Family history aggregation unit-based tests to detect rare genetic variant associations with application to the Framingham Heart Study
Family history aggregation unit-based tests to detect rare genetic variant associations with application to the Framingham Heart Study
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DOI：
10.1016/j.ajhg.2022.03.001
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发表时间：
2022-04-07
影响因子：
9.8
通讯作者：
Dupuis, Josee
中科院分区：
文献类型：
--
作者：
Wang, Yanbing;Chen, Han;Dupuis, Josee
标准遗传研究的一个挑战是保持良好的检测相关性的能力，特别是对于低流行疾病和罕见变异。在评估平衡研究设计中变量之间的关联时，传统方法是最有效的。如果不考虑家庭相关性和不平衡的病例对照比，这些分析可能导致夸大的I型误差。提高统计能力的一个经济有效的解决方案是利用现有的家族史（FH），其中包含有关疾病遗传性的宝贵信息。在这里，我们开发了解决上述I型错误问题的方法，同时通过结合来自FH的附加信息，提供了分析罕见变体聚合的最佳能力。利用这些利用FH并考虑相关性和不平衡设计的方法，我们成功地通过使用Framingham心脏研究的外显子组芯片数据检测出与阿尔茨海默病、痴呆和2型糖尿病相关的基因。
A challenge in standard genetic studies is maintaining good power to detect associations, especially for low prevalent diseases and rare variants. The traditional methods are most powerful when evaluating the association between variants in balanced study designs. Without accounting for family correlation and unbalanced case-control ratio, these analyses could result in inflated type I error. One cost-effective solution to increase statistical power is exploitation of available family history (FH) that contains valuable information about disease heritability. Here, we develop methods to address the aforementioned type I error issues while providing optimal power to analyze aggregates of rare variants by incorporating additional information from FH. With enhanced power in these methods exploiting FH and accounting for relatedness and unbalanced designs, we successfully detect genes with suggestive associations with Alzheimer disease, dementia, and type 2 diabetes by using the exome chip data from the Framingham Heart Study.