Genome-wide genetic data on ~500,000 UK Biobank participants

TLDR

The UK Biobank is a large prospective cohort of ~500,000 adults aged 40–69, providing unprecedented phenotypic depth and scope, and its genome‑wide genotype data offers new opportunities for quality assessment while posing challenges due to diverse ancestries. The study aims to describe the genome‑wide genotype data (~805,000 markers) collected from all participants and the quality‑control procedures applied. The authors generated, QC‑filtered, phased, and imputed the data to ~96 million variants using the HRC and UK10K reference panels, provided tools for efficient GWAS and PheWAS with a compressed format, and validated the dataset through HLA allele imputation and a height GWAS. Analyses reveal population structure and relatedness, a >100‑fold increase in testable variants, successful replication of known HLA–disease associations, and a height GWAS that confirms the dataset’s analytical utility.

Abstract

Abstract The UK Biobank project is a large prospective cohort study of ~500,000 individuals from across the United Kingdom, aged between 40-69 at recruitment. A rich variety of phenotypic and health-related information is available on each participant, making the resource unprecedented in its size and scope. Here we describe the genome-wide genotype data (~805,000 markers) collected on all individuals in the cohort and its quality control procedures. Genotype data on this scale offers novel opportunities for assessing quality issues, although the wide range of ancestries of the individuals in the cohort also creates particular challenges. We also conducted a set of analyses that reveal properties of the genetic data – such as population structure and relatedness – that can be important for downstream analyses. In addition, we phased and imputed genotypes into the dataset, using computationally efficient methods combined with the Haplotype Reference Consortium (HRC) and UK10K haplotype resource. This increases the number of testable variants by over 100-fold to ~96 million variants. We also imputed classical allelic variation at 11 human leukocyte antigen (HLA) genes, and as a quality control check of this imputation, we replicate signals of known associations between HLA alleles and many common diseases. We describe tools that allow efficient genome-wide association studies (GWAS) of multiple traits and fast phenome-wide association studies (PheWAS), which work together with a new compressed file format that has been used to distribute the dataset. As a further check of the genotyped and imputed datasets, we performed a test-case genome-wide association scan on a well-studied human trait, standing height.