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Testing an optimally weighted combination of common and/or rare variants with multiple traits

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  • Zhenchuan Wang
  • Qiuying Sha
  • Shurong Fang
  • Kui Zhang
  • Shuanglin Zhang

Abstract

Recently, joint analysis of multiple traits has become popular because it can increase statistical power to identify genetic variants associated with complex diseases. In addition, there is increasing evidence indicating that pleiotropy is a widespread phenomenon in complex diseases. Currently, most of existing methods test the association between multiple traits and a single genetic variant. However, these methods by analyzing one variant at a time may not be ideal for rare variant association studies because of the allelic heterogeneity as well as the extreme rarity of rare variants. In this article, we developed a statistical method by testing an optimally weighted combination of variants with multiple traits (TOWmuT) to test the association between multiple traits and a weighted combination of variants (rare and/or common) in a genomic region. TOWmuT is robust to the directions of effects of causal variants and is applicable to different types of traits. Using extensive simulation studies, we compared the performance of TOWmuT with the following five existing methods: gene association with multiple traits (GAMuT), multiple sequence kernel association test (MSKAT), adaptive weighting reverse regression (AWRR), single-TOW, and MANOVA. Our results showed that, in all of the simulation scenarios, TOWmuT has correct type I error rates and is consistently more powerful than the other five tests. We also illustrated the usefulness of TOWmuT by analyzing a whole-genome genotyping data from a lung function study.

Suggested Citation

  • Zhenchuan Wang & Qiuying Sha & Shurong Fang & Kui Zhang & Shuanglin Zhang, 2018. "Testing an optimally weighted combination of common and/or rare variants with multiple traits," PLOS ONE, Public Library of Science, vol. 13(7), pages 1-16, July.
    • Handle: RePEc:plo:pone00:0201186
    DOI: 10.1371/journal.pone.0201186
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    References listed on IDEAS

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    1. Matthew Stephens, 2013. "A Unified Framework for Association Analysis with Multiple Related Phenotypes," PLOS ONE, Public Library of Science, vol. 8(7), pages 1-19, July.
    2. Qiong Yang & Yuanjia Wang, 2012. "Methods for Analyzing Multivariate Phenotypes in Genetic Association Studies," Journal of Probability and Statistics, Hindawi, vol. 2012, pages 1-13, July.
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