Department of Biostatistics, University of Washington, Seattle, WA 98195, USA.
Am J Hum Genet. 2010 Feb 12;86(2):172-84. doi: 10.1016/j.ajhg.2010.01.001. Epub 2010 Feb 4.
Genome-wide association studies are routinely conducted to identify genetic variants that influence complex disorders. It is well known that failure to properly account for population or pedigree structure can lead to spurious association as well as reduced power. We propose a method, ROADTRIPS, for case-control association testing in samples with partially or completely unknown population and pedigree structure. ROADTRIPS uses a covariance matrix estimated from genome-screen data to correct for unknown population and pedigree structure while maintaining high power by taking advantage of known pedigree information when it is available. ROADTRIPS can incorporate data on arbitrary combinations of related and unrelated individuals and is computationally feasible for the analysis of genetic studies with millions of markers. In simulations with related individuals and population structure, including admixture, we demonstrate that ROADTRIPS provides a substantial improvement over existing methods in terms of power and type 1 error. The ROADTRIPS method can be used across a variety of study designs, ranging from studies that have a combination of unrelated individuals and small pedigrees to studies of isolated founder populations with partially known or completely unknown pedigrees. We apply the method to analyze two data sets: a study of rheumatoid arthritis in small UK pedigrees, from Genetic Analysis Workshop 15, and data from the Collaborative Study of the Genetics of Alcoholism on alcohol dependence in a sample of moderate-size pedigrees of European descent, from Genetic Analysis Workshop 14. We detect genome-wide significant association, after Bonferroni correction, in both studies.
全基因组关联研究通常用于识别影响复杂疾病的遗传变异。众所周知，如果不能正确考虑人口或家系结构，可能会导致虚假关联以及降低效力。我们提出了一种方法 ROADTRIPS，用于在部分或完全未知人口和家系结构的样本中进行病例对照关联测试。ROADTRIPS 使用从全基因组筛查数据中估计的协方差矩阵来纠正未知的人口和家系结构，同时在可用时利用已知的家系信息来保持高功效。ROADTRIPS 可以合并任意组合的相关和无关个体的数据，并且对于分析具有数百万个标记的遗传研究来说，计算上是可行的。在包括混合在内的具有相关个体和人口结构的模拟中，我们证明 ROADTRIPS 在功效和第一类错误方面与现有方法相比有了很大的改进。ROADTRIPS 方法可以用于各种研究设计，从具有无关个体和小家族的研究到具有部分已知或完全未知家族的孤立创始人群体的研究。我们将该方法应用于分析两个数据集：一个是在英国小家族中进行的类风湿关节炎研究，来自遗传分析研讨会 15，另一个是来自酒精依赖遗传分析合作研究的数据集，在具有欧洲血统的中等规模家族中进行，来自遗传分析研讨会 14。在两项研究中，我们都检测到了全基因组显著关联，经过 Bonferroni 校正。
