Sparse Group Models for investigating Pleiotropy effect from genome-wide association studies

Genome-wide association studies (GWAS) focus on testing association between millions of genetic markers (or single nucleotide polymorphisms, SNPs) and a phenotype in an agnostic way, where every SNP is tested independently from the other SNPs for association with the phenotype. One major finding from GWAS era is that pleiotropy – that occurs when one gene influence two or more unrelated traits - is a widespread phenomenon in human complex traits. Several methods were proposed to combine results across studies of different phenotypes in order to improve the power of detecting pleiotropic associations at SNP level. It is well established that incorporating prior biological knowledge as gene or biological pathways structures to consider complex mechanisms can help to discover additional genetic risk factors. We propose differentSparse Group Models  considering gene (or pathway) structure We develop methods using both penalised likelihood methods and Bayesian spike and slab priors to induce structured sparsity at a pathway, gene or SNP level.