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Methods for Analyzing Multivariate Phenotypes in Genetic Association Studies

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  • Qiong Yang
  • Yuanjia Wang

Abstract

Multivariate phenotypes are frequently encountered in genetic association studies. The purpose of analyzing multivariate phenotypes usually includes discovery of novel genetic variants of pleiotropy effects, that is, affecting multiple phenotypes, and the ultimate goal of uncovering the underlying genetic mechanism. In recent years, there have been new method development and application of existing statistical methods to such phenotypes. In this paper, we provide a review of the available methods for analyzing association between a single marker and a multivariate phenotype consisting of the same type of components (e.g., all continuous or all categorical) or different types of components (e.g., some are continuous and others are categorical). We also reviewed causal inference methods designed to test whether the detected association with the multivariate phenotype is truly pleiotropy or the genetic marker exerts its effects on some phenotypes through affecting the others.

Suggested Citation

  • 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.
    • Handle: RePEc:hin:jnljps:652569
    DOI: 10.1155/2012/652569
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    Cited by:

    1. Nan Lin & Yun Zhu & Ruzong Fan & Momiao Xiong, 2017. "A quadratically regularized functional canonical correlation analysis for identifying the global structure of pleiotropy with NGS data," PLOS Computational Biology, Public Library of Science, vol. 13(10), pages 1-33, October.
    2. 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.
    3. Michael C Turchin & Matthew Stephens, 2019. "Bayesian multivariate reanalysis of large genetic studies identifies many new associations," PLOS Genetics, Public Library of Science, vol. 15(10), pages 1-18, October.

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