False discovery rate control in genome-wide association studies with population structure

• Published in: Proceedings of the National Academy of Sciences - PNAS Vol. 118; no. 40; pp. 1 - 12
• Main Authors: Sesia, Matteo, Bates, Stephen, Candès, Emmanuel, Marchini, Jonathan, Sabatti, Chiara
• Format: Journal Article
• Language: English
• Published: United States National Academy of Sciences 05-10-2021
• Subjects: Algorithms; Biobanks; Biological Sciences; Data analysis; Genome, Human - genetics; Genome-wide association studies; Genome-Wide Association Study - methods; Genomes; Genotype; Genotypes; Human populations; Humans; Linkage disequilibrium; Linkage Disequilibrium - genetics; Multifactorial Inheritance - genetics; Phenotype; Phenotypes; Physical Sciences; Polygenic inheritance; Population (statistical); Population genetics; Population structure; Population studies; Software; hidden Markov models; population structure; false discovery rate; genome-wide association studies; knockoffs
• ISSN: 0027-8424; 1091-6490; 1091-6490
• DOI: 10.1073/pnas.2105841118

We present a comprehensive statistical framework to analyze data from genome-wide association studies of polygenic traits, producing interpretable findings while controlling the false discovery rate. In contrast with standard approaches, our method can leverage sophisticated multivariate algorithms but makes no parametric assumptions about the unknown relation between genotypes and phenotype. Instead, we recognize that genotypes can be considered as a random sample from an appropriate model, encapsulating our knowledge of genetic inheritance and human populations. This allows the generation of imperfect copies (knockoffs) of these variables that serve as ideal negative controls, correcting for linkage disequilibrium and accounting for unknown population structure, which may be due to diverse ancestries or familial relatedness. The validity and effectiveness of our method are demonstrated by extensive simulations and by applications to the UK Biobank data. These analyses confirm our method is powerful relative to state-of-the-art alternatives, while comparisons with other studies validate most of our discoveries. Finally, fast software is made available for researchers to analyze Biobank-scale datasets.