Poster

Category:
Family, Child, Adolescent Health (Includes Maternal & Child Health)
Year:
2017
Title:
Meta-analysis of Quantitative Pleiotropic Traits at Gene Level with Multivariate Functional Linear Models
Presenter:
(Other Other)
Authors:
Chiu, Chi-Yang (The Eunice Kennedy Shriver National Institute of Child Health and Human Development), Jung, Jeesun (National Institute on Alcohol Abuse and Alcoholism), Chen, Wei (University of Pittsburgh), Weeks, Daniel (University of Pittsburgh), Ren, Haobo (DataParadise Inc), Ting Lee, Mei-ling (University of Maryland), Xiong, Momiao (University of Texas), Fan, Ruzong (Georgetown University Medical Center)
Abstract:
For a meta-analysis of multiple studies, multivariate functional linear models are developed to connect genetic variant data to multiple quantitative traits adjusting for covariates. The goal is to take the advantage of both meta-analysis and pleiotropy analysis in order to improve power and to carry out a unified association analysis of multiple studies and multiple traits of complex disorders. Three types of approximate F-distributions based on Pillai-Bartlett trace, Hotelling-Lawley trace, and Wilks's Lambda are introduced to test association between multiple quantitative traits and multiple genetic variants. Simulation analysis is performed to evaluate the false positive rates and power performance of the proposed models and tests. The proposed methods were applied to analyze lipid traits in eight European cohorts.