Bivariate association analysis for quantitative traits using generalized estimation equation

Fang Yang; Zihui Tang; Hongwen Deng

doi:10.1016/S1673-8527(08)60166-6

Volume 36 Issue 12

Dec. 2009

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Article Navigation > Journal of Genetics and Genomics > 2009 > 36(12): 733-743

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Bivariate association analysis for quantitative traits using generalized estimation equation

doi: 10.1016/S1673-8527(08)60166-6

a
Laboratory of Molecular and Statistical Genetics, College of Life Sciences, Hunan Normal University, Changsha 410081, China
b
Center of Systematic Biomedical Research, Shanghai University of Science and Technology, Shanghai 200093, China
c
Departments of Orthopedic Surgery and Basic Medical Sciences, University of Missouri-Kansas City, Kansas City, MO 64108, USA

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Corresponding author: E-mail address: hwdeng@hunnu.edu.cn (Hongwen Deng)
Received Date: 2009-05-25
Accepted Date: 2009-11-09
Rev Recd Date: 2009-11-09

Available Online: 2009-12-21

Publish Date: 2009-12-20

Abstract

Abstract

Quantitative traits often underlie risk for complex diseases. Many studies collect multiple correlated quantitative phenotypes and perform univariate analyses on each of them respectively. However, this strategy may not be powerful and has limitations to detect pleiotropic genes that may underlie correlated quantitative traits. In addition, testing multiple traits individually will exacerbate perplexing problem of multiple testing. In this study, generalized estimating equation 2 (GEE2) is applied to association mapping of two correlated quantitative traits. We suppose that a quantitative trait locus is located in a chromosome region that exerts pleiotropic effects on multiple quantitative traits. In that region, multiple SNPs are genotyped. Genotypes of these SNPs and the two quantitative traits affected by a causal SNP were simulated under various parameter values: residual correlation coefficient between two traits, causal SNP heritability, minor allele frequency of the causal SNP, extent of linkage disequilibrium with the causal SNP, and the test sample size. By power analytical analyses, it is showed that the bivariate method is generally more powerful than the univariate method. This method is robust and yields false-positive rates close to the pre-set nominal significance level. Our real data analyses attested to the usefulness of the method.
- general estimating equation,
- bivariate,
- quantitative trait,
- linkage disequilibrium

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References(26)

References

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