Bivariate association analysis for quantitative traits using generalized estimation equation  

作　　者：Fang Yang Zihui Tang Hongwen Deng 

机构地区：[1]Laboratory of Molecular and Statistical Genetics, College of Life Sciences, Hunan Normal University, Changsha 410081, China [2]Center of Systematic Biomedical Research, Shanghai University of Science and Technology, Shanghai 200093, China [3]Departments of Orthopedic Surgery and Basic Medical Sciences, University of Missouri-Kansas City, Kansas City, MO 64108, USA

出　　处：《Journal of Genetics and Genomics》2009年第12期733-743,共11页遗传学报（英文版）

基　　金：supported by grants from the Natural Science Foundation of China (No.30600364,30470534,and 30230210);the NSFC-Canadian Institutes of Health Research (CIHR) Joint Health Research Initia-tive Proposal (No.30811120436);the NSFC/RGC Joint Research Scheme (No.30731160618);Shanghai Leading Academic Discipline Project (No.S30501);supported by grants from NIH (No.P50AR055081,R01AG026564,R01AR050496,and R01AR057049);the Dickson/Missouri endowment

摘　　要：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 plei- otropic 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 ana- lytical 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.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 plei- otropic 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 ana- lytical 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.

关 键 词：general estimating equation BIVARIATE quantitative trait linkage disequilibrium 

分 类 号：S511.03[农业科学—作物学] TP311.13[自动化与计算机技术—计算机软件与理论]