Haplotype estimation for biobank-scale data sets

O’Connell, Jared; Sharp, Kevin; Shrine, Nick; Wain, Louise V.; Hall, Ian P.; Tobin, Martin D.; Zagury, Jean‐François; Delaneau, Olivier; Marchini, Jonathan

doi:10.1038/ng.3583

Cited by 210 publications

(206 citation statements)

References 24 publications

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“…The first steps of the quality control have been previously described (SI Materials and Methods, URLs). Phasing and imputation were performed using SHAPEIT and IMPUTE2 (SI Materials and Methods, URLs), respectively, as previously described (35). After imputation, we selected 9,493,148 autosomal SNPs with imputation quality r 2 > 0.3, MAF > 1%, and Hardy-Weinberg equilibrium test P value >10 −6 .…”

Section: Methodsmentioning

confidence: 99%

Detection and quantification of inbreeding depression for complex traits from SNP data

Yengo

Zhu

Wray

et al. 2017

Proc. Natl. Acad. Sci. U.S.A.

106

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Quantifying the effects of inbreeding is critical to characterizing the genetic architecture of complex traits. This study highlights through theory and simulations the strengths and shortcomings of three SNP-based inbreeding measures commonly used to estimate inbreeding depression (ID). We demonstrate that heterogeneity in linkage disequilibrium (LD) between causal variants and SNPs biases ID estimates, and we develop an approach to correct this bias using LD and minor allele frequency stratified inference (LDMS). We quantified ID in 25 traits measured in ∼140,000 participants of the UK Biobank, using LDMS, and confirmed previously published ID for 4 traits. We find unique evidence of ID for handgrip strength, waist/hip ratio, and visual and auditory acuity (ID between −2.3 and −5.2 phenotypic SDs for complete inbreeding; P < 0.001). Our results illustrate that a careful choice of the measure of inbreeding combined with LDMS stratification improves both detection and quantification of ID using SNP data.M ating between close relatives has detrimental consequences on the survival and fertility of resulting offspring (1). This overall reduction of fitness, referred to as inbreeding depression (ID), is observable in a wide range of organisms, including plants (2), animals (3, 4), and humans (5). In humans, major abnormalities are more frequent in children from consanguineous marriages (6) and genes causing rare diseases can be mapped by ascertaining children from such matings (7). To date, although the genetic basis of ID is not completely elucidated, two main hypotheses are proposed to explain this phenomenon: homozygosity for partially recessive deleterious mutations and heterozygous advantage (overdominance) (1, 8). More generally, ID can be estimated for any complex trait, even if the trait is not an obvious component of fitness. For polygenic traits, ID can be detected if there is directional dominance (DD) across loci, which means that the phenotype of individuals who are heterozygous deviates from the average phenotypes of homozygous individuals in a consistent direction. For fitness components, DD is negative; i.e., on average homozygosity reduces fitness.In practice, ID can be estimated from pedigree studies when the relationships between parents are known (6, 9). However, given the limited number and the small sizes of such studies in humans, contemporary efforts (5, 10) to quantify ID have instead used SNP genotyping platforms to directly estimate inbreeding coefficients (F). SNP data may allow a more accurate evaluation of inbreeding (11), in particular for distant and cryptic inbreeding, and allow inference to be drawn from large population data (10). Conceptually, once a measure of F is derived from SNP data, ID can subsequently be estimated by correlating phenotype with the estimated F.Genome-wide estimators of F fall in two categories: average homozygosity measures across loci (irrespective of position) and measures of continuous runs of homozygosity (ROH). Using ROH, ID has been reported for diseas...

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Section: Methodsmentioning

confidence: 99%

Detection and quantification of inbreeding depression for complex traits from SNP data

Yengo

Zhu

Wray

et al. 2017

Proc. Natl. Acad. Sci. U.S.A.

106

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“…Over the past decade, phasing has most commonly been performed via statistical methods applied within a genotyped cohort 2-14 . Wet-lab technologies for direct phasing have also generated considerable recent interest, but these methods are currently much less scalable 15 .…”

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confidence: 99%

“…In general, the accuracy of statistical phasing methods increases steadily with sample size due to improved modeling of linkage disequilbrium and increasing prevalence of identity-by-descent. We and others have recently developed methods that achieve very high statistical phasing accuracy in cohorts comprising a large fraction of a population 8 or containing >100,000 samples 13,14 . However, for smaller cohorts, accuracy of cohort-based statistical phasing is fundamentally limited by the quantity of data available.…”

mentioning

confidence: 99%

“…This paradigm targets a user group complementary to recent methods for phasing very large cohorts 13,14 . In particular, methods for mapping molecular QTLs using allele-specific reads require accurate phasing information, but recent papers introducing these methods have reported that inaccurate phasing currently limits their potential 16,17 .…”

mentioning

confidence: 99%

“…The Eagle2 algorithm represents a substantial computational advance over existing reference-based phasing algorithms: Eagle2 achieves a 20x speedup over SHAPEIT2 12 —i.e., genome-wide phasing in 1.5 minutes per sample—with a 10% improvement in accuracy across a range of ancestries. Eagle2 achieves this performance via two key ideas that distinguish it from previous phasing algorithms 3-14 : a new data structure based on the positional Burrows-Wheeler transform 20 and a rapid search algorithm that explores only the most relevant phase paths through a hidden Markov model (HMM). We have released Eagle2 as open-source software (see URLs).…”

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confidence: 99%

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Reference-based phasing using the Haplotype Reference Consortium panel

et al. 2016

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Haplotype phasing is a fundamental problem in medical and population genetics. Phasing is generally performed via statistical phasing within a genotyped cohort, an approach that can attain high accuracy in very large cohorts but attains lower accuracy in smaller cohorts. Here, we instead explore the paradigm of reference-based phasing. We introduce a new phasing algorithm, Eagle2, that attains high accuracy across a broad range of cohort sizes by efficiently leveraging information from large external reference panels (such as the Haplotype Reference Consortium, HRC) using a new data structure based on the positional Burrows-Wheeler transform. We demonstrate that Eagle2 attains a ≈20x speedup and ≈10% increase in accuracy compared to reference-based phasing using SHAPEIT2. On European-ancestry samples, Eagle2 with the HRC panel achieves >2x the accuracy of 1000 Genomes-based phasing. Eagle2 is open source and freely available for HRC-based phasing via the Sanger Imputation Service and the Michigan Imputation Server.

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Linkage Disequilibrium, Recombination and Haplotype Structure

McVean

Kelleher

2019

Handbook of Statistical Genomics

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Haplotype estimation for biobank-scale data sets

Cited by 210 publications

References 24 publications

Detection and quantification of inbreeding depression for complex traits from SNP data

Detection and quantification of inbreeding depression for complex traits from SNP data

Reference-based phasing using the Haplotype Reference Consortium panel

Linkage Disequilibrium, Recombination and Haplotype Structure

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