ROADTRIPS: Case-Control Association Testing with Partially or Completely Unknown Population and Pedigree Structure
ROADTRIPS: Case-Control Association Testing with Partially or Completely Unknown Population and Pedigree Structure
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DOI：
10.1016/j.ajhg.2010.01.001
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发表时间：
2010-02-12
影响因子：
9.8
通讯作者：
McPeek, Mary Sara
中科院分区：
文献类型：
--
作者：
Thornton, Timothy;McPeek, Mary Sara
常规进行全基因组关联研究，以确定影响复杂疾病的遗传变异。众所周知，不能正确地解释种群或谱系结构会导致虚假的关联以及权力的降低。我们提出了一种方法，ROADTRIPS，在部分或完全未知群体和谱系结构的样本中进行病例对照关联检验。ROADTRIPS使用从基因组筛选数据估计的协方差矩阵来纠正未知的种群和谱系结构，同时通过利用已知的谱系信息（当可用时）保持高功率。ROADTRIPS可以结合有亲缘关系和无亲缘关系个体的任意组合数据，并且在计算上可行，可用于分析具有数百万个标记的遗传研究。在具有相关个体和种群结构（包括外加剂）的模拟中，我们证明了ROADTRIPS在功率和1型误差方面比现有方法有了实质性的改进。ROADTRIPS方法可用于多种研究设计，包括不相关个体和小谱系的组合研究，以及谱系部分已知或完全未知的孤立创始人群体的研究。我们应用该方法分析了两个数据集：来自遗传分析研讨会15的小型英国家系类风湿性关节炎的研究，以及来自遗传分析研讨会14的中等规模欧洲血统家系酒精依赖的酒精中毒遗传学合作研究的数据。在两项研究中，我们在Bonferroni校正后检测到全基因组显著关联。
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.