).

[0175] 14.Knowles,J.W.,Rader,D.J.&Khoury,M.J.Cascade Screening for FamilialHypercholesterolemia and the Use of Genetic Testing.JAMA 318,381-382(2017).

[0176] 15.Richards,S.et al.Standards and guidelines for the interpretationof sequence variants:a joint consensus recommendation of the American Collegeof Medical Genetics and Genomics and the Association for MolecularPathology.Genet.Med.17,405-424(2015).

[0177] 16.Hensen,E.F.et al.Somatic loss of maternal chromosome 11 causesparent-of-origin-dependent inheritance in SDHD-linked paraganglioma andphaeochromocytoma families.Oncogene 23,4076-4083(2004).

[0178] 17.Huai-Xiang,H.et al.SDH5,a Gene Required for Flavination ofSuccinate Dehydrogenase,Is Mutated in Paraganglioma.Science(80-.).325,1139-1142(2009).

[0179] 18.Hampel,H.Genetic counseling and cascade genetic testing in Lynchsyndrome.Fam.Cancer 15,423-427(2016).

[0180] 19.Lee,P.W.C.et al.Evaluating the impact of universal Lynch syndromescreening in a publicly funded healthcare system.Cancer Med.9,6507-6514(2020).

[0181] 20.Braley,E.F.et al.Patient ethnicity and cascade genetic testing:adescriptive study of a publicly funded hereditary cancer program.Fam.Cancer(2021)doi:10.1007 / s10689-021-00270-0.

[0182] 21.Chaisson,M.J.P.et al.Multi-platform discovery of haplotype-resolved structural variation in human genomes.Nat.Commun.10,1784(2019).

[0183] 22.Zook,J.M.et al.An open resource for accurately benchmarking smallvariant and reference calls.Nat.Biotechnol.37,561-566(2019).

[0184] 23.Auton,A.et al.A global reference for human geneticvariation.Nature526,68-74(2015).

[0185] 24.Zheng,Z.et al.Symphonizing pileup and full-alignment for deeplearning-based long-read variant calling.bioRxiv 2021.12.29.474431(2021)doi:10.1101 / 2021.12.29.474431.

[0186] 25.Court,F.et al.Genome-wide parent-of-origin DNA methylationanalysis reveals the intricacies of human imprinting and suggests a germlinemethylation-independent mechanism of establishment.Genome Res.24,554-569(2014).

[0187] 26.Joshi,R.S.et al.DNA Methylation Profiling of Uniparental DisomySubjects Provides a Map of Parental Epigenetic Bias in the HumanGenome.Am.J.Hum.Genet.99,555-566(2016).

[0188] 27.Zink,F.et al.Insights into imprinting from parent-of-origin phasedmethylomes and transcriptomes.Nat.Genet.50,1542-1552(2018).

[0189] 28.Hernandez Mora,J.R.et al.Characterization of parent-of-originmethylation using the Illumina Infinium MethylationEPIC arrayplatform.Epigenomics 10,941-954(2018).

[0190] 29.Akbari,V.et al.Genome-Wide Detection of Imprinted DifferentiallyMethylated Regions Using Nanopore Sequencing.bioRxiv2021.07.17.452734(2021)doi:10.1101 / 2021.07.17.452734.

[0191] 30.Porubsky,D.et al.breakpointR:an R / Bioconductor package to localizestrand state changes in Strand-seq data.Bioinformatics 36,1260-1261(2020).

[0192] 31.Miller,D.T.et al.ACMG SF v3.0 list for reporting of secondaryfindings in clinical exome and genome sequencing:a policy statement of theAmerican College of Medical Genetics and Genomics(ACMG).Genet.Med.23,1381-1390(2021).

[0193] 32.Offit,K.et al.Cascading After Peridiagnostic Cancer GeneticTesting:An Alternative to Population-Based Screening.J.Clin.Oncol.38,1398-1408(2020).

[0194] 33.Prickett,A.R.&Oakey,R.J.A survey of tissue-specific genomicimprinting in mammals.Mol.Genet.Genomics 287,621-630(2012).

[0195] 34.Monk,D.,Mackay,D.J.G.,Eggermann,T.,Maher,E.R.&Riccio,A.Genomicimprinting disorders:lessons on how genome,epigenome and environmentinteract.Nat.Rev.Genet.20,235-248(2019).

[0196] 35.Renfree,M.B.,Hore,T.A.,Shaw,G.,Marshall Graves,J.A.&Pask,A.J.Evolution of Genomic Imprinting:Insights from Marsupials andMonotremes.Annu.Rev.Genomics Hum.Genet.10,241-262(2009).

[0197] 36.Cheong,C.Y.et al.Germline and somatic imprinting in the nonhumanprimate highlights species differences in oocyte methylation.Genome Res.25,611-623(2015).

[0198] 37.Xie,W.et al.Base-Resolution Analyses of Sequence and Parent-of-Origin Dependent DNA Methylation in the Mouse Genome.Cell 148,816-831(2012).

[0199] 38.Shafin,K.et al.Haplotype-aware variant calling with PEPPER-Margin-DeepVariant enables high accuracy in nanopore long-reads.Nat.Methods 18,1322-1332(2021).

[0200] 39.Porubsky,D.et al.Haplotype-resolved inversion landscape revealshotspots of mutational recurrence associated with genomic disorders.bioRxiv2021.12.20.472354(2021)doi:10.1101 / 2021.12.20.472354.

[0201] 40.Hanlon,V.C.T.et al.Construction of Strand-seq libraries in opennanoliter arrays.Cell Reports Methods 2,100150(2022).

[0202] 41.Ledford,H.Cancer geneticists tackle troubling ethnic bias instudies.Nature 154-155(2019)doi:10.1038 / d41586-019-01080-2.PMID:30967668.

[0203] 42.Shafin,K.et al.Nanopore sequencing and the Shasta toolkit enableefficient de novo assembly of eleven human genomes.Nat.Biotechnol.(2020)doi:10.1038 / s41587-020-0503-6.

[0204] 43.Jain,M.et al.Nanopore sequencing and assembly of a human genomewith ultra-long reads.Nat.Biotechnol.36,338-345(2018).

[0205] 44.De Coster,W.et al.Structural variants identified by OxfordNanopore PromethION sequencing of the human genome.Genome Res.29,1178-1187(2019).

[0206] 45.Li,H.Minimap2:pairwise alignment for nucleotidesequences.Bioinformatics 34,3094-3100(2018).

[0207] 46.Akbari,V.et al.NanoMethPhase.Zenodo(2021)doi:10.5281 / zenodo.4474430.

[0208] 47.Wagner,J.et al.Benchmarking challenging small variants with linkedand long reads.bioRxiv 2020.07.24.212712(2021)doi:10.1101 / 2020.07.24.212712.

[0209] 48.Martin,M.Cutadapt removes adapter sequences from high-throughputsequencing reads.EMBnet.journal;Vol 17,No 1 Next Gener.Seq.Data Anal.-10.14806 / ej.17.1.200(2011).

[0210] 49.Langmead,B.&Salzberg,S.L.Fast gapped-read alignment with Bowtie2.Nat.Methods 9,357-359(2012).

[0211] 50.Gros,C.,Sanders,A.D.,Korbel,J.O.,Marschall,T.&Ebert,P.ASHLEYS:automated quality control for single-cell Strand-seq data.Bioinformatics 37,3356-3357(2021).

[0212] 51.Hanlon,V.C.T.,Mattsson,C.-A.,Spierings,D.C.J.,Guryev,V.&Lansdorp,P.M.InvertypeR:Bayesian inversion genotyping with Strand-seq data.BMCGenomics 22,582(2021).

[0213] 52.Stunnenberg,H.G.et al.The International Human EpigenomeConsortium:A Blueprint for Scientific Collaboration and Discovery.Cell(2016)doi:10.1016 / j.cell.2016.11.007.

[0214] 53.Consortium,E.P.An integrated encyclopedia of DNA elements in thehuman genome.Nature 489,57(2012).

[0215] 54.Bernstein,B.E.et al.The NIH Roadmap Epigenomics MappingConsortium.Nat.Biotechnol.28,1045-1048(2010).

[0216] 55.Byrska-Bishop,M.et al.High coverage whole genome sequencing of theexpanded 1000 Genomes Project cohort including 602trios.bioRxiv2021.02.06.430068(2021)doi:10.1101 / 2021.02.06.430068.

[0217] 56.Hao,Z.et al.RIdeogram:drawing SVG graphics to visualize and mapgenome-wide data on the idiograms.PeerJ Comput.Sci.6,e251(2020).

[0218] 57.Murphy,N.M.et al.Haplotyping the human leukocyte antigen systemfrom single chromosomes.Sci Rep 6,30381(2016).https: / / doi.org / 10.1038 / srep30381.

[0219] 58.Ramagopalan et al.Parent-of-origin of HLA-DRB1*1501 and age ofonset of multiple sclerosis.”Journal of Human Genetics 54(2009):547-549.

[0220] 59.Deighton and Walker.The familial nature of rheumatoidarthritis.Annals of the Rheumatic Diseases 1991,50:62-65.

[0221] 60.Naranbhai V.et al.HLA-A*03 and response to immune checkpointblockade in cancer:an epidemiological biomarker study.Lancet Oncol.2022 Jan;23(1):172-184.doi:10.1016 / S1470-2045(21)00582-9.Epub2021 Dec 9.

[0222] 61.de la Cruz,M.R.et al.Cis-acting factors causing secondaryepimutations;impact on the risk for cancer and other diseases.Cancers(Basel).2021 Oct;13(19):4807.Published online 2021 Sep 26.doi:10.3390 / cancers13194807.

[0223] 62.Hitchins,M.P.et al.Dominantly inherited constitutional epigeneticsilencing of MLH1 in a cancer-affected family is linked to a singlenucleotide variant within the 5'UTR.Cancer Cell.2011 Aug16;20(2):200-13.doi:10.1016 / j.ccr.2011.07.003.

[0224] 63.Chan,T.et al.Heritable germline epimutation of MSH2 in a familywith hereditary nonpolyposis colorectal cancer.Nat Genet 38,1178-1183(2006).https: / / doi.org / 10.1038 / ng1866.

[0225] 64.Evans D.G.R.et al.A Dominantly Inherited 5'UTR Variant CausingMethylation-Associated Silencing of BRCA1 as a Cause of Breast and OvarianCancer.Am J Hum Genet.2018 Aug 2;103(2):213-220.doi:10.1016 / j.ajhg.2018.07.002.PMID:30075112;PMCID:PMC6080768.

[0226] 65.Dixon K.et al.Defining the heterogeneity of unbalanced structuralvariation underlying breast cancer susceptibility by nanopore genomesequencing.Eur J Hum Genet.2023 Feb 16.doi:10.1038 / s41431-023-01284-1.Epubahead of print.PMID:36797466.

[0227] 66.Thibodeau,M.L.,et al.Improved structural variant interpretationfor hereditary cancer susceptibility using long-read sequencing.GenetMed.2020 Nov;22(11):1892-1897.doi:10.1038 / s41436-020-0880-8.Epub2020 Jul6.PMID:32624572;PMCID:PMC7605438.

[0228] 67.Weinberg,C.R.Methods for detection of parent-of-origin effects ingenetic studies of case-parents triads.Am J Hum Genet.1999 Jul;65(1):229-235.doi:10.1086 / 302466.

[0229] 68.Baysal,B.E.et al.Mutations in SDHD,a mitochondrial complex IIgene,in hereditary paraganglioma.Science.Feb 42000;287(5454):848-51.doi:10.1126 / science.287.5454.848.

[0230] 69.Bayley J.P.et al.Paraganglioma and pheochromocytoma upon maternaltransmission of SDHD mutations.BMC Med Genet.Oct 102014;15:111.doi:10.1186 / s12881-014-0111-8.

[0231] 70.Hampel H.et al.Assessment of Tumor Sequencing as a Replacement forLynch Syndrome Screening and Current Molecular Tests for Patients WithColorectal Cancer.JAMA Oncol.06 2018;4(6):806-813.doi:10.1001 / jamaoncol.2018.0104.

[0232] 71.Beard V.K.et al.Genetic testing in families with hereditarycolorectal cancer in British Columbia and Yukon:a retrospective cross-sectional analysis.CMAJ Open.2020 Oct-Dec 2020;8(4):E637-E642.doi:10.9778 / cmajo.20190167.

[0233] 72.LaDuca H.et al.A clinical guide to hereditary cancer paneltesting:evaluation of gene-specific cancer associations and sensitivity ofgenetic testing criteria in a cohort of 165,000 high-risk patients.GenetMed.Feb 2020;22(2):407-415.doi:10.1038 / s41436-019-0633-8.

[0234] 73.Schrader K.A.,et al.Germline Variants in Targeted Tumor SequencingUsing Matched Normal DNA.JAMA Oncology.2016;2(1):104-111.doi:10.1001 / jamaoncol.2015.5208.

[0235] 74.Frey M.K.,et al.Prospective Feasibility Trial of a Novel Strategyof Facilitated Cascade Genetic Testing Using Telephone Counseling.J ClinOncol.May 1 2020;38(13):1389-1397.doi:10.1200 / jco.19.02005。

Claims

1. A method for assigning parentage to a haplotype, a subhaplotype, or an allele associated with the haplotype in a subject, the method comprising: generating a chromosome length haplotype of the subject's genome; determining the differential methylation status of at least one imprinted differentially methylated region (iDMR) of each autosome of the subject; as well as The determined differential methylation status of at least one iDMR for each autosome of the subject is associated with each of the chromosome length haplotypes to assign a parentage to each of the chromosome length haplotypes.

2. The method according to claim 1, wherein the step of generating a chromosome length haplotype of the genome comprises determining the chromosome length haplotype by at least one sequencing method.

3. The method for assigning parentage to a haplotype, a subhaplotype, or an allele associated with the haplotype according to claim 2, wherein the step of generating a chromosome length haplotype of the genome comprises: (i) performing a first sequencing method capable of determining long-range phase information; (ii) performing a second sequencing method capable of accurately determining at least short sequence reads and assigning them to haplotypes; (iii) generating a chromosome length haplotype using the overlapping results of the first sequencing method and the second sequencing method; (iv) determining the methylation status of the at least one iDMR associated with each autosome; as well as (v) using the determined methylation status of the at least one iDMR associated with each autosome, assigning a parentage to each of the chromosome length haplotypes based on the methylation status of the at least one iDMR associated with each autosome.

4. A method according to claim 3, wherein the results of the first sequencing method and the second sequencing method performed in steps (i) and (ii) are iteratively used to phase the variation in step (iii), wherein the variation optionally includes single nucleotide polymorphisms (SNPs) and / or indels.

5. The method according to any one of claims 3 or 4, wherein step (iv) is performed simultaneously with step (ii) using the second sequencing method.

6. The method of assigning parentage to a haplotype, a subhaplotype, or an allele associated with the haplotype according to claim 2, comprising: (i) determining at least a portion of the sequence of each of the haplotypes using a first sequencing method capable of determining long-range phase information; (ii) determining the differential methylation status of the at least one iDMR of each autosome of the subject using a second sequencing method; (iii) generating the chromosome length haplotype using the results of the first sequencing method and the second sequencing method; as well as (iv) using the results of the second sequencing method, assigning a parentage to each of the chromosome length haplotypes based on the differential methylation status of the at least one iDMR of each autosome of the subject.

7. The method according to any one of claims 2 to 6, comprising: obtaining a sample from the subject; as well as The at least one sequencing method is performed on the sample.

8. The method of claim 7, wherein the sample comprises a blood sample or a sample of culturable living cells.

9. The method according to any one of claims 3 to 8, wherein the first sequencing method comprises Strand-seq, Hi-C, 3C-Seq, 10X, continuous long reads (CLR), high fidelity (HiFi), Pore-C or a combination thereof.

10. The method according to any one of claims 3 to 9, wherein the second sequencing method comprises nanopore sequencing, single molecule real-time (SMRT) sequencing, bisulfite sequencing, or a combination thereof.

11. The method according to any one of claims 3 to 10, wherein the first sequencing method and the second sequencing method are the same sequencing method.

12. The method of any one of claims 3 to 10, wherein the first sequencing method comprises StrandSeq and the second sequencing method comprises Nanopore sequencing, wherein Nanopore data is used to call variants, and wherein at least some of the variants are phased with Strand-seq in an inversion-aware manner to construct sparse chromosome length haplotypes.

13. The method for assigning parentage to a haplotype or an allele associated with the haplotype according to claim 2, comprising: (i) generating sparse chromosome-level haplotypes using a first sequencing method; (ii) phasing sequencing data obtained using a second sequencing method using the sparse chromosome-level haplotype to generate a first haplotype and a second haplotype; (iii) re-phasing all sequence variations identified by the first sequencing method to each of the first haplotype and the second haplotype using the phased sequencing data from step (ii); (iv) performing secondary phasing on the sequencing data obtained using the second sequencing method; as well as (v) extracting per-read methylation information of each read obtained by the second sequencing method, and integrating the per-read methylation information at the at least one iDMR of each autosome of the subject with the phase information obtained in step (iv) to assign each read to the first haplotype or the second haplotype, and determine the parentage of each of the first haplotype and the second haplotype.

14. A method according to claim 13, wherein the first sequencing method comprises Strand-seq, the second sequencing method comprises nanopore sequencing, and the multiple phased single nucleotide variations (SNVs) determined using the first sequencing method are used to phase the sequencing data obtained using the second sequencing method in step (ii).

15. The method according to claim 14, wherein If each of the reads from the second sequencing method has a greater number of SNVs from the first haplotype mapped to the read than the number of SNVs from the second haplotype mapped to the read, marking the read as being from the first haplotype; and Wherein if each of the reads from the second sequencing method has a number of SNVs from the second haplotype mapped to the read that is greater than the number of SNVs from the first haplotype mapped to the read, the read is marked as being from the second haplotype.

16. The method according to any one of claims 1 to 15, wherein the chromosome length haplotype comprises a maternal haplotype and a paternal haplotype.

17. A method for assigning parentage to a haplotype, a subhaplotype, or an allele associated with said haplotype, the method comprising: Generates the haplotype of the genome; determining the differential methylation status of at least one imprinted differentially methylated region (iDMR) associated with each of the haplotypes; and The determined differential methylation state of the at least one iDMR is associated with each of the haplotypes to assign a parentage to each of the haplotypes.

18. The method of claim 17, wherein the haplotype is a partial haplotype or a chromosome length haplotype.

19. A method for assigning parentage to a haplotype, a subhaplotype, or an allele associated with the haplotype using only a sample obtained from a subject, the method being achieved by assessing the methylation status of an imprinted differentially methylated region (iDMR) associated with each haplotype in a pair of haplotypes of the subject.

20. The method according to any one of claims 1 to 19, further comprising: selecting an undesirable subhaplotype or allele from one of the haplotypes; Determining the parent associated with the undesired subhaplotype or allele based on the determination of the parentage of the haplotype associated with the undesired subhaplotype or allele; and Cascade genetic testing is performed on family members of the parent associated with the undesired subhaplotype or allele.

21. The method according to any one of claims 1 to 19, further comprising: selecting a subhaplotype or allele of interest from one of the haplotypes; Determining the parent associated with the subhaplotype or allele of interest based on the determination of the parentage of the haplotype associated with the subhaplotype or allele of interest; and Cascade genetic testing is performed on family members of the parent associated with the subhaplotype or allele of interest.

22. The method of claim 20, wherein the undesirable allele is an allele of SDHD, SDHAF2, MAX, BRCA1, BRCA2, MLH1, MSH2, MSH6, PMS2, EPCAM, ATM, PALB2, TP53, APC, ACTA2, ACTC1, ACVRL1, AIP, ALK, ANKRD26, APOB, ARMC5, ATP7B, ATR, AXIN2, BAG3, BAP1, BARD1, BLM, BMPR1A, BRIP1, BTD, BUB1B, CACNA1S, CASQ2, CASR, CDC73, CDH1, CDK4 , CDKN1B, CDKN1C, CDKN2A, CEBPA, CFTR, CHEK1, CHEK2, COL3A1, CPA1, CTC1, CTNNA1, CTRC, CYLD, DDB2, DDX41, DES, DICER1, DIS3L2, DKC1, DLST, DROSHA, DSC2, DSG2, DSP, EGFR, EGLN1, ENG, ERCC1, ERCC2, ERCC3, ERCC4, ERCC5, ETV6, EXT1, EXT2, EZH2, FAM175A, FAN1, FANCA, FANCB, FANCC, FANCD2, FANCE, FA NCF, FANCG, FANCI, FANCL, FANCM, FBN1, FH, FLCN, FLNC, FOCAD, GAA, GALNT12, GATA2, GLA, GPC3, GREM1, HFE, HNF1A, HOXB13, HRAS, KCNH2, KCNQ1, KIF1B, KIT, LDLR, LMNA, LZTR1, MC1R, MEN1, MET, MITF, MLH3, MRE11, MRE11A, MSH3, MUTYH, MYBPC3, MYH11, MYH7, MYL2, MYL3, NBN, NF1, NF2, NHP2, NOP10, NTHL1, OTC, PAX5, PALLD, PCSK9, PDGFRA, PHOX2B, PIK3CA, PKP2, POLD1, POLE, POLH, POT1, PRKAG2, PRKAR1A, PRSS1, PTCH1, PTCH2, PTEN, RAB43, RABL3, RAD1, RA D50, RAD51C, RAD51D, RB1, RBM20, RECQL, RECQL4, RECQL5, REST, RET, RINT1, RPE65, RPS20, RUNX1, RYR1, RYR2, SAMD9, SAMD9L, SCN5A, SDHA, SDHB, SDHC,SLC45A2, SLX4, SMAD3, SMAD4, SMARCA4, SMARCB1, SMARCE1, SPINK1, SRP72, STK11, SUFU, TERC, TERT, TGFBR1, TGFBR2, TINF2, TMEM127, TMEM43, TNNC1, TNNI3, TNNT2, TP53I3, TPM1, TRDN, TRIP13, TSC1, TSC2, TTN, TTR, TYR, VHL, WRAP53, WRN, WT1, XPA, XPC or XRCC2, optionally wherein the undesirable allele is BRCA1 187delAG, BRCA1 5385insC or BRCA2 6174delT.

23. The method of any one of claims 1 to 19, further comprising determining whether two or more variants are cis-phased or trans-phased.

24. A method according to any one of claims 1 to 19, comprising: If the allele is a mutant form of succinate dehydrogenase complex D subunit (SDHD) and the parent is the father, periodically assess the subject and / or perform cascade genetic testing for paraganglioma or pheochromocytoma, or if the allele is a mutant form of SDHD and the parent is the mother, perform cascade genetic testing; If the allele is a mutant form of succinate dehydrogenase complex assembly factor 2 (SDHAF2) and the parent is the father, periodically assessing the subject for paraganglioma and / or performing cascade genetic testing; or If the allele is a mutant form of the MYC-associated factor X gene (MAX) and the parent is the father, the subject is regularly evaluated for pheochromocytoma and / or undergoes cascade genetic testing.

25. The method according to any one of claims 1 to 19, comprising: If the allele is a mutant form of ATM, BRCA1, BRCA2, MLH1, MSH2, MSH6, EPCAM, PMS2, PALB2, TP53, CHEK2, BRIP1, RAD51C, RAD51D or MUTYH and the parent is determined to be a parent with a family history of pancreatic cancer, pancreatic cancer screening for the subject is initiated at the age of 50 years or 10 years younger than the earliest pancreatic cancer diagnosed in the family, whichever is earlier.

26. The method of any one of claims 1 to 19, further comprising predicting similar pharmacogenomic outcomes for parents who have been identified as carrying an undesirable haplotype, subhaplotype, or allele by identifying a parental source of the undesirable haplotype, subhaplotype, or allele in a subject who has been determined to have a particular pharmacogenomic outcome.

27. The method of any one of claims 1 to 19, further comprising predicting similar pharmacogenomic outcomes for parents identified as carrying a desired haplotype, subhaplotype, or allele by identifying a parental source of the desired haplotype, subhaplotype, or allele in the subject who has been determined to have a specific pharmacogenomic outcome.

28. The method according to any one of claims 1 to 19, further comprising: determining whether the haplotype, subhaplotype, or allele is an HLA haplotype, subhaplotype, or allele associated with a disease risk having a known parent-of-origin effect; and If the haplotype, subhaplotype or allele is determined to be an HLA haplotype, subhaplotype or allele associated with a disease risk having a known parent-of-origin effect, the subject's disease risk is regularly monitored based on the known parent-of-origin effect.

29. The method of any one of claims 1 to 19, wherein the haplotype, subhaplotype or allele is an HLA haplotype, subhaplotype or allele, the method comprising selecting a cancer vaccine or cancer therapy for a parent of the subject based on determining that the parent is the parent of the HLA haplotype, subhaplotype or allele in the subject.

30. The method according to any one of claims 1 to 19, further comprising comparing a DNA sequence obtained from a sample at a crime scene with at least a portion of the haplotype, subhaplotype or allele and parentage information obtained from a subject, and if the DNA sequence obtained from the sample at the crime scene matches the DNA sequence of one of the haplotypes, subhaplotypes or alleles obtained from the subject, concluding that the identified parentage of the subject is associated with the crime scene.

31. The method of any one of claims 1 to 19, wherein the haplotype, subhaplotype, or allele comprises a significant de novo germline variation, wherein a biological parent of the subject tested negative for the germline variation, the method further comprising determining the parentage of the de novo germline variation to identify a biological parent at potential risk for recurrence of the germline variation or determining that the biological parent has a risk of postzygotic somatic mosaicism indicating a risk of the biological parent developing a disease.

32. The method of any one of claims 1 to 19, further comprising comparing haplotypes from two individuals with corresponding mutations to identify the ancestral mutation by identifying the ancestral haplotype.

33. The method of any one of claims 1 to 19, further comprising administering therapy to a parent of the parental origin determined to have an undesirable haplotype, subhaplotype, or allele, wherein the therapy optionally comprises antisense oligonucleotides, allele-specific targeting constructs, or allele-specific gene editing targeting the undesirable allele.

34. The method of claim 33, wherein the undesired allele comprises a mutation associated with a disorder having a dominant negative effect, optionally associated with a single nucleotide variation (SNV), copy number variation (CNV), indel, methylation or a triplet.

35. The method of any one of claims 1 to 19, further comprising detecting an abnormal methylation pattern in at least one of the haplotypes, optionally wherein the abnormal methylation pattern is detected in MLH1, MSH2 or BRCA2.

36. The method of any one of claims 1 to 19, further comprising performing a structural variation characterization of at least one of the haplotypes.

37. The method of any one of claims 1 to 19, wherein the subject is a mammal, optionally a human.

38. An apparatus for determining the parentage of a haplotype, a subhaplotype, or an allele associated with said haplotype, the apparatus comprising: a first sequencing device, the first sequencing device being used to perform a first sequencing technique to provide long-range phase information; a second sequencing device for performing a second sequencing technique to provide a differential methylation status of at least one iDMR associated with the haplotype; and A processor is configured to analyze the long-range phase information and the differential methylation state of the at least one iDMR to determine the parentage of the haplotype, subhaplotype, or allele associated with the haplotype.

39. A kit for determining the parentage of a haplotype, a subhaplotype, or an allele associated with the haplotype, the kit comprising: instructions for performing a first sequencing technique to provide long-range phase information; instructions for performing a second sequencing technique to provide a differential methylation status of at least one iDMR associated with the haplotype; as well as Instructions for combining the long-range phase information and the differential methylation status of the at least one iDMR to determine the parentage of the haplotype, subhaplotype, or allele associated with the haplotype.

Images