title: iGWAS: Integrative Genome-Wide Association Studies of Genetic and Genomic Data for Disease Susceptibility Using Mediation Analysis.
abstract: Genome-wide association studies (GWAS) have been a standard practice in identifying single nucleotide polymorphisms (SNPs) for disease susceptibility. We propose a new approach, termed integrative GWAS (iGWAS) that exploits the information of gene expressions to investigate the mechanisms of the association of SNPs with a disease phenotype, and to incorporate the family-based design for genetic association studies. Specifically, the relations among SNPs, gene expression, and disease are modeled within the mediation analysis framework, which allows us to disentangle the genetic effect on a disease phenotype into two parts: an effect mediated through a gene expression (mediation effect, ME) and an effect through other biological mechanisms or environment-mediated mechanisms (alternative effect, AE). We develop omnibus tests for the ME and AE that are robust to underlying true disease models. Numerical studies show that the iGWAS approach is able to facilitate discovering genetic association mechanisms, and outperforms the SNP-only method for testing genetic associations. We conduct a family-based iGWAS of childhood asthma that integrates genetic and genomic data. The iGWAS approach identifies six novel susceptibility genes (MANEA, MRPL53, LYCAT, ST8SIA4, NDFIP1, and PTCH1) using the omnibus test with false discovery rate less than 1%, whereas no gene using SNP-only analyses survives with the same cut-off. The iGWAS analyses further characterize that genetic effects of these genes are mostly mediated through their gene expressions. In summary, the iGWAS approach provides a new analytic framework to investigate the mechanism of genetic etiology, and identifies novel susceptibility genes of childhood asthma that were biologically meaningful.
authors.affiliation: Departments of Epidemiology and Biostatistics, Brown University, Providence, Rhode Island, United States of America.
authors.forename: Yen-Tsung
authors.initials: YT
authors.lastname: Huang
authors.affiliation: Departments of Epidemiology and Biostatistics, Harvard School of Public Health, Boston, Massachusetts, United States of America.
authors.forename: Liming
authors.initials: L
authors.lastname: Liang
authors.affiliation: National Heart and Lung Institute, Imperial College London, London, United Kingdom.
authors.forename: Miriam F
authors.initials: MF
authors.lastname: Moffatt
authors.affiliation: National Heart and Lung Institute, Imperial College London, London, United Kingdom.
authors.forename: William O C M
authors.initials: WO
authors.lastname: Cookson
authors.affiliation: Departments of Epidemiology and Biostatistics, Harvard School of Public Health, Boston, Massachusetts, United States of America.
authors.forename: Xihong
authors.initials: X
authors.lastname: Lin
mesh_terms: D000465:Algorithms; D001249:Asthma / Q000235:genetics*; D002648:Child; D002874:Chromosome Mapping; D003198:Computer Simulation; D005190:Family; D008040:Genetic Linkage; D020022:Genetic Predisposition to Disease; D015894:Genome, Human; D055106:Genome-Wide Association Study; D023281:Genomics / Q000379:methods; D006801:Humans; D008957:Models, Genetic; D010641:Phenotype; D020641:Polymorphism, Single Nucleotide / Q000235:genetics*; D040641:Quantitative Trait Loci / Q000235:genetics*
keywords: childhood asthma; genome-wide association studies; integrative genomics; mediation analysis; variance component tests