LSMM: a statistical approach to integrating functional annotations with genome-wide association studies.Motivation
 In this study, we propose a latent sparse mixed model (LSMM) to integrate functional annotations with GWAS data.  To allow LSMM scalable to millions of variants and hundreds of functional annotations, we developed an efficient variational expectation-maximization algorithm for model parameter estimation and statistical inference.  We first conducted comprehensive simulation studies to evaluate the performance of LSMM.  The LSMM software is available at https://github.com/mingjingsi/LSMM.



LSMM