Department of Statistics, University of Connecticut, Storrs, CT 06269, USA.
Am J Hum Genet. 2013 May 2;92(5):744-59. doi: 10.1016/j.ajhg.2013.04.004.
There is increasing interest in the joint analysis of multiple phenotypes in genome-wide association studies (GWASs), especially for the analysis of multiple secondary phenotypes in case-control studies and in detecting pleiotropic effects. Multiple phenotypes often measure the same underlying trait. By taking advantage of similarity across phenotypes, one could potentially gain statistical power in association analysis. Because continuous phenotypes are likely to be measured on different scales, we propose a scaled marginal model for testing and estimating the common effect of single-nucleotide polymorphism (SNP) on multiple secondary phenotypes in case-control studies. This approach improves power in comparison to individual phenotype analysis and traditional multivariate analysis when phenotypes are positively correlated and measure an underlying trait in the same direction (after transformation) by borrowing strength across outcomes with a one degree of freedom (1-DF) test and jointly estimating outcome-specific scales along with the SNP and covariate effects. To account for case-control ascertainment bias for the analysis of multiple secondary phenotypes, we propose weighted estimating equations for fitting scaled marginal models. This weighted estimating equation approach is robust to departures from normality of continuous multiple phenotypes and the misspecification of within-individual correlation among multiple phenotypes. Statistical power improves when the within-individual correlation is correctly specified. We perform simulation studies to show the proposed 1-DF common effect test outperforms several alternative methods. We apply the proposed method to investigate SNP associations with smoking behavior measured with multiple secondary smoking phenotypes in a lung cancer case-control GWAS and identify several SNPs of biological interest.
人们对全基因组关联研究（GWAS）中多种表型的联合分析越来越感兴趣，特别是在病例对照研究中分析多种次要表型和检测多效性效应方面。多种表型通常测量相同的潜在特征。通过利用表型之间的相似性，人们有可能在关联分析中获得统计能力。由于连续表型可能在不同的尺度上进行测量，因此我们提出了一种缩放边际模型，用于检验和估计单核苷酸多态性（SNP）对病例对照研究中多种次要表型的共同效应。与个体表型分析和传统的多元分析相比，当表型呈正相关且以相同方向（经过转换后）测量潜在特征时，这种方法通过跨结果借用一个自由度（1-DF）检验的力量，并共同估计特定结果的比例，同时估计 SNP 和协变量效应，从而提高了功效。为了解决分析多种次要表型时的病例对照确定偏差问题，我们提出了用于拟合缩放边际模型的加权估计方程。这种加权估计方程方法对于连续多个表型的正态性偏离和多个表型之间个体内相关性的指定不当具有稳健性。当正确指定个体内相关性时，统计功效会提高。我们进行模拟研究表明，所提出的 1-DF 共同效应检验优于几种替代方法。我们应用所提出的方法来研究 SNP 与肺癌病例对照 GWAS 中用多种次要吸烟表型测量的吸烟行为之间的关联，并确定了几个具有生物学意义的 SNP。
