Multi-resolution localization of causal variants across the genome

Matteo Sesia, Eugene Katsevich, Stephen Bates, Emmanuel Candès () and Chiara Sabatti ()
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Matteo Sesia: Stanford University
Eugene Katsevich: Carnegie Mellon University
Stephen Bates: Stanford University
Emmanuel Candès: Stanford University
Chiara Sabatti: Stanford University

Nature Communications, 2020, vol. 11, issue 1, 1-10

Abstract: Abstract In the statistical analysis of genome-wide association data, it is challenging to precisely localize the variants that affect complex traits, due to linkage disequilibrium, and to maximize power while limiting spurious findings. Here we report on KnockoffZoom: a flexible method that localizes causal variants at multiple resolutions by testing the conditional associations of genetic segments of decreasing width, while provably controlling the false discovery rate. Our method utilizes artificial genotypes as negative controls and is equally valid for quantitative and binary phenotypes, without requiring any assumptions about their genetic architectures. Instead, we rely on well-established genetic models of linkage disequilibrium. We demonstrate that our method can detect more associations than mixed effects models and achieve fine-mapping precision, at comparable computational cost. Lastly, we apply KnockoffZoom to data from 350k subjects in the UK Biobank and report many new findings.

Date: 2020
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DOI: 10.1038/s41467-020-14791-2

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