title: A Powerful Procedure for Pathway-Based Meta-analysis Using Summary Statistics Identifies 43 Pathways Associated with Type II Diabetes in European Populations.
abstract: Meta-analysis of multiple genome-wide association studies (GWAS) has become an effective approach for detecting single nucleotide polymorphism (SNP) associations with complex traits. However, it is difficult to integrate the readily accessible SNP-level summary statistics from a meta-analysis into more powerful multi-marker testing procedures, which generally require individual-level genetic data. We developed a general procedure called Summary based Adaptive Rank Truncated Product (sARTP) for conducting gene and pathway meta-analysis that uses only SNP-level summary statistics in combination with genotype correlation estimated from a panel of individual-level genetic data. We demonstrated the validity and power advantage of sARTP through empirical and simulated data. We conducted a comprehensive pathway-based meta-analysis with sARTP on type 2 diabetes (T2D) by integrating SNP-level summary statistics from two large studies consisting of 19,809 T2D cases and 111,181 controls with European ancestry. Among 4,713 candidate pathways from which genes in neighborhoods of 170 GWAS established T2D loci were excluded, we detected 43 T2D globally significant pathways (with Bonferroni corrected p-values < 0.05), which included the insulin signaling pathway and T2D pathway defined by KEGG, as well as the pathways defined according to specific gene expression patterns on pancreatic adenocarcinoma, hepatocellular carcinoma, and bladder carcinoma. Using summary data from 8 eastern Asian T2D GWAS with 6,952 cases and 11,865 controls, we showed 7 out of the 43 pathways identified in European populations remained to be significant in eastern Asians at the false discovery rate of 0.1. We created an R package and a web-based tool for sARTP with the capability to analyze pathways with thousands of genes and tens of thousands of SNPs.
authors.affiliation: Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, United States of America.
authors.forename: Han
authors.initials: H
authors.lastname: Zhang
authors.affiliation: Information Management Services Inc., Calverton, Maryland, United States of America.
authors.forename: William
authors.initials: W
authors.lastname: Wheeler
authors.affiliation: Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, United States of America.
authors.forename: Paula L
authors.initials: PL
authors.lastname: Hyland
authors.affiliation: Department of Statistics, University of Kentucky, Lexington, Kentucky, United States of America.
authors.forename: Yifan
authors.initials: Y
authors.lastname: Yang
authors.affiliation: Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, United States of America.
authors.forename: Jianxin
authors.initials: J
authors.lastname: Shi
authors.affiliation: Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland, United States of America.|Department of Oncology, School of Medicine, Johns Hopkins University, Baltimore, Maryland, United States of America.
authors.forename: Nilanjan
authors.initials: N
authors.lastname: Chatterjee
authors.affiliation: Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, United States of America.
authors.forename: Kai
authors.initials: K
authors.lastname: Yu
mesh_terms: D003924:Diabetes Mellitus, Type 2 / Q000235:genetics*; D005006:Ethnicity / Q000235:genetics*; D005060:Europe; D015870:Gene Expression / Q000235:genetics; D020022:Genetic Predisposition to Disease / Q000235:genetics*; D055106:Genome-Wide Association Study / Q000379:methods; D005838:Genotype; D006801:Humans; D020641:Polymorphism, Single Nucleotide / Q000235:genetics; D015398:Signal Transduction / Q000235:genetics*