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Multiple phenotype association tests using summary statistics in genome-wide association studies.

Zhonghua Liu1, Xihong Lin1

  • 1Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston 02115, U.S.A.

Biometrics
|June 28, 2017
PubMed
Summary

This study introduces novel methods for jointly testing genetic associations with multiple correlated phenotypes using Genome-Wide Association Studies (GWAS) summary statistics. The approach enhances discovery of genetic variants influencing complex traits without individual-level data.

Keywords:
Correlated phenotypesFisher methodLinear mixed modelsPleiotropySummary statisticsVariance component test

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Area of Science:

  • Genetics
  • Statistical Genetics
  • Bioinformatics

Background:

  • Genome-Wide Association Studies (GWAS) typically analyze single traits, potentially missing variants affecting multiple correlated phenotypes.
  • Existing methods often require individual-level data or make restrictive assumptions about phenotype correlations.
  • Genetic variants frequently exhibit pleiotropy, influencing multiple traits simultaneously.

Purpose of the Study:

  • To develop robust and powerful statistical methods for jointly testing genetic associations with multiple correlated phenotypes using only GWAS summary statistics.
  • To account for arbitrary between-phenotype correlations without accessing individual-level genetic data.
  • To improve the power and efficiency of genetic discovery in large-scale association studies.

Main Methods:

  • Estimation of the between-phenotype correlation matrix from individual phenotype GWAS summary statistics.
  • Development of novel genetic association tests for multiple phenotypes within a linear mixed model framework for summary statistics.
  • Joint testing of common mean and variance components, with analytical computation of p-values for computational efficiency.

Main Results:

  • Simulation studies confirmed that the proposed tests maintain correct Type I error rates and offer improved power compared to existing methods.
  • The methods successfully identified additional genetic variants associated with lipid traits in the Global Lipids Genetics Consortium GWAS dataset.
  • These variants were not detected by the original single-trait analyses, demonstrating the utility of multi-phenotype testing.

Conclusions:

  • The proposed methods provide a computationally efficient and statistically robust approach for multi-phenotype association testing using GWAS summary statistics.
  • This framework enhances the ability to detect pleiotropic genetic effects and discover novel genotype-phenotype associations.
  • The methods are practically valuable for large-scale genetic studies, facilitating a more comprehensive understanding of genetic architecture.