An efficient hierarchical generalized linear mixed model for pathway analysis of genome-wide association studies
Year: 2011
Type: article
Abstract: MOTIVATION: In genome-wide association studies (GWAS) of complex diseases, genetic variants having real but weak associations often fail to be detected at the stringent genome-wide significance level. Pathway analysis, which tests disease association with combined association signals from a group of variants in the same pathway, has become increasingly popular. However, because of the complexities in genetic data and the large sample sizes in typical GWAS, pathway analysis remains to be challenging. We propose a new statistical model for pathway analysis of GWAS. This model includes a fixed effects component that models mean disease association for a group of genes, and a random effects component that models how each gene's association with disease varies about the gene group mean, thus belongs to the class of mixed effects models. RESULTS: The proposed model is computationally efficient and uses only summary statistics. In addition, it corrects for the presence of overlapping genes and linkage disequilibrium (LD). Via simulated and real GWAS data, we showed our model improved power over currently available pathway analysis methods while preserving type I error rate. Furthermore, using the WTCCC Type 1 Diabetes (T1D) dataset, we demonstrated mixed model analysis identified meaningful biological processes that agreed well with previous reports on T1D. Therefore, the proposed methodology provides an efficient statistical modeling framework for systems analysis of GWAS. AVAILABILITY: The software code for mixed models analysis is freely available at http://biostat.mc.vanderbilt.edu/LilyWang.
Source: Bioinformatics
Institutions: Vanderbilt University, Vanderbilt-Ingram Cancer Center
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: bronze
Funders: National Alliance for Research on Schizophrenia and Depression, Wellcome Trust, National Institutes of Health, National Institute on Alcohol Abuse and Alcoholism, National Institute of Mental Health, National Cancer Institute, National Institute of General Medical Sciences, National Institute of Allergy and Infectious Diseases, National Institute of Diabetes and Digestive and Kidney Diseases, National Center for Research Resources, Eunice Kennedy Shriver National Institute of Child Health and Human Development
Awards: Eunice Kennedy Shriver Intellectual and Developmental Disability Research Center, Generation of Ethanol Response Gene Resource by Large-Scale Data Integration, Genes Controlling Assembly and Function of Serotonin Systems, IFN-Gamma Gene Regulation in T Cells and NK Cells, Medical Scientist Training Program, SPORE in GI Cancer, Gene Expression and Diagnosis of Autoimmune Disease, Gene Expression and Diagnosis of Diabetes, The Vanderbilt Institute for Clinical and Translational Research (VICTR) TL1, G9000049790, G9000121181, Cancer Center Support Grant
OpenAlex ID: W2127720359