Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, United States of America.
Genet Epidemiol. 2013 Dec;37(8):759-67. doi: 10.1002/gepi.21759. Epub 2013 Nov 5.
Genetic association studies often collect data on multiple traits that are correlated. Discovery of genetic variants influencing multiple traits can lead to better understanding of the etiology of complex human diseases. Conventional univariate association tests may miss variants that have weak or moderate effects on individual traits. We propose several multivariate test statistics to complement univariate tests. Our framework covers both studies of unrelated individuals and family studies and allows any type/mixture of traits. We relate the marginal distributions of multivariate traits to genetic variants and covariates through generalized linear models without modeling the dependence among the traits or family members. We construct score-type statistics, which are computationally fast and numerically stable even in the presence of covariates and which can be combined efficiently across studies with different designs and arbitrary patterns of missing data. We compare the power of the test statistics both theoretically and empirically. We provide a strategy to determine genome-wide significance that properly accounts for the linkage disequilibrium (LD) of genetic variants. The application of the new methods to the meta-analysis of five major cardiovascular cohort studies identifies a new locus (HSCB) that is pleiotropic for the four traits analyzed.
遗传关联研究通常会收集多个相关性状的数据。发现影响多个性状的遗传变异可以帮助更好地理解复杂人类疾病的病因。传统的单变量关联检验可能会遗漏对单个性状具有弱或中等影响的变异。我们提出了几种多元检验统计量来补充单变量检验。我们的框架涵盖了无关个体研究和家族研究，并允许任何类型/混合的性状。我们通过广义线性模型将多元性状的边缘分布与遗传变异和协变量联系起来，而无需对性状或家庭成员之间的相关性进行建模。我们构建了得分型统计量，即使在存在协变量的情况下，这些统计量在计算上也很快，数值上也很稳定，并且可以有效地在具有不同设计和任意缺失数据模式的研究中进行组合。我们从理论和经验上比较了检验统计量的功效。我们提供了一种确定全基因组显著性的策略，该策略可以正确考虑遗传变异的连锁不平衡 (LD)。新方法在对五个主要心血管队列研究的荟萃分析中的应用确定了一个新的位点 (HSCB)，该位点对分析的四个性状具有多效性。
