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Phasing for medical sequencing using rare variants and large haplotype reference panels.

ABSTRACT: There is growing recognition that estimating haplotypes from high coverage sequencing of single samples in clinical settings is an important problem. At the same time very large datasets consisting of tens and hundreds of thousands of high-coverage sequenced samples will soon be available. We describe a method that takes advantage of these huge human genetic variation resources and rare variant sharing patterns to estimate haplotypes on single sequenced samples. Sharing rare variants between two individuals is more likely to arise from a recent common ancestor and, hence, also more likely to indicate similar shared haplotypes over a substantial flanking region of sequence.Our method exploits this idea to select a small set of highly informative copying states within a Hidden Markov Model (HMM) phasing algorithm. Using rare variants in this way allows us to avoid iterative MCMC methods to infer haplotypes. Compared to other approaches that do not explicitly use rare variants we obtain significant gains in phasing accuracy, less variation over phasing runs and improvements in speed. For example, using a reference panel of 7420 haplotypes from the UK10K project, we are able to reduce switch error rates by up to 50% when phasing samples sequenced at high-coverage. In addition, a single step rephasing of the UK10K panel, using rare variant information, has a downstream impact on phasing performance. These results represent a proof of concept that rare variant sharing patterns can be utilized to phase large high-coverage sequencing studies such as the 100 000 Genomes Project dataset.A webserver that includes an implementation of this new method and allows phasing of high-coverage clinical samples is available at https://phasingserver.stats.ox.ac.uk/marchini@stats.ox.ac.ukSupplementary data are available at Bioinformatics online.

SUBMITTER: Sharp K

PROVIDER: S-EPMC4920110 | biostudies-literature | 2016 Jul

REPOSITORIES: biostudies-literature

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Phasing for medical sequencing using rare variants and large haplotype reference panels.

Sharp Kevin K Kretzschmar Warren W Delaneau Olivier O Marchini Jonathan J

Bioinformatics (Oxford, England) 20160227 13

<h4>Motivation</h4>There is growing recognition that estimating haplotypes from high coverage sequencing of single samples in clinical settings is an important problem. At the same time very large datasets consisting of tens and hundreds of thousands of high-coverage sequenced samples will soon be available. We describe a method that takes advantage of these huge human genetic variation resources and rare variant sharing patterns to estimate haplotypes on single sequenced samples. Sharing rare v ...[more]

PMID: 27153703

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Project description:BackgroundImputation has become a standard approach in genome-wide association studies (GWAS) to infer in silico untyped markers. Although feasibility for common variants imputation is well established, we aimed to assess rare and ultra-rare variants' imputation in an admixed Caribbean Hispanic population (CH).MethodsWe evaluated imputation accuracy in CH (N = 1,000), focusing on rare (0.1% ≤ minor allele frequency (MAF) ≤ 1%) and ultra-rare (MAF < 0.1%) variants. We used two reference panels, the Haplotype Reference Consortium (HRC; N = 27,165) and 1000 Genome Project (1000G phase 3; N = 2,504) and multiple phasing (SHAPEIT, Eagle2) and imputation algorithms (IMPUTE2, MACH-Admix). To assess imputation quality, we reported: (a) high-quality variant counts according to imputation tools' internal indexes (e.g., IMPUTE2 "Info" ≥ 80%). (b) Wilcoxon Signed-Rank Test comparing imputation quality for genotyped variants that were masked and imputed; (c) Cohen's kappa coefficient to test agreement between imputed and whole-exome sequencing (WES) variants; (d) imputation of G206A mutation in the PSEN1 (ultra-rare in the general population an more frequent in CH) followed by confirmation genotyping. We also tested ancestry proportion (European, African and Native American) against WES-imputation mismatches in a Poisson regression fashion.ResultsSHAPEIT2 retrieved higher percentage of imputed high-quality variants than Eagle2 (rare: 51.02% vs. 48.60%; ultra-rare 0.66% vs. 0.65%, Wilcoxon p-value < 0.001). SHAPEIT-IMPUTE2 employing HRC outperformed 1000G (64.50% vs. 59.17%; 1.69% vs. 0.75% for high-quality rare and ultra-rare variants, respectively, Wilcoxon p-value < 0.001). SHAPEIT-IMPUTE2 outperformed MaCH-Admix. Compared to 1000G, HRC-imputation retrieved a higher number of high-quality rare and ultra-rare variants, despite showing lower agreement between imputed and WES variants (e.g., rare: 98.86% for HRC vs. 99.02% for 1000G). High Kappa (K = 0.99) was observed for both reference panels. Twelve G206A mutation carriers were imputed and all validated by confirmation genotyping. African ancestry was associated with higher imputation errors for uncommon and rare variants (p-value < 1e-05).ConclusionReference panels with larger numbers of haplotypes can improve imputation quality for rare and ultra-rare variants in admixed populations such as CH. Ethnic composition is an important predictor of imputation accuracy, with higher African ancestry associated with poorer imputation accuracy.

Project description:BackgroundLow-pass sequencing followed by sequence variant genotype imputation is an alternative to the routine microarray-based genotyping in cattle. However, the impact of haplotype reference panels and their interplay with the coverage of low-pass whole-genome sequencing data have not been sufficiently explored in typical livestock settings where only a small number of reference samples is available.MethodsSequence variant genotyping accuracy was compared between two variant callers, GATK and DeepVariant, in 50 Brown Swiss cattle with sequencing coverages ranging from 4- to 63-fold. Haplotype reference panels of varying sizes and composition were built with DeepVariant based on 501 individuals from nine breeds. High-coverage sequence data for 24 Brown Swiss cattle were downsampled to between 0.01- and 4-fold to mimic low-pass sequencing. GLIMPSE was used to infer sequence variant genotypes from the low-pass sequencing data using different haplotype reference panels. The accuracy of the sequence variant genotypes that were inferred from low-pass sequencing data was compared with sequence variant genotypes called from high-coverage data.ResultsDeepVariant was used to establish bovine haplotype reference panels because it outperformed GATK in all evaluations. Within-breed haplotype reference panels were more accurate and efficient to impute sequence variant genotypes from low-pass sequencing than equally-sized multibreed haplotype reference panels for all target sample coverages and allele frequencies. F1 scores greater than 0.9, which indicate high harmonic means of recall and precision of called genotypes, were achieved with 0.25-fold sequencing coverage when large breed-specific haplotype reference panels (n = 150) were used. In absence of such large within-breed haplotype panels, variant genotyping accuracy from low-pass sequencing could be increased either by adding non-related samples to the haplotype reference panel or by increasing the coverage of the low-pass sequencing data. Sequence variant genotyping from low-pass sequencing was substantially less accurate when the reference panel lacked individuals from the target breed.ConclusionsVariant genotyping is more accurate with DeepVariant than GATK. DeepVariant is therefore suitable to establish bovine haplotype reference panels. Medium-sized breed-specific haplotype reference panels and large multibreed haplotype reference panels enable accurate imputation of low-pass sequencing data in a typical cattle breed.

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Phasing for medical sequencing using rare variants and large haplotype reference panels.

Publications

Phasing for medical sequencing using rare variants and large haplotype reference panels.

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