Haplotype Phasing Research Articles

A New Pedigree-Based SNP Haplotype Method for Genomic Polymorphism and Genetic Studies.

Single nucleotide polymorphisms (SNPs) are usually the most frequent genomic variants. Directly pedigree-phased multi-SNP haplotypes provide a more accurate view of polymorphic population genomic structure than individual SNPs. The former are, therefore, more useful in genetic correlation with subject phenotype. We describe a new pedigree-based methodology for generating non-ambiguous SNP haplotypes for genetic study. SNP data for haplotype analysis were extracted from a larger Type 1 Diabetes Genetics Consortium SNP dataset based on minor allele frequency variation and redundancy, coverage rate (the frequency of phased haplotypes in which each SNP is defined) and genomic location. Redundant SNPs were eliminated, overall haplotype polymorphism was optimized and the number of undefined haplotypes was minimized. These edited SNP haplotypes from a region containing HLA-DRB1 (DR) and HLA-DQB1 (DQ) both correlated well with HLA-typed DR,DQ haplotypes and differentiated HLA-DR,DQ fragments shared by three pairs of previously identified megabase-length conserved extended haplotypes. In a pedigree-based genetic association assay for type 1 diabetes, edited SNP haplotypes and HLA-typed HLA-DR,DQ haplotypes from the same families generated essentially identical qualitative and quantitative results. Therefore, this edited SNP haplotype method is useful for both genomic polymorphic architecture and genetic association evaluation using SNP markers with diverse minor allele frequencies.

RaPID: ultra-fast, powerful, and accurate detection of segments identical by descent (IBD) in biobank-scale cohorts

While genetic relatedness, usually manifested as segments identical by descent (IBD), is ubiquitous in modern large biobanks, current IBD detection methods are not efficient at such a scale. Here, we describe an efficient method, RaPID, for detecting IBD segments in a panel with phased haplotypes. RaPID achieves a time and space complexity linear to the input size and the number of reported IBDs. With simulation, we showed that RaPID is orders of magnitude faster than existing methods while offering competitive power and accuracy. In UK Biobank, RaPID identified 3,335,807 IBDs with a length ≥ 10 cM among 223,507 male X chromosomes in 11 min.

Phasing quality assessment in a brown layer population through family- and population-based software

BackgroundHaplotype data contains more information than genotype data and provides possibilities such as imputing low frequency variants, inferring points of recombination, detecting recurrent mutations, mapping linkage disequilibrium (LD), studying selection signatures, estimating IBD probabilities, etc. In addition, haplotype structure is used to assess genetic diversity and expected accuracy in genomic selection programs. Nevertheless, the quality and efficiency of phasing has rarely been a subject of thorough study but was assessed mainly as a by-product in imputation quality studies. Moreover, phasing studies based on data of a poultry population are non-existent. The aim of this study was to evaluate the phasing quality of FImpute and Beagle, two of the most used phasing software.ResultsWe simulated ten replicated samples of a layer population comprising 888 individuals from a real SNP dataset of 580 k and a pedigree of 12 generations. Chromosomes analyzed were 1, 7 and 20. We measured the percentage of SNPs that were phased equally between true and phased haplotypes (Eqp), proportion of individuals completely correctly phased, number of incorrectly phased SNPs or Breakpoints (Bkp) and the length of inverted haplotype segments. Results were obtained for three different groups of individuals, with no parents or offspring genotyped in the dataset, with only one parent, and with both parents, respectively. The phasing was performed with Beagle (v3.3 and v4.1) and FImpute v2.2 (with and without pedigree). Eqp values ranged from 88 to 100%, with the best results from haplotypes phased with Beagle v4.1 and FImpute with pedigree information and at least one parent genotyped. FImpute haplotypes showed a higher number of Bkp than Beagle. As a consequence, switched haplotype segments were longer for Beagle than for FImpute.ConclusionWe concluded that for the dataset applied in this study Beagle v4.1 or FImpute with pedigree information and at least one parent genotyped in the data set were the best alternatives for obtaining high quality phased haplotypes.

High-quality, genome-wide SNP genotypic data for pedigreed germplasm of the diploid outbreeding species apple, peach, and sweet cherry through a common workflow.

High-quality genotypic data is a requirement for many genetic analyses. For any crop, errors in genotype calls, phasing of markers, linkage maps, pedigree records, and unnoticed variation in ploidy levels can lead to spurious marker-locus-trait associations and incorrect origin assignment of alleles to individuals. High-throughput genotyping requires automated scoring, as manual inspection of thousands of scored loci is too time-consuming. However, automated SNP scoring can result in errors that should be corrected to ensure recorded genotypic data are accurate and thereby ensure confidence in downstream genetic analyses. To enable quick identification of errors in a large genotypic data set, we have developed a comprehensive workflow. This multiple-step workflow is based on inheritance principles and on removal of markers and individuals that do not follow these principles, as demonstrated here for apple, peach, and sweet cherry. Genotypic data was obtained on pedigreed germplasm using 6-9K SNP arrays for each crop and a subset of well-performing SNPs was created using ASSIsT. Use of correct (and corrected) pedigree records readily identified violations of simple inheritance principles in the genotypic data, streamlined with FlexQTL software. Retained SNPs were grouped into haploblocks to increase the information content of single alleles and reduce computational power needed in downstream genetic analyses. Haploblock borders were defined by recombination locations detected in ancestral generations of cultivars and selections. Another round of inheritance-checking was conducted, for haploblock alleles (i.e., haplotypes). High-quality genotypic data sets were created using this workflow for pedigreed collections representing the U.S. breeding germplasm of apple, peach, and sweet cherry evaluated within the RosBREED project. These data sets contain 3855, 4005, and 1617 SNPs spread over 932, 103, and 196 haploblocks in apple, peach, and sweet cherry, respectively. The highly curated phased SNP and haplotype data sets, as well as the raw iScan data, of germplasm in the apple, peach, and sweet cherry Crop Reference Sets is available through the Genome Database for Rosaceae.

CNR1 and FAAH variation and affective states induced by marijuana smoking

ABSTRACTBackground: Polymorphisms in cannabinoid receptor type 1 (encoded by CNR1) and fatty acid amide hydrolase (encoded by FAAH) have been associated with cannabis dependence, but it remains unknown whether variation within these genes influences cannabis’ acute effects on affect.Objective: Conduct a secondary data analysis study to determine whether previously observed acute effects of tetrahydrocannabinol (THC) on mood was dependent upon variation in CNR1 and FAAH.Methods: A balanced placebo design was used crossing marijuana administration (i.e., 0% THC vs. 2.8% THC) with stimulus expectancy. Participants (N = 118; 64% male) provided DNA and completed the Profile of Mood States questionnaire prior to and after smoking. Haplotypes were constructed from genotyped single nucleotide polymorphisms for CNR1 (rs1049353 and rs806368) and FAAH (rs4141964, rs324420, and rs11576941); rs2023239 (CNR1) and rs6703669 (FAAH) were not part of a phased haplotype block. Analyses tested both main and interaction effects for genotype across CNR1 and FAAH, and drug, and expectancy effects.Results: THC increased levels of POMS Tension-Anxiety and Confusion-Bewilderment over and above the effects of variation in CNR1 and FAAH. Significant drug X genotype/haplotype and expectancy X genotype/haplotype interaction effects were observed for some but not all mood states [e.g., ‘C’ allele carriers of rs2023239 who received THC had higher levels of Anger-Hostility (β= 0.29 (0.12), p= .02) compared to those who received placebo].Conclusion: These preliminary findings suggest individual differences in mood states after using marijuana depend on genetic variation. Such information might be useful in understanding either motivation for use of marijuana and/or risk for associated behaviors.

Fast and Accurate Shared Segment Detection and Relatedness Estimation in Un-phased Genetic Data via TRUFFLE

Relationship estimation and segment detection between individuals is an important aspect of disease gene mapping. Existing methods are either tailored for computational efficiency or require phasing to improve accuracy. We developed TRUFFLE, a method that integrates computational techniques and statistical principles for the identification and visualization of identity-by-descent (IBD) segments using un-phased data. By skipping the haplotype phasing step and, instead, relying on a simpler region-based approach, our method is computationally efficient while maintaining inferential accuracy. In addition, an error model corrects for segment break-ups that occur as a consequence of genotyping errors. TRUFFLE can estimate relatedness for 3.1 million pairs from the 1000 Genomes Project data in a few minutes on a typical laptop computer. Consistent with expectation, we identified only three second cousin or closer pairs across different populations, while commonly used methods identified a large number of such pairs. Similarly, within populations, we identified many fewer related pairs. Compared to methods relying on phased data, TRUFFLE has comparable accuracy but is drastically faster and has fewer broken segments. We also identified specific local genomic regions that are commonly shared within populations, suggesting selection. When applied to pedigree data, we observed 99.6% accuracy in detecting 1st to 5th degree relationships. As genomic datasets become much larger, TRUFFLE can enable disease gene mapping through implicit shared haplotypes by accurate IBD segment detection.

Haploseek: a 24-hour all-in-one method for preimplantation genetic diagnosis (PGD) of monogenic disease and aneuploidy

To develop an economical, user-friendly, and accurate all-in-one next-generation sequencing (NGS)-based workflow for single-cell gene variant detection combined with comprehensive chromosome screening in a 24-hour workflow protocol. We subjected single lymphoblast cells or blastomere/blastocyst biopsies from four different families to low coverage (0.3×-1.4×) genome sequencing. We combined copy-number variant (CNV) detection and whole-genome haplotype phase prediction via Haploseek, a novel, user-friendly analysis pipeline. We validated haplotype predictions for each sample by comparing with clinical preimplantation genetic diagnosis (PGD) case results or by single-nucleotide polymorphism (SNP) microarray analysis of bulk DNA from each respective lymphoblast culture donor. CNV predictions were validated by established commercial kits for single-cell CNV prediction. Haplotype phasing of the single lymphoblast/embryo biopsy sequencing data was highly concordant with relevant ground truth haplotypes in all samples/biopsies from all four families. In addition, whole-genome copy-number assessments were concordant with the results of a commercial kit. Our results demonstrate the establishment of a reliable method for all-in-one molecular and chromosomal diagnosis of single cells. Important features of the Haploseek pipeline include rapid sample processing, rapid sequencing, streamlined analysis, and user-friendly reporting, so as to expedite clinical PGD implementation.

Rare variant phasing using paired tumor:normal sequence data

BackgroundIn standard high throughput sequencing analysis, genetic variants are not assigned to a homologous chromosome of origin. This process, called haplotype phasing, can reveal information important for understanding the relationship between genetic variants and biological phenotypes. For example, in genes that carry multiple heterozygous missense variants, phasing resolves whether one or both gene copies are altered. Here, we present a novel approach to phasing variants that takes advantage of unique properties of paired tumor:normal sequencing data from cancer studies.ResultsVAF phasing uses changes in variant allele frequency (VAF) between tumor and normal samples in regions of somatic chromosomal gain or loss to phase germline variants. We apply VAF phasing to 6180 samples from the Cancer Genome Atlas (TCGA) and demonstrate that our method is highly concordant with other standard phasing methods, and can phase an average of 33% more variants than other read-backed phasing methods. Using variant annotation tools designed to score gene haplotypes, we find a suggestive association between carrying multiple missense variants in a single copy of a cancer predisposition gene and earlier age of cancer diagnosis.ConclusionsVAF phasing exploits unique properties of tumor genomes to increase the number of germline variants that can be phased over standard read-backed methods in paired tumor:normal samples. Our phase-informed association testing results call attention to the need to develop more tools for assessing the joint effect of multiple genetic variants.

The third-generation sequencing combined with targeted capture technology for high-resolution HLA typing and MHC region haplotype identification

The high-resolution and accurate typing of human leukocyte antigen (HLA) is of great significance for the study of tissue matching in organ transplantation and the correlation between HLA and disease. In this study, the peripheral blood of 12 patients with primary hepatocellular carcinoma was used to compare the advantages and disadvantages of the next- and third-generation sequencing technology for high-resolution HLA typing. In addition, probe capture technology was used to capture the MHC region of YH and HeLa standard cell lines, and a primary hepatocellular carcinoma patient. The captured products were sequenced using PacBio platform to assess the potential of ultra-long reads sequencing technology for analysis of the entire MHC region. Our results showed that: (1) the next- and third-generation sequencing technology can both achieve 6-8 digit high resolution in HLA typing. However, the coverage of the third-generation is significantly better than the next-generation sequencing technology. (2) The ultra-long reads of the third generation sequencing can directly span the entire amplicon region, which has obvious advantages for haplotype phasing, with 92.79% of the HLA genes having accurate phasing results, which is much higher than the 75.65% from the next-generation data. (3) The long-reads from the third generating sequencing can not only be used to assemble the MHC region but also the ability to phase the entire MHC region of 3.6 Mb, thereby helping to clarify the localization information of the mutation sites, alleles and non-coding regions on each MHC haplotype, and providing a theoretical basis for the study of immune and other related diseases.

High-resolution HLA phased haplotype frequencies to predict the success of unrelated donor searches and clinical outcome following hematopoietic stem cell transplantation

HLA matching is a critical factor for successful allogeneic hematopoietic stem cell transplantation. For unrelated donor searches, matching is usually based on high-resolution typing at five HLA loci, looking for a 10/10 match. Some studies have proposed that further matching at the haplotype level could be beneficial for clinical outcome. In this study, we determined the phased haplotypes of 291 patients using family members and segregation analysis. The sum of ranks of the haplotypes carried by patients was used as a surrogate predictor of a successful unrelated donor search. The putative impact of haplotypes was then analyzed in a cohort of 211 recipients transplanted with 10/10 matched unrelated donors. A logistic regression analysis showed a highly significant effect of the haplotypes in the outcome of a search, but we did not find any significant effect on overall survival, graft versus host disease or relapse/progression following HSCT. This study provides useful data for the optimization of unrelated bone marrow donor searches, but does not confirm previous reports that matching at the haplotype level has a clinical impact following HSCT. Due to the extreme polymorphism of HLA genes, further studies are warranted to better understand the many factors at play.

Haploseek: A new 24-hour all-in-one method for combined PGT of monogenic diseases (PGT-M) chromosomal rearrangements (PGT-SR) and aneuploidy (PGT-A)

Introduction Recent technological advances have enabled combined gene mutation detection and comprehensive chromosome screening (CCS) from a single embryo biopsy by means of microarray or high coverage next generation sequencing (NGS) technologies. However, these methods are still challenging, very expensive, time consuming, and therefore not widely applied. Therefore, the aim of this study was to develop an economical, user-friendly, all-in-one NGS-based workflow for gene mutation detection combined with CCS screening while maintaining the highest standard for accuracy in a 24 hour workflow protocol. Materials & Methods Three families, of 3-4 children each, were recruited at the Shaare Zedek Medical Center, Jerusalem, Israel. For each family, whole genome haplotype phasing of the parents was accomplished by SNP microarray genotyping analysis of the couple and one child. From the other children in each family, lymphoblast cultures were prepared and single cells were isolated so that each child would be represented by one cell only. Subsequently, each child's single cell isolate was subjected to low genomic coverage (1x) single read (1 × 50) whole genome sequencing on a NextSeq 500 instrument followed by combined chromosome copy number assessment (PGT-A/PGT-SR) and whole genome haplotype phasing for PGT-M. Sequencing reads were aligned to the reference human genome followed by crude variant calling at SNP positions matching those on the trio-phased SNP microarrays. Custom R scripts were devised to integrate trio-phased microarray data and low coverage sequencing data so as to predict paternal and maternal haplotypes throughout the genomes of each sequenced sample. In addition, a custom user-friendly software, termed, ‘haploseek’, was created to visualize data output on a web browser in two plots depicting haplotype prediction and marginal haplotype probability, respectively. For copy number variant (CNV) prediction, a custom CNV detection R script was devised and plotted by web browser. All single cell haplotype and CNV predictions were validated by SNP microarray analysis of bulk genomic peripheral blood DNA from the corresponding tissue culture donor. The entire single cell sequencing assay was completed within 24 hours, beginning from single cell isolation until test result conclusion. Results Whole genome copy number assessment and haplotype phasing of the single cell sequencing data was completely (100%) concordant with known, microarray determined, whole genome chromosome copy numbers and haplotypes in each child from all 3 families. Interestingly, a relatively long segmental deletion of chr21q was identified in the single cell sequencing data of one of the children together with haplotype information suggesting that the deleted allele was paternal in origin. This result and all other copy number assessments in the low coverage data were confirmed by array validation as well. Conclusions Our results demonstrate the establishment of a reliable method for all-in-one molecular and chromosomal diagnosis of single cells. Importantly, the whole ‘Haploseek’ process was predicated upon rapid sample processing, rapid sequencing, stream-lined analysis, and user-friendly result reporting so as to expedite clinical implementation. Thus, our novel NGS –based method may become the ultimate, economically feasible, 24 hour end-to-end solution for combined PGT-M, PGT-SR and PGT-A application.

GenHap: a novel computational method based on genetic algorithms for haplotype assembly

BackgroundIn order to fully characterize the genome of an individual, the reconstruction of the two distinct copies of each chromosome, called haplotypes, is essential. The computational problem of inferring the full haplotype of a cell starting from read sequencing data is known as haplotype assembly, and consists in assigning all heterozygous Single Nucleotide Polymorphisms (SNPs) to exactly one of the two chromosomes. Indeed, the knowledge of complete haplotypes is generally more informative than analyzing single SNPs and plays a fundamental role in many medical applications.ResultsTo reconstruct the two haplotypes, we addressed the weighted Minimum Error Correction (wMEC) problem, which is a successful approach for haplotype assembly. This NP-hard problem consists in computing the two haplotypes that partition the sequencing reads into two disjoint sub-sets, with the least number of corrections to the SNP values. To this aim, we propose here GenHap, a novel computational method for haplotype assembly based on Genetic Algorithms, yielding optimal solutions by means of a global search process. In order to evaluate the effectiveness of our approach, we run GenHap on two synthetic (yet realistic) datasets, based on the Roche/454 and PacBio RS II sequencing technologies. We compared the performance of GenHap against HapCol, an efficient state-of-the-art algorithm for haplotype phasing. Our results show that GenHap always obtains high accuracy solutions (in terms of haplotype error rate), and is up to 4× faster than HapCol in the case of Roche/454 instances and up to 20× faster when compared on the PacBio RS II dataset. Finally, we assessed the performance of GenHap on two different real datasets.ConclusionsFuture-generation sequencing technologies, producing longer reads with higher coverage, can highly benefit from GenHap, thanks to its capability of efficiently solving large instances of the haplotype assembly problem. Moreover, the optimization approach proposed in GenHap can be extended to the study of allele-specific genomic features, such as expression, methylation and chromatin conformation, by exploiting multi-objective optimization techniques. The source code and the full documentation are available at the following GitHub repository: https://github.com/andrea-tango/GenHap.

Generating a Three-Dimensional Genome from Xenopus with Hi-C.

Hi-C is a sequencing-based method that captures three-dimensional (3-D) genome interactions by counting the interaction frequencies of pairs of genomic loci. This protocol describes the application of in situ Hi-C to the Xenopus embryo. Briefly, after fixing embryos with formaldehyde, nuclei are isolated and chromatin is digested with a restriction enzyme. Restriction sites are filled in with a biotinylated nucleotide and the blunted ends are re-ligated in place, all while still contained in the nuclei (i.e., in situ). Subsequently, the re-ligated genomic DNA is isolated and fragmented by sonication. Biotinylated ligation junctions are captured with streptavidin-coated beads, and DNA fragments are amplified by ligation-mediated polymerase chain reaction (LM-PCR). The PCR product is isolated and sequenced from both ends (paired-end), and informatics methods are then applied to align the two sides of the ligation junctions to the reference genome. Because ligation occurs much more frequently intra- than interchromosomally, and with generally decreasing frequency the further away DNA loci are from each other on the linear chromosome, interaction frequency information can be used to assist in assembling genomes and to phase haplotypes, which is especially useful in the case of a tetraploid organism such as X. laevis Our streamlined version of in situ Hi-C was optimized for high throughput and low cost, and enables generation of high-quality Hi-C libraries from small cell numbers (down to ∼10,000 cells) in 2 d.

Haplotype-resolved and integrated genome analysis of the cancer cell line HepG2.

HepG2 is one of the most widely used human cancer cell lines in biomedical research and one of the main cell lines of ENCODE. Although the functional genomic and epigenomic characteristics of HepG2 are extensively studied, its genome sequence has never been comprehensively analyzed and higher order genomic structural features are largely unknown. The high degree of aneuploidy in HepG2 renders traditional genome variant analysis methods challenging and partially ineffective. Correct and complete interpretation of the extensive functional genomics data from HepG2 requires an understanding of the cell line’s genome sequence and genome structure. Using a variety of sequencing and analysis methods, we identified a wide spectrum of genome characteristics in HepG2: copy numbers of chromosomal segments at high resolution, SNVs and Indels (corrected for aneuploidy), regions with loss of heterozygosity, phased haplotypes extending to entire chromosome arms, retrotransposon insertions and structural variants (SVs) including complex and somatic genomic rearrangements. A large number of SVs were phased, sequence assembled and experimentally validated. We re-analyzed published HepG2 datasets for allele-specific expression and DNA methylation and assembled an allele-specific CRISPR/Cas9 targeting map. We demonstrate how deeper insights into genomic regulatory complexity are gained by adopting a genome-integrated framework.

Comprehensive, integrated, and phased whole-genome analysis of the primary ENCODE cell line K562.

K562 is widely used in biomedical research. It is one of three tier-one cell lines of ENCODE and also most commonly used for large-scale CRISPR/Cas9 screens. Although its functional genomic and epigenomic characteristics have been extensively studied, its genome sequence and genomic structural features have never been comprehensively analyzed. Such information is essential for the correct interpretation and understanding of the vast troves of existing functional genomics and epigenomics data for K562. We performed and integrated deep-coverage whole-genome (short-insert), mate-pair, and linked-read sequencing as well as karyotyping and array CGH analysis to identify a wide spectrum of genome characteristics in K562: copy numbers (CN) of aneuploid chromosome segments at high-resolution, SNVs and indels (both corrected for CN in aneuploid regions), loss of heterozygosity, megabase-scale phased haplotypes often spanning entire chromosome arms, structural variants (SVs), including small and large-scale complex SVs and nonreference retrotransposon insertions. Many SVs were phased, assembled, and experimentally validated. We identified multiple allele-specific deletions and duplications within the tumor suppressor gene FHIT. Taking aneuploidy into account, we reanalyzed K562 RNA-seq and whole-genome bisulfite sequencing data for allele-specific expression and allele-specific DNA methylation. We also show examples of how deeper insights into regulatory complexity are gained by integrating genomic variant information and structural context with functional genomics and epigenomics data. Furthermore, using K562 haplotype information, we produced an allele-specific CRISPR targeting map. This comprehensive whole-genome analysis serves as a resource for future studies that utilize K562 as well as a framework for the analysis of other cancer genomes.

Extending phylogeography to account for lineage fusion

AbstractSecondary contact between long isolated populations has several possible outcomes. These include the strengthening of preexisting reproductive isolating mechanisms via reinforcement, the emergence of a hybrid lineage that is distinct from its extant parental lineages and which occupies a spatially restricted zone between them, or complete merging of two populations such that parental lineages are no longer extant (“lineage fusion” herein). The latter scenario has rarely been explicitly considered in single‐species and comparative phylogeographic studies, yet it has the potential to impact inferences about population history and levels of congruence. In this paper, we explore the idea that insights into past lineage fusion may now be possible, owing to the advent of next‐generation sequencing. Using simulated DNA sequence haplotype datasets (i.e., loci with alleles comprised of a set of linked nucleotide polymorphisms), we examined basic requirements (number of loci and individuals sampled) for identifying cases when a present‐day panmictic population is the product of lineage fusion, using an exemplar statistical framework—approximate Bayesian computation. We found that with approximately 100 phased haplotype loci (each 400 bp long) and modest sample sizes of individuals (10 per population), lineage fusion can be detected under rather challenging scenarios. This included some scenarios where reticulation was fully contained within a Last Glacial Maximum timeframe, provided that mixing was symmetrical, ancestral gene pools were moderately to deeply diverged, and the lag time between the fusion event and gene pool sampling was relatively short. However, the more realistic case of asymmetrical mixing is not prohibitive if additional genetic data (e.g., 400 loci) are available. Notwithstanding some simplifying assumptions of our simulations and the knowledge gaps that remain about the circumstances under which lineage fusion is potentially detectable, we suggest that the recent release from data limitation allows phylogeographers to expand the scope of inferences about long‐term population history.

Inverse PCR to perform long-distance haplotyping: main applications to improve preimplantation genetic diagnosis in hemophilia.

Among other applications of long-distance haplotype phasing in clinical genetics, determination of linked DNA markers as surrogate for problematic structural variants (e.g., repeat-mediated rearrangements) is essential to perform diagnosis from low-quality DNA samples. We describe a next-of-kin-independent (physical) phasing approach based on inverse-PCR (iPCR) paired-end amplification (PI). This method enables typing the multialleles of the short tandem repeat (STR) F8Int21[CA]n at the F8-intron 21, as a surrogate DNA marker for the F8-intron 22 inversion (Inv22), the hemophilia A-causative hotspot, within the transmitted haplotype in informative carriers. We provide proof-of-concept by blindly validating the PI approach in 15 carrier mother/affected-son duos. Every F8Int21[CA]n STR allele determined in phase with the Inv22 allele in the female carriers from the informative duos was confirmed in the hemizygous proband (P = 0.00003). A second surrogate STR locus at the F8-IVS22 was obtained by the PI approach improving severe-HA preimplantation genetic diagnosis by augmenting heterozygosity in Inv22 carriers bypassing the requirement for family linkage analysis. The ability of the PI-assay to combine other marker pairs was demonstrated by haplotyping a SNV (F8:c.6118T > C) with a >28kb-distant F8-IVS22 STR. The PI approach has proven flexibility to target different marker pairs and has potential for multiplex characterization of iPCR products by massively parallel sequencing.

Off the street phasing (OTSP): no hassle haplotype phasing for molecular PGD applications.

Pre-implantation genetic diagnosis (PGD) for molecular disorders requires the construction of parental haplotypes. Classically, haplotype resolution ("phasing") is obtained by genotyping multiple polymorphic markers in both parents and at least one additional relative. However, this process is time-consuming, and immediate family members are not always available. The recent availability of massive genomic data for many populations promises to eliminate the needs for developing family-specific assays and for recruiting additional family members. In this study, we aimed to validate population-assisted haplotype phasing for PGD. Targeted sequencing of CFTR gene variants and ~ 1700 flanking polymorphic SNPs (± 2Mb) was performed on 54 individuals from 12 PGD families of (a) Full Ashkenazi (FA; n = 16), (b) mixed Ashkenazi (MA; n = 23 individuals with at least one Ashkenazi and one non-Ashkenazi grandparents), or (c) non-Ashkenazi (NA; n = 15) descent. Heterozygous genotype calls in each individual were phased using various whole genome reference panels and appropriate computational models. All computationally derived haplotype predictions were benchmarked against trio-based phasing. Using the Ashkenazi reference panel, phasing of FA was highly accurate (99.4% ± 0.2% accuracy); phasing of MA was less accurate (95.4% ± 4.5% accuracy); and phasing of NA was predictably low (83.4% ± 6.6% accuracy). Strikingly, for founder mutation carriers, our haplotyping approach facilitated near perfect phasing accuracy (99.9% ± 0.1% and 98.2% ± 2.8% accuracy for W1282X and delF508 carriers, respectively). Our results demonstrate the feasibility of replacing classical haplotype phasing with population-based phasing with uncompromised accuracy.

A Comparative Analysis of the Illumina Truseq Synthetic Long-read Haplotyping Sequencing Platform versus the 10X Genomics Chromium Genome Sequencing Platform for Haplotype Phasing and the Identification of Single-nucleotide variants (SNVs) in Hanwoo (Korean Native Cattle)

A Comparative Analysis of the Illumina Truseq Synthetic Long-read Haplotyping Sequencing Platform versus the 10X Genomics Chromium Genome Sequencing Platform for Haplotype Phasing and the Identification of Single-nucleotide variants (SNVs) in Hanwoo (Korean Native Cattle)

A statistical framework for detecting mislabeled and contaminated samples using shallow-depth sequence data

BackgroundResearchers typically sequence a given individual multiple times, either re-sequencing the same DNA sample (technical replication) or sequencing different DNA samples collected on the same individual (biological replication) or both. Before merging the data from these replicate sequence runs, it is important to verify that no errors, such as DNA contamination or mix-ups, occurred during the data collection pipeline. Methods to detect such errors exist but are often ad hoc, cannot handle missing data and several require phased data. Because they require some combination of genotype calling, imputation, and haplotype phasing, these methods are unsuitable for error detection in low- to moderate-depth sequence data where such tasks are difficult to perform accurately. Additionally, because most existing methods employ a pairwise-comparison approach for error detection rather than joint analysis of the putative replicates, results may be difficult to interpret.ResultsWe introduce a new method for error detection suitable for shallow-, moderate-, and high-depth sequence data. Using Bayes Theorem, we calculate the posterior probability distribution over the set of relations describing the putative replicates and infer which of the samples originated from an identical genotypic source.ConclusionsOur method addresses key limitations of existing approaches and produced highly accurate results in simulation experiments. Our method is implemented as an R package called BIGRED (Bayes Inferred Genotype Replicate Error Detector), which is freely available for download: https://github.com/ac2278/BIGRED.