Sparse block signal detection and identification for shared cross-trait association analysis

Genome-wide association studies (GWAS) have identified thousands of single nucleotide polymorphisms (SNPs) that are associated with complex traits. GWAS data allows us to investigate the shared genetic etiologies among different traits. However, linkage disequilibrium (LD) between the SNPs complicates the detection and identification of shared genetic effects. In this paper we model the LD by dividing the genome into LD blocks and linking the genetic variants within a block to a possible latent causal variant. An eigenvector-projected score statistic that leverages the set of variants in LD and a maxtype test statistic (Max-block) are proposed to detect the existence of cross-trait genetic association. The Max-block is easy to calculate and is shown to control the genome-wide error rate. After the detection a stepwise procedure is proposed to identify the significant blocks that explain the genetic sharing between two traits. Simulation experiments show that Max-block is more powerful than standard approaches in the sparse settings and is robust to different signal strengths or levels of sparsity. The method is applied to study shared cross-trait associations in 10 pediatric autoimmune diseases and identified several regions that explain the genetic sharing between juvenile idiopathic arthritis (JIA) and ulcerative colitis (UC) and between UC and Crohn’s disease (CD). In addition, our analysis also indicates the genetic sharing in the MHC region among systemic lupus (SLE), celiac disease (CEL) and common variable immunodeficiency (CVID). Results from real data and simulation studies show that Max-block provides an important alternative to commonly used genetic correlation estimation in understanding genetic correlation among complex diseases.