Liu Nianjun, Zhao Hongyu, Patki Amit, Limdi Nita A, Allison David B
Department of Biostatistics, University of Alabama at Birmingham, Birmingham, AL 35294.
Stat Interface. 2011;4(3):317-326. doi: 10.4310/sii.2011.v4.n3.a6.
In genetic studies, associations between genotypes and phenotypes may be confounded by unrecognized population structure and/or admixture. Studies have shown that even in European populations, which are thought to be relatively homogeneous, population stratification exists and can affect the validity of association studies. A number of methods have been proposed to address this issue in recent years. Among them, the mixed-model based approach and the principal component-based approach have several advantages over other methods. However, these approaches have not been thoroughly evaluated on large human datasets. The objectives of this study are to (1) evaluate and compare the performance of the mixed-model approach and the principal component-based approach for genetic association mapping using human data consisting of unrelated individuals, and (2) understand the relationship between these two approaches. To achieve these goals, we simulate datasets based on the HapMap data under various scenarios. Our results indicate that the mixed-model approach performs well in controlling for population structure/admixture. It has similar performance as that based on principal component analysis. However, the approach combining mixed-model and principal component analysis does not perform as well as either method itself.
在基因研究中，基因型与表型之间的关联可能会因未被识别的群体结构和/或混合情况而产生混淆。研究表明，即使在被认为相对同质化的欧洲人群中，群体分层现象也存在，并且会影响关联研究的有效性。近年来，人们提出了许多方法来解决这个问题。其中，基于混合模型的方法和基于主成分的方法比其他方法具有若干优势。然而，这些方法尚未在大型人类数据集上得到全面评估。本研究的目的是：（1）使用由无关个体组成的人类数据，评估和比较混合模型方法和基于主成分的方法在基因关联定位中的性能；（2）了解这两种方法之间的关系。为实现这些目标，我们在各种情况下基于HapMap数据模拟数据集。我们的结果表明，混合模型方法在控制群体结构/混合情况方面表现良好。它与基于主成分分析的方法具有相似的性能。然而，将混合模型和主成分分析相结合的方法表现不如这两种方法单独使用时好。
