Hunan Normal University, Changsha, China.
J Genet Genomics. 2009 Dec;36(12):733-43. doi: 10.1016/S1673-8527(08)60166-6.
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.
数量性状通常是复杂疾病的风险基础。许多研究收集多个相关的定量表型，并分别对每个表型进行单变量分析。然而，这种策略可能没有足够的效力，并且有限制检测可能导致相关定量性状的多效基因的能力。此外，单独测试多个性状会加剧多重测试的复杂问题。在这项研究中，广义估计方程 2（GEE2）被应用于两个相关定量性状的关联映射。我们假设一个数量性状基因座位于一个染色体区域，该区域对多个定量性状具有多效性影响。在该区域中，对多个 SNP 进行基因分型。在各种参数值下模拟这些 SNP 和受因果 SNP 影响的两个定量性状的基因型：两个性状之间的残差相关系数、因果 SNP 的遗传力、因果 SNP 的次要等位基因频率、与因果 SNP 的连锁不平衡程度以及检验样本量。通过功效分析表明，双变量方法通常比单变量方法更有效。该方法具有鲁棒性，并产生接近预设名义显著性水平的假阳性率。我们的真实数据分析证明了该方法的有用性。
