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Genotype phasing in pedigrees using whole-genome sequence data.

ABSTRACT: Phasing is the process of inferring haplotypes from genotype data. Efficient algorithms and associated software for accurate phasing in pedigrees are needed, especially for populations lacking reference panels of sequenced individuals. We present a novel method for phasing genotypes from whole-genome sequence data in pedigrees, called PULSAR (Phasing Using Lineage Specific Alleles/Rare variants). The method is based on the property that alleles specific to a single founding chromosome within a pedigree are highly informative for identifying haplotypes that are shared identical by descent. Simulation studies are used to assess the performance of PULSAR with various pedigree sizes and structures, and the effect of genotyping errors and the presence of nonsequenced individuals is investigated. In pedigrees with complete sequencing and realistic genotyping error rates, PULSAR correctly phases >99.9% of heterozygous genotypes, excluding sites at which all individuals are heterozygous, and does so with a switch error rate frequently below 10^-4. PULSAR is highly accurate, capable of genotype error correction and imputation, and computationally competitive with alternative phasing software applicable to pedigrees. Our method has the significant advantage of not requiring reference panels that are essential for other population-based phasing algorithms. A software implementation of PULSAR is freely available.

SUBMITTER: Blackburn AN

PROVIDER: S-EPMC7253450 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:The determination of the relationship between a pair of individuals is a fundamental application of genetics. Previously, we and others have demonstrated that identity-by-descent (IBD) information generated from high-density single-nucleotide polymorphism (SNP) data can greatly improve the power and accuracy of genetic relationship detection. Whole-genome sequencing (WGS) marks the final step in increasing genetic marker density by assaying all single-nucleotide variants (SNVs), and thus has the potential to further improve relationship detection by enabling more accurate detection of IBD segments and more precise resolution of IBD segment boundaries. However, WGS introduces new complexities that must be addressed in order to achieve these improvements in relationship detection. To evaluate these complexities, we estimated genetic relationships from WGS data for 1490 known pairwise relationships among 258 individuals in 30 families along with 46 population samples as controls. We identified several genomic regions with excess pairwise IBD in both the pedigree and control datasets using three established IBD methods: GERMLINE, fastIBD, and ISCA. These spurious IBD segments produced a 10-fold increase in the rate of detected false-positive relationships among controls compared to high-density microarray datasets. To address this issue, we developed a new method to identify and mask genomic regions with excess IBD. This method, implemented in ERSA 2.0, fully resolved the inflated cryptic relationship detection rates while improving relationship estimation accuracy. ERSA 2.0 detected all 1(st) through 6(th) degree relationships, and 55% of 9(th) through 11(th) degree relationships in the 30 families. We estimate that WGS data provides a 5% to 15% increase in relationship detection power relative to high-density microarray data for distant relationships. Our results identify regions of the genome that are highly problematic for IBD mapping and introduce new software to accurately detect 1(st) through 9(th) degree relationships from whole-genome sequence data.

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Genotype phasing in pedigrees using whole-genome sequence data.

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