Streamlining large-scale genomic data management: Insights from the UK Biobank whole-genome sequencing data
Year: 2025
Type: article
Abstract: Biobank-scale whole-genome sequencing (WGS) studies are increasingly pivotal in unraveling the genetic bases of diverse health outcomes. However, managing and analyzing these datasets' sheer volume and complexity presents significant challenges. We highlight the annotated genomic data structure (aGDS) format, substantially reducing the WGS data file size while enabling seamless integration of genomic and functional information for comprehensive WGS analyses. The aGDS format yielded 23 chromosome-specific files for the UK Biobank 500k WGS dataset, occupying only 1.10 tebibytes of storage. We develop the vcf2agds toolkit that streamlines the conversion of WGS data from VCF to aGDS format. Additionally, the STAARpipeline equipped with the aGDS files enabled scalable, comprehensive, and functionally informed WGS analysis, facilitating the detection of common and rare coding and noncoding phenotype-genotype associations. Overall, the vcf2agds toolkit and STAARpipeline provide a streamlined solution that facilitates efficient data management and analysis of biobank-scale WGS data across hundreds of thousands of samples.
Source: Cell Genomics
Authors: Xihao Li, Xihao Li, Andrew R. Wood, Yuxin Yuan, Manrui Zhang, Yushu Huang, Gareth Hawkes, Robin N. Beaumont, Michael N. Weedon, Wenyuan Li, Xiaoyu Li, Xiaoyu Li, Xihong Lin, Zilin Li
Language: English
Subfield: Genetics
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $8,900
Funders: UK Research and Innovation, National Institute for Health and Care Research, Department of Health and Social Care, Université de Genève, National Institutes of Health, Medical Research Council, Biotechnology and Biological Sciences Research Council, NIHR Imperial Biomedical Research Centre, National Institute on Aging, National Heart, Lung, and Blood Institute, National Human Genome Research Institute, National Cancer Institute
Awards: G1081030536, G208141779, G3089498897, G315517791, Using whole genome sequencing to identify non-coding elements associated with diabetes and related traits across ancestries, G4844573334, Deciphering the Non-Coding Genome using Millions of Diverse Whole-Genome Sequences, G9000038143, G9000039249, G9000044315, G9000293524, Predictive Modeling of the Functional and Phenotypic Impacts of Genetic Variants, Enhanced Machine Learning Tools for Complex Data Evaluation and Integration in Advancing Health Outcomes, Construction and Application of Comprehensive Knowledge Graphs for Alzheimer's Disease, Statistical Methods for Integrative Analysis of Large-Scale Multi-Ethnic Whole Genome Sequencing Studies and Biobanks of Common Diseases, G936437485
OpenAlex ID: W4414379968