Zhang Shuanglin, Zhu Xiaofeng, Zhao Hongyu
Department of Epidemiology and Public Health, Yale University School of Medicine, New Haven, Connecticut 06520-8034, USA.
Genet Epidemiol. 2003 Jan;24(1):44-56. doi: 10.1002/gepi.10196.
Although genetic association studies using unrelated individuals may be subject to bias caused by population stratification, alternative methods that are robust to population stratification such as family-based association designs may be less powerful. Recently, various statistical methods robust to population stratification were proposed for association studies, using unrelated individuals to identify associations between candidate markers and traits of interest (both qualitative and quantitative). Here, we propose a semiparametric test for association (SPTA). SPTA controls for population stratification through a set of genomic markers by first deriving a genetic background variable for each sampled individual through his/her genotypes at a series of independent markers, and then modeling the relationship between trait values, genotypic scores at the candidate marker, and genetic background variables through a semiparametric model. We assume that the exact form of relationship between the trait value and the genetic background variable is unknown and estimated through smoothing techniques. We evaluate the performance of SPTA through simulations both with discrete subpopulation models and with continuous admixture population models. The simulation results suggest that our procedure has a correct type I error rate in the presence of population stratification and is more powerful than statistical association tests for family-based association designs in all the cases considered. Moreover, SPTA is more powerful than the Quantitative Similarity-Based Association Test (QSAT) developed by us under continuous admixture populations, and the number of independent markers needed by SPTA to control for population stratification is substantially fewer than that required by QSAT.
尽管使用无亲缘关系个体的基因关联研究可能会受到群体分层导致的偏差影响，但诸如基于家系的关联设计等对群体分层具有稳健性的替代方法可能效力较低。最近，针对关联研究提出了各种对群体分层具有稳健性的统计方法，利用无亲缘关系个体来识别候选标记与感兴趣性状（包括定性和定量性状）之间的关联。在此，我们提出一种关联的半参数检验（SPTA）。SPTA通过一组基因组标记来控制群体分层，首先通过每个采样个体在一系列独立标记处的基因型推导出其遗传背景变量，然后通过半参数模型对性状值、候选标记处的基因型得分以及遗传背景变量之间的关系进行建模。我们假设性状值与遗传背景变量之间关系的精确形式未知，并通过平滑技术进行估计。我们通过离散亚群体模型和连续混合群体模型的模拟来评估SPTA的性能。模拟结果表明，我们的方法在存在群体分层的情况下具有正确的I型错误率，并且在所考虑的所有情况下都比基于家系的关联设计的统计关联检验更具效力。此外，在连续混合群体下，SPTA比我们开发的基于定量相似性的关联检验（QSAT）更具效力，并且SPTA控制群体分层所需的独立标记数量比QSAT所需的数量少得多。
