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A novel method to test associations between a weighted combination of phenotypes and genetic variants

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  • Huanhuan Zhu
  • Shuanglin Zhang
  • Qiuying Sha

Abstract

Many complex diseases like diabetes, hypertension, metabolic syndrome, et cetera, are measured by multiple correlated phenotypes. However, most genome-wide association studies (GWAS) focus on one phenotype of interest or study multiple phenotypes separately for identifying genetic variants associated with complex diseases. Analyzing one phenotype or the related phenotypes separately may lose power due to ignoring the information obtained by combining phenotypes, such as the correlation between phenotypes. In order to increase statistical power to detect genetic variants associated with complex diseases, we develop a novel method to test a weighted combination of multiple phenotypes (WCmulP). We perform extensive simulation studies as well as real data (COPDGene) analysis to evaluate the performance of the proposed method. Our simulation results show that WCmulP has correct type I error rates and is either the most powerful test or comparable to the most powerful test among the methods we compared. WCmulP also has an outstanding performance for identifying single-nucleotide polymorphisms (SNPs) associated with COPD-related phenotypes.

Suggested Citation

  • Huanhuan Zhu & Shuanglin Zhang & Qiuying Sha, 2018. "A novel method to test associations between a weighted combination of phenotypes and genetic variants," PLOS ONE, Public Library of Science, vol. 13(1), pages 1-17, January.
    • Handle: RePEc:plo:pone00:0190788
    DOI: 10.1371/journal.pone.0190788
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    References listed on IDEAS

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    1. Paul F O’Reilly & Clive J Hoggart & Yotsawat Pomyen & Federico C F Calboli & Paul Elliott & Marjo-Riitta Jarvelin & Lachlan J M Coin, 2012. "MultiPhen: Joint Model of Multiple Phenotypes Can Increase Discovery in GWAS," PLOS ONE, Public Library of Science, vol. 7(5), pages 1-1, May.
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