Accurate rare variant phasing of whole-genome and whole-exome sequencing data in the UK Biobank

Hofmeister, Robin J.; Ribeiro, Diogo M; Rubinacci, Simone; Delaneau, Olivier

doi:10.1101/2022.10.19.512867

Cited by 35 publications

(64 citation statements)

References 60 publications

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“…For the SNP array data, we applied the standard QC recommended by the original authors [24], and phased the data using SHAPEIT4 [25]resulting in 976,754 haplotypes and a total of 670,741 SNPs. For the whole genome sequencing data available on the UK Biobank research analysis platform [1], we used data recently processed and phased by the SHAPEIT5 authors [26], for a total of 300,238 haplotypes and 13,780,193 bi-allelic SNPs and indels on chromosome 20. For the UK Biobank WGS dataset, we applied our method independently to 13 regions of at least 4 megabases and 4 centimorgans on chromosome 20.…”

Section: Resultsmentioning

confidence: 99%

μ-PBWT: Enabling the Storage and Use of UK Biobank Data on a Commodity Laptop

Cozzi

Rossi

Rubinacci

et al. 2023

Preprint

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Motivation:The positional Burrows-Wheeler Transform (PBWT) has been introduced as a key data structure for indexing haplotype sequences with the main purpose of finding maximal haplotype matches inhsequences containingwvariation sites inO(hw)-time with a significant improvement over classical quadratic time approaches. However the original PBWT data structure does not allow queries over the modern biobank panels of haplotypes consisting of several millions of haplotypes, as they must be kept entirely in memory.Results:In this paper, we present a method for constructing the run-length encoded PBWT for memory efficient haplotype matching. We implement our method, which we refer to as μ-PBWT, and evaluate it on datasets of 1000 Genome Project and UK Biobank data. Our experiments demonstrate that the μ-PBWT reduces the memory usage up to a factor of 25 compared to the best current PBWT-based indexing. In particular, μ-PBWT produces an index that stores high-coverage whole genome sequencing data of chromosome 20 in half the space of its BCF file. In addition, μ-PBWT is able to index a dataset with 2 million haplotypes and 2.3 million sites in 4 GB of space, which can be uploaded in 20 seconds on a commodity laptop. μ-PBWT is an adaptation of techniques for the run-length compressed BWT for the PBWT (RLPBWT) and it is based on keeping in memory only a small representation of the RLPBWT that still allows the efficient computation of set maximal matches (SMEMs) over the original panel.Availability:Our implementation is open source and available at https://github.com/dlcgold/muPBWT. The binary is available at https://bioconda.github.io/recipes/mupbwt/README.html

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Section: Resultsmentioning

confidence: 99%

μ-PBWT: Enabling the Storage and Use of UK Biobank Data on a Commodity Laptop

Cozzi

Rossi

Rubinacci

et al. 2023

Preprint

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“…Rare variants, which are of the greatest interest in Mendelian diseases, are also challenging to phase using population-based approaches given the small numbers of shared haplotypes from which to make phasing estimates in the population. Recent methods have shown accurate phasing of rare variants using genome sequencing data [25][26][27] , but relies on a large genome reference panel. In our work, there were limited numbers of genome sequences available for use in a population-based phasing approach.…”

Section: Discussionmentioning

confidence: 99%

Inferring compound heterozygosity from large-scale exome sequencing data

Guo

Francioli

Stenton

et al. 2023

Preprint

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Severe recessive diseases arise when both the maternal and the paternal copies of a gene carry, or are impacted by, a damaging genetic variant in the affected individual. When a patient carries two different potentially causal variants, accurate diagnosis requires determining that these two variants occur on different copies of the chromosome (i.e., are intrans) rather than on the same copy (i.e., incis). However, current approaches for determining phase, beyond parental testing, are limited in clinical settings. We developed a strategy for inferring phase for rare variant pairs within genes, leveraging haplotype patterns observed in exome sequencing data from the Genome Aggregation Database (gnomAD v2, n=125,748). When applied to trio data where phase is known, our approach estimates phase with high accuracy, even for very rare variants (frequency <1x10-4), and also correctly phases 95.2% of variant pairs in a set of 293 patients carrying presumed causal compound heterozygous variants. We provide a public resource of phasing estimates from gnomAD, including phasing estimates for coding variants across the genome and counts per gene of rare variants intrans, that can aid interpretation of rare co-occurring variants in the context of recessive disease.

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“…To demonstrate the benefits of using sequenced biobanks for lcWGS imputation, we phased the recent release of the UK Biobank (UKB) WGS data 3,4 using SHAPEIT5 5 and created a UKB reference panel of 280,238 haplotypes and 582,534,516 markers ( Supplementary Note S1 ). We used the UKB panel to impute lcWGS samples with GLIMPSE2 and other recently released imputation methods: GLIMPSE1 1 and QUILT v1.0.4 2 .…”

Section: Mainmentioning

confidence: 99%

Imputation of low-coverage sequencing data from 150,119 UK Biobank genomes

Rubinacci

Hofmeister

Mota

et al. 2022

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Recent work highlights the advantages of low-coverage whole genome sequencing (lcWGS), followed by genotype imputation, as a cost-effective genotyping technology for statistical and population genetics. The release of whole genome sequencing data for 150,119 UK Biobank (UKB) samples represents an unprecedented opportunity to impute lcWGS with high accuracy. However, despite recent progress, current methods struggle to cope with the growing numbers of samples and markers in modern reference panels, resulting in unsustainable computational costs. For instance, the imputation cost for a single genome is 1.11 British pounds using GLIMPSE v1.1.1 (GLIMPSE1) on the UKB research analysis platform (RAP) and rises to 242.8 British pounds using QUILT v1.0.4. To overcome this computational burden, we introduce GLIMPSE v2.0.0 (GLIMPSE2), a major improvement of GLIMPSE, that scales sublinearly in both the number of samples and markers. GLIMPSE2 imputes a low-coverage genome from the UKB reference panel for only 0.08 British pounds in compute cost while retaining high accuracy for both ancient and modern genomes, particularly at rare variants (MAF < 0.1%) and for very low-coverage samples (0.1x-0.5x).

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Accurate rare variant phasing of whole-genome and whole-exome sequencing data in the UK Biobank

Cited by 35 publications

References 60 publications

μ-PBWT: Enabling the Storage and Use of UK Biobank Data on a Commodity Laptop

μ-PBWT: Enabling the Storage and Use of UK Biobank Data on a Commodity Laptop

Inferring compound heterozygosity from large-scale exome sequencing data

Imputation of low-coverage sequencing data from 150,119 UK Biobank genomes

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