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Activity Number: 237 - SPEED:Statistical Methods for GWAs, Genetics, Genomics, and Other Omics Studies, Part 1
Type: Contributed
Date/Time: Monday, July 29, 2019 : 2:00 PM to 3:50 PM
Sponsor: Section on Statistics in Genomics and Genetics
Abstract #303096 Presentation
Title: Multivariate Association Analysis with Correlated Traits in Families
Author(s): Souvik Seal*
Companies: Division of Biostatistics, University of Minnesota
Keywords: Multivariate Analysis of Variance; Seemingly unrelated Regression; Multiple phenotypes; Family data; Score test; Marginal association test

Multiple genome-wide association studies have reported variants that affects multiple traits, demonstrating evidence of pleiotropy or shared genetic basis for multiple phenotypes. But the systematic detection of such effects can be very challenging. Joint analysis of these correlated phenotypes can improve power to detect these pleiotropic variants. However, the implementation of many such joint analysis techniques can be computationally intensive at a genomewide level. These multivariate techniques are even harder to implement in families. Multivariate analysis of variance (MANOVA) is a popular technique to perform a joint analysis of correlated traits. Implementation of MANOVA in families would require modeling both familial correlation as well as between trait correlation and hence can be computationally very intensive on a genome-wide scale. In this paper, we develop a test based on seemingly unrelated regression (SUR) which can be viewed as an extension of MANOVA for unrelated individuals in a familial setup. We also extend two other marginal score based tests in the familial context. All the tests are computationally rapid and thus, suitable on a genomewide scale.

Authors who are presenting talks have a * after their name.

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