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Testing Genetic Association by Regressing Genotype over Multiple Phenotypes

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  • Kai Wang

Abstract

Complex disorders are typically characterized by multiple phenotypes. Analyzing these phenotypes jointly is expected to be more powerful than dealing with one of them at a time. A recent approach (O'Reilly et al. 2012) is to regress the genotype at a SNP marker on multiple phenotypes and apply the proportional odds model. In the current research, we introduce an explicit expression for the score test statistic and its non-centrality parameter that determines its power. Same simulation studies as those reported in Galesloot et al. (2014) were conducted to assess its performance. We demonstrate by theoretical arguments and simulation studies that, despite its potential usefulness for multiple phenotypes, the proportional odds model method can be less powerful than regular methods for univariate traits. We also introduce an implementation of the proposed score statistic in an R package named iGasso.

Suggested Citation

  • Kai Wang, 2014. "Testing Genetic Association by Regressing Genotype over Multiple Phenotypes," PLOS ONE, Public Library of Science, vol. 9(9), pages 1-9, September.
    • Handle: RePEc:plo:pone00:0106918
    DOI: 10.1371/journal.pone.0106918
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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.
    2. Tessel E Galesloot & Kristel van Steen & Lambertus A L M Kiemeney & Luc L Janss & Sita H Vermeulen, 2014. "A Comparison of Multivariate Genome-Wide Association Methods," PLOS ONE, Public Library of Science, vol. 9(4), pages 1-8, April.
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