Imputation of low-coverage sequencing data from 150,119 UK Biobank genomes
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Open Access
- 29 June 2023
- journal article
- research article
- Published by Springer Nature in Nature Genetics
- Vol. 55 (7) , 1088-1090
Abstract
The release of 150,119 UK Biobank sequences represents an unprecedented opportunity as a reference panel to impute low-coverage whole-genome sequencing data with high accuracy but current methods cannot cope with the size of the data. Here we introduce GLIMPSE2, a low-coverage whole-genome sequencing imputation method that scales sublinearly in both the number of samples and markers, achieving efficient whole-genome imputation from the UK Biobank reference panel while retaining high accuracy for ancient and modern genomes, particularly at rare variants and for very low-coverage samples.Keywords
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Funding Information
- Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung (PP00P3_176977)
This publication has 18 references indexed in Scilit:
- Low-pass sequencing increases the power of GWAS and decreases measurement error of polygenic risk scores compared to genotyping arraysGenome Research, 2021
- Efficient phasing and imputation of low-coverage sequencing data using large reference panelsNature Genetics, 2021
- Genotype imputation using the Positional Burrows Wheeler TransformPLOS Genetics, 2020
- The mutational constraint spectrum quantified from variation in 141,456 humansNature, 2020
- The “All of Us” Research ProgramNew England Journal of Medicine, 2019
- The UK Biobank resource with deep phenotyping and genomic dataNature, 2018
- A One-Penny Imputed Genome from Next-Generation Reference PanelsThe American Journal of Human Genetics, 2018
- Second-generation PLINK: rising to the challenge of larger and richer datasetsGigaScience, 2015
- Efficient haplotype matching and storage using the positional Burrows–Wheeler transform (PBWT)Bioinformatics, 2014
- Faculty Opinions recommendation of Modeling linkage disequilibrium and identifying recombination hotspots using single-nucleotide polymorphism data.2003