Department of Statistics, University of Chicago, Chicago, IL 60637, USA.
Am J Hum Genet. 2013 May 2;92(5):652-66. doi: 10.1016/j.ajhg.2013.03.014.
Genetic association studies often sample individuals with known familial relationships in addition to unrelated individuals, and it is common for some individuals to have missing data (phenotypes, genotypes, or covariates). When some individuals in a sample are related, power can be gained by incorporating all individuals in the analysis, including individuals with partially missing data, while properly accounting for the dependence among them. We propose MASTOR, a mixed-model, retrospective score test for genetic association with a quantitative trait. MASTOR achieves high power in samples that contain related individuals by making full use of the relationship information to incorporate partially missing data in the analysis while correcting for dependence. Individuals with available phenotype and covariate information who are not genotyped but have genotyped relatives in the sample can still contribute to the association analysis because of the dependence among genotypes. Similarly, individuals who are genotyped but are missing covariate or phenotype information can contribute to the analysis. MASTOR is valid even when the phenotype model is misspecified and with either random or phenotype-based ascertainment. In simulations, we demonstrate the correct type 1 error of MASTOR, the increase in power that comes from making full use of the relationship information, the robustness to misspecification of the phenotype model, and the improvement in power that comes from modeling the heritability. We show that MASTOR is computationally feasible and practical in genome-wide association studies. We apply MASTOR to data on high-density lipoprotein cholesterol from the Framingham Heart study.
遗传关联研究通常会在采样时纳入具有已知家族关系的个体以及无关个体，并且一些个体的数据（表型、基因型或协变量）缺失是很常见的。当样本中的一些个体具有亲缘关系时，可以通过在分析中纳入所有个体（包括部分数据缺失的个体），同时适当考虑它们之间的相关性，从而获得更大的统计效力。我们提出了 MASTOR，这是一种用于与数量性状进行遗传关联的混合模型、回顾性评分检验方法。MASTOR 通过充分利用关系信息，在分析中纳入部分缺失数据，并纠正依赖性，从而在包含相关个体的样本中实现了高统计效力。那些具有可用表型和协变量信息但未进行基因分型但在样本中有基因分型亲属的个体，由于基因型之间的相关性，仍然可以为关联分析做出贡献。同样，那些进行了基因分型但缺失协变量或表型信息的个体也可以为分析做出贡献。即使表型模型存在一定的偏差，或者采用随机或基于表型的选择方法，MASTOR 仍然是有效的。在模拟中，我们验证了 MASTOR 的正确的Ⅰ型错误率、充分利用关系信息所带来的统计效力提高、表型模型的偏差稳健性以及通过建模遗传率所带来的统计效力提高。我们还表明，MASTOR 在全基因组关联研究中具有计算可行性和实用性。我们将 MASTOR 应用于弗雷明汉心脏研究中的高密度脂蛋白胆固醇数据。
