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The UK Biobank is a collection of a half million individuals with paired genetic and phenotype information that has been enormously valuable in studies of genetic etiology for common diseases and traits. However, most genome-wide analyses of this dataset use only the European ancestry individuals. Analyzing a more inclusive and diverse dataset increases power and improves the potential for discovery. Here, we present a multi-ancestry analysis of 7,221 phenotypes, across 6 continental ancestry groups, for a total of 16,119 genome-wide association studies.
Methodological extensions to estimate genetic heritability and shared risk factors for phenotypes of the UK Biobank
We will investigate new approaches to estimating heritability for a wide range of phenotypes and health outcomes, and look at what genetic risk factors are shared between them. We'll extend our existing approach that uses genome-wide association study summary statistics, to consider more sophisticated models that capture a broader range of possible effects (eg. dominance, epistasis). Our new method will simultaneously estimate heritability and the true causal SNP effects that account for LD. We'll gain a clearer understanding of which loci are risk factors for a range of diseases/traits and how these are shared between them. This work will estimate heritability and detect shared heritability between a large set of phenotypes. In doing so, we aim to find shared genetic risk factors that help explain disease comorbidity and associated risk factors. This work is in line with the UK Biobank?s aim of enabling research to improve ?prevention, diagnosis and treatment of illness and the promotion of health throughout society?. It will not focus on one particular disorder or subtype of disorder, but instead apply these methods to learn about a wide range of traits through a hypothesis-free approach. We will build on our previously published work to see if more sophisticated models of genetic risk explain more heritability of a broad range of diseases/traits and that which is shared between them. With an approach to infer the true causal effect of genetic variants, rather than that which is confounded by the fact that neighbouring variants often occur together, we obtain a more accurate picture of exactly which ones are protective or pathogenic. This work would involve using imputed genotype data and all available phenotypes (self-reported phenotypes, medical records/registry data). We are requesting the full UK Biobank cohort, including genetic data on all participants.