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Nonlinear post-selection inference for genome-wide association studies

Abstract : Association testing in genome-wide association studies (GWAS) is often performed at either the SNP level or the gene level. The two levels can bring different insights into disease mechanisms. In the present work, we provide a novel approach based on nonlinear post-selection inference to bridge the gap between them. Our approach selects, within a gene, the SNPs or LD blocks most associated with the phenotype, before testing their combined effect. Both the selection and the association testing are conducted nonlinearly. We apply our tool to the study of BMI and its variation in the UK BioBank. In this study, our approach outperformed other gene-level association testing tools, with the unique benefit of pinpointing the causal SNPs.
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Contributor : Chloé-Agathe Azencott Connect in order to contact the contributor
Submitted on : Tuesday, January 26, 2021 - 7:16:03 PM
Last modification on : Monday, January 10, 2022 - 10:16:05 AM

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Lotfi Slim, Clément Chatelain, Chloé-Agathe Azencott. Nonlinear post-selection inference for genome-wide association studies. Pacific Symposium on Biocomputing, Jan 2022, Kohala Coast, Hawaii, United States. ⟨10.1101/2020.09.30.320515⟩. ⟨hal-03122125⟩



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