Kévin Caye

This paper introduces sparse latent factor regression models for genome-wide and epigenome-wide association studies, showing improved statistical performance and robustness in both simulated and real data applications.

LFMM 2.0 improves confounder adjustment in genome and epigenome-wide association studies using latent factor models, outperforming existing methods in simulations and providing new insights into disease mechanisms.