Whole-genome sequencing of half-a-million UK Biobank participants

Abstract

Summary ParagraphWhole-genome sequencing (WGS) provides a comprehensive view of the genome, enabling detection of coding and non-coding genetic variation, and surveying complex regions which are difficult to genotype. Here, we report on whole-genome sequencing of 490,640 UK Biobank participants, building on previous genotyping1and whole-exome sequencing (WES) efforts2,3. This advance deepens our understanding of how genetics influences disease biology and further enhances the value of this open resource for the study of human biology and health. Coupling this dataset with rich phenotypic data, we surveyed within- and cross-ancestry genomic associations with health-related phenotypes and identified novel genetic and clinical insights. While most genome-wide significant associations with disease traits were primarily observed in Europeans, we also identified strong or novel signals in individuals of African and Asian ancestries. Deeper capture of exonic variation in both coding and UTR sequences, strengthened and surfaced novel insights relative to WES analyses. This landmark dataset, representing the largest collection of WGS and available to the UK Biobank research community, will enable advances into our understanding of the human genome, and facilitate the discovery of new diagnostics, therapeutics with higher efficacy and improved safety profile, and enable precision medicine strategies with the potential to improve global health.Abstract FigureGraphic summary.Framework of the WGS UKB study. This figure captures the flow of this manuscript. We start with the collection of patient samples by UK Biobank and followed by the strategy taken to perform WGS. We continue with quality control performed on GraphTyper and DRAGEN datasets, followed by variant calling of SNPs, in/dels, and structural variants (SV). Thereafter we defined the phenotypes (binary and quantitative) associated with SV, SNPs and at the gene level (rare variant analysis) and conclude with the definition of five ancestry groups and collective association effect as a cross-ancestry meta-analysis.

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“…Thus, a collapsing analysis using WGS data provided a better opportunity to identify rare non-coding variants linked to human disease. While the necessity for WGS data restricted our PheWAS to the UK Biobank, currently the only resource which couples WGS with phenotypic data, it still allowed for a comprehensive analysis as it represents the largest collection of WGS on nearly 500,000 individuals 56 .…”

“…Additionally, collapsing analysis, in which aggregation of rare variants is tested for association with disease, has emerged as a better way to understand the impact of rare variants captured in human disease. The UK Biobank Consortium updated the available Whole Genome Sequencing (WGS) data to a total of 490,640 individuals 56 and made it available on the trusted research environment (Research Analysis Platform) on DNANexus in 2023. The SNPs and short Indels in the WGS dataset were jointly called over all individuals using GraphTyper 82 .…”

“…Whole slide images were acquired on a Zeiss Axioscan Z.1 (Jena, Germany) with 20x objective, Hamamatsu Flash camera (Hamamatsu, Japan) Excelitas Xylis light source (PA), and DAPI/CFP/TRITC/Cy5 filters from Semrock (NY).is tested for association with disease, has emerged as a better way to understand the impact of rare variants captured in human disease. The UK Biobank Consortium updated the available Whole Genome Sequencing (WGS) data to a total of 490,640 individuals56 and made it available 61 on…”

MultiSuSiE improves multi-ancestry fine-mapping in All of Us whole-genome sequencing data

“…We have developed MultiSuSiE, a fast and powerful method for multi-ancestry fine-mapping, and applied MultiSuSiE to 14 quantitative traits leveraging WGS data in 47k African-ancestry and 94k European-ancestry individuals from All of Us; our analysis of WGS data avoids ancestry-based differences in imputation quality in genotyping chip data 26 , which may produce false-positive discoveries 27,28 . MultiSuSiE extends the approach introduced by SuSiE 13,18 to efficiently search the high-dimensional space of causal variant configurations, while allowing effect sizes to vary across ancestries.…”

Efficient storage and regression computation for population-scale genome sequencing studies

“…Our regression approaches are designed specifically to address the unique challenges presented by case-control, quantitative, and multi-phenotype analysis where a large fraction ( > 99.99%) of the greater than 1 billion of genetic variants discovered from whole genome sequencing studies are rare (i.e. minor allele frequency less than 1% in the population) 10 . These methods utilize sparse computation techniques to enhance processing speed without compromising analytical accuracy.…”