Document Type
Article
Publication Date
1-1-2019
Department
Department of Mathematical Sciences
Abstract
There is increasing evidence showing that pleiotropy is a widespread phenomenon in complex diseases for which multiple correlated traits are often measured. Joint analysis of multiple traits could increase statistical power by aggregating multiple weak effects. Existing methods for multiple trait association tests usually study each of the multiple traits separately and then combine the univariate test statistics or combine p-values of the univariate tests for identifying disease associated genetic variants. However, ignoring correlation between phenotypes may cause power loss. Additionally, the genetic variants in one gene (including common and rare variants) are often viewed as a whole that affects the underlying disease since the basic functional unit of inheritance is a gene rather than a genetic variant. Thus, results from gene level association tests can be more readily integrated with downstream functional and pathogenic investigation, whereas many existing methods for multiple trait association tests only focus on testing a single common variant rather than a gene. In this article, we propose a statistical method by Testing an Optimally Weighted Combination of Multiple traits (TOW-CM) to test the association between multiple traits and multiple variants in a genomic region (a gene or pathway). We investigate the performance of the proposed method through extensive simulation studies. Our simulation studies show that the proposed method has correct type I error rates and is either the most powerful test or comparable with the most powerful tests. Additionally, we illustrate the usefulness of TOW-CM based on a COPDGene study.
Publication Title
PLoS One
Recommended Citation
Zhang, J.,
Sha, Q.,
Liu, G.,
&
Wang, X.
(2019).
A gene based approach to test genetic association based on an optimally weighted combination of multiple traits..
PLoS One,
14(8), 0220914-0220914.
http://doi.org/10.1371/journal.pone.0220914
Retrieved from: https://digitalcommons.mtu.edu/michigantech-p/381
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Version
Publisher's PDF
Publisher's Statement
©2019Zhanget al. Article deposited here in compliance with publisher policies. Publisher's version of record: https://doi.org/10.1371/journal.pone.0220914