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Association Tests of Multiple Phenotypes: ATeMP.

Xiaobo Guo1, Yixi Li2, Xiaohu Ding3

  • 1Department of Statistical Science, School of Mathematics & Computational Science, Sun Yat-Sen University, Guangzhou, GD 510275, China; SYSU-CMU Shunde International Joint Research Institute, Shunde, GD 528300, China; Southern China Research Center of Statistical Science, Sun Yat-Sen University, Guangzhou, GD 510275, China.

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Summary

Jointly analyzing multiple phenotypes in genome-wide association studies (GWASs) is crucial. This study reveals MultiPhen loses power with non-normal phenotypes and proposes improved methods, ATeMP-rn and ATeMP-or, for better performance.

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Area of Science:

  • Genetics
  • Statistical Genetics
  • Bioinformatics

Background:

  • Joint analysis of multiple phenotypes in genome-wide association studies (GWASs) is increasingly important for complex human disorders.
  • The MultiPhen method uses a proportional odds model but its performance with non-normal phenotypic distributions is not well understood.
  • Measurement errors in explanatory variables can attenuate estimation in statistical models.

Purpose of the Study:

  • To investigate the properties and performance of the MultiPhen method, particularly when phenotypes are non-normally distributed.
  • To establish an equivalence between MultiPhen and the generalized Kendall tau association test.
  • To develop and validate improved methods for joint phenotype analysis in GWASs.

Main Methods:

  • Established an equivalence relationship between MultiPhen and the generalized Kendall tau association test.
  • Demonstrated that MultiPhen may lose statistical power when phenotypes exhibit non-normal distributions.
  • Proposed two novel solutions, ATeMP-rn and ATeMP-or, to enhance the power of MultiPhen.

Main Results:

  • The equivalence analysis revealed potential power loss in MultiPhen for non-normal phenotypes.
  • Simulation studies confirmed the reduced performance of MultiPhen under non-normality.
  • The proposed ATeMP-rn and ATeMP-or methods demonstrated improved effectiveness in maintaining statistical power.
  • A real case study from the Guangzhou Twin Eye Study validated the proposed methods.

Conclusions:

  • MultiPhen's performance is sensitive to phenotypic distribution, potentially losing power with non-normal data.
  • The novel ATeMP-rn and ATeMP-or methods offer robust alternatives for joint phenotype association analysis in GWASs.
  • These improved methods enhance the reliability and power of genetic association studies for complex traits.