A Pleiotropy-Informed Bayesian False Discovery Rate Adapted to a Shared Control Design Finds New Disease Associations From GWAS Summary Statistics

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Abstract

<title>Author Summary</title> <p>Many diseases have a significant hereditary component, only part of which has been explained by analysis of genome-wide association studies (GWAS). Shared aetiology, treatment protocols, and overlapping results from existing GWAS suggest similarities in genetic susceptibility between related diseases, which may be exploited to detect more disease-associated SNPs without the need for further data. We extend an existing method for detecting SNPs associated with a given disease by conditioning on association with another disease. Our extension allows GWAS for the two conditions to share control samples, enabling larger overall control groups and application to the common case when GWAS for related diseases pool control samples. We demonstrate that our technique limits the expected overall false discovery rate at a threshold dependent on the two diseases. We apply our technique to genotype data from ten immune mediated diseases. Overall pleiotropy between phenotypes is demonstrated graphically. We are able to declare several SNPs significant at a genome-wide level whilst controlling at a lower false-discovery rate than would be possible using a conventional approach, identifying eight previously unknown disease associations. This technique can improve SNP detection in GWAS by re-analysing existing data, and gives insight into the shared genetic bases of autoimmune diseases.</p>

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Liley, J., & Wallace, C. (2015). A Pleiotropy-Informed Bayesian False Discovery Rate Adapted to a Shared Control Design Finds New Disease Associations From GWAS Summary Statistics. PLoS Genetics, 11(2), 1–26. https://doi.org/10.1371/journal.pgen.1004926

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