A Distributed Analysis Method for Detecting Genetic Interactions for Complex Diseases in Large Research Consortia

• Activity Number: 300 - Gene-Gene and Gene-Environment Interactions
• Type: Contributed
• Date/Time: Tuesday, August 1, 2017 : 8:30 AM to 10:20 AM
• Sponsor: Section on Statistics in Genomics and Genetics
• Author(s): Yulun Liu* and Jing Huang and Elisabetta Manduchi and Paul Scheet and Jason Moore and Yong Chen
• Companies: University of Pennsylvania and University of Pennsylvania and University of Pennsylvania and The University of Texas MD Anderson Cancer Center and University of Pennsylvania and University of Pennsylvania
• Keywords: Gene-gene interaction ; Heterogeneity ; Meta-analysis

Abstract

Many genomics research groups and the related initiatives collaborate to form large-scale consortia and develop open access to enable wide-scale sharing of genome-wide association study (GWAS) data. Despite the perceived benefits of data sharing from large consortia, some potential issues such as the privacy-preserving at individual patient-level, heterogeneous data sources and the other practical factors are raised by data sharing. This leads to the demand of new statistical approaches for the distributed analyses of GWAS databases. In this paper, we develop a novel two-stage testing procedure, named as phylogenY-based Effect-size Tests for Interactions using first 2 moments (YETI2), to detect gene-gene interactions through both pooled marginal effects and heterogeneity across study sites using a meta-analytic framework. Our proposed method is a computationally fast algorithm for combining multiple and distributed GWAS databases, compared with the conventional distributed regression analysis. We illustrate the proposed method using the bladder cancer data from the Database of Genotype and Phenotype (dbGaP).