Knockoff-Based Statistics for the Identification of Putative Causal Genes in Genetic Studies

Gene-based tests are important tools for elucidating the genetic basis of complex traits. Despite substantial recent efforts in this direction, the existing tests are still limited, owing to low power and detection of false-positive signals due to the confounding effects of linkage disequilibrium. In this talk, we describe a gene-based test that attempts to address these limitations by incorporating data on long-range chromatin interactions, several recent technical advances for region-based testing, and the knockoff framework for synthetic genotype generation. Through extensive simulations and applications to multiple diseases and traits, we show that the proposed test increases the power over state-of-the-art gene-based tests and provides a narrower focus on the possible causal genes involved at a locus. We also propose a computationally efficient gene-based testing approach for biobank-scale data, and show applications to UK Biobank data with 405,296 participants for multiple binary and quantitative traits.