Submitted by webadmin on Thu, 02/24/2022 - 10:37
Knockoff Statistics for Variable Selection in Genetic Association Studies with Trio Design

The father-mother-child trio design is a popular family-based design to study the genetics of complex traits, especially those with early onset. We propose KnockoffTrio, a novel statistical method to identify putative causal genetic variants for trio design built upon a recently developed knockoff framework in statistics. KnockoffTrio controls the false discovery rate in the presence of arbitrary correlations among tests, and is less conservative and thus more powerful than the conventional methods that control the family-wise error rate via Bonferroni correction. KnockoffTrio is naturally robust against bias induced by population substructure because it is derived conditional on parental genotypes. Furthermore, KnockoffTrio is not restricted to family-based association tests and can be used in conjunction with more powerful, potentially nonlinear models to improve power of standard family-based tests. We show using empirical simulations that KnockoffTrio can prioritize causal variants over associations due to linkage disequilibrium. We show applications to 5,758 trios from three study cohorts for autism spectrum disorders and show that the KnockoffTrio can identify several significant associations that are missed by conventional tests applied to the same data.


Speaker: Dr Yi YANG
Date: 8 March 2022 (Tuesday)
Time: 10:00am – 11:00am
PosterClick here



Dr. Yi YANG is a postdoctoral research scientist in the Department of Biostatistics at Columbia University. He received a PhD in biostatistics from the University of Minnesota. Prior to his PhD, he received a master’s degree in social work from New York University and a bachelor’s degree in management information systems from Zhejiang University Chu Kochen Honors College. Dr. Yi Yang’s methodology research has focused on the variable selection in statistical genetics with Bayesian hierarchical models and knockoff statistics.