A unified mixed-model method for association mapping that accounts for multiple levels of relatedness
A unified mixed-model method for association mapping that accounts for multiple levels of relatedness
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DOI：
10.1038/ng1702
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发表时间：
2006-02-01
期刊：
影响因子：
30.8
通讯作者：
Buckler, ES
中科院分区：
文献类型：
--
作者：
Yu, JM;Pressoir, G;Buckler, ES
由于种群结构可能导致虚假的关联，这限制了关联研究在人类和植物遗传学中的使用。然而，如果功能关联的真实信号能够从种群结构(1，2)产生的大量错误信号中分离出来，那么关联映射就有很大的希望。我们开发了一种统一的混合模型方法来同时解释随机遗传标记检测到的多个水平的相关性。我们将这种新方法应用于两个样本：一个是14个人类家系的家系样本，用于定量基因表达分析；另一个是277个具有复杂家庭关系和群体结构的不同玉米自交系的样本，用于数量性状分析。与其他方法相比，我们的方法证明了对类型I和类型II误码率的改进控制。由于这种新方法跨越了基于家族的关联样本和结构化关联样本之间的界限，它为现有的关联映射方法提供了强有力的补充。
As population structure can result in spurious associations, it has constrained the use of association studies in human and plant genetics. Association mapping, however, holds great promise if true signals of functional association can be separated from the vast number of false signals generated by population structure(1,2). We have developed a unified mixed-model approach to account for multiple levels of relatedness simultaneously as detected by random genetic markers. We applied this new approach to two samples: a family-based sample of 14 human families, for quantitative gene expression dissection, and a sample of 277 diverse maize inbred lines with complex familial relationships and population structure, for quantitative trait dissection. Our method demonstrates improved control of both type I and type II error rates over other methods. As this new method crosses the boundary between family-based and structured association samples, it provides a powerful complement to currently available methods for association mapping.