Association studies for quantitative traits in structured populations
Association studies for quantitative traits in structured populations
复制标题
DOI：
10.1002/gepi.1045
复制
发表时间：
2002-01-01
影响因子：
2.1
通讯作者：
Roeder, K
中科院分区：
文献类型：
--
作者：
Bacanu, SA;Devlin, B;Roeder, K
疾病与遗传多态性之间的关联通常为我们寻找常见疾病的遗传成分提供关键信息。 Devlin 和 Roeder [1999：Biometrics 55：997-1004]引入了基因组控制，这是一种统计方法，克服了使用基于群体的样本进行关联测试的缺点，即由群体结构引起的虚假关联。本质上，基因组控制（GC）使用整个基因组中的标记来调整由于子结构导致的测试统计数据的任何膨胀。迄今为止，基因组控制（GC）已经开发用于二元性状和双或多等位基因标记。使用 GC 的关联测试仅限于单个基因。在本报告中，我们将 GC 推广到数量性状 (QT) 和多位点模型。通过统计分析和模拟，我们表明 GC 在 QT 模型群体子结构的合理设置中控制虚假关联，包括基因与基因相互作用。通过模拟，我们假设测试的 QT 基因座是因果关系且其特定遗传率为 2.5-5%，从而探索随机样本和选定样本的 GC 能力。我们发现 GC 与随机或选定样本相结合，在此设置中具有良好的能力，并且更复杂的模型会导致更小的 GC 校正。后者表明可以通过指定更复杂的遗传模型来实现更大的功效，但只有当这些模型基本上正确且先验指定时，这种观察才会成立。 (C) 2002 Wiley-Liss, Inc.
Association between disease and genetic polymorphisms often contributes critical information in our search for the genetic components of common diseases. Devlin and Roeder [1999: Biometrics 55:997-1004] introduced genomic control, a statistical method that overcomes a drawback to the use of population-based samples for tests of association, namely spurious associations induced by population structure. In essence, genomic control (GC) uses markers throughout the genome to adjust for any inflation in test statistics due to substructure. To date, genomic control (GC) has been developed for binary traits and bi- or multiallelic markers. Tests of association using GC have been limited to single genes. In this report, we generalize GC to quantitative traits (QT) and multilocus models. Using statistical analysis and simulations, we show that GC controls spurious associations in reasonable settings of population substructure for QT models, including gene-gene interaction. Through simulations, we explore GC power for both random and selected samples, assuming the QT locus tested is causal and its specific heritability is 2.5-5%. We find that GC, combined with either random or selected samples, has good power in this setting, and that more complex models induce smaller GC corrections. The latter suggests greater power can be achieved by specifying more complex genetic models, but this observation only follows when such models are largely correct and specified a priori. (C) 2002 Wiley-Liss, Inc.