Fardo David W, Druen Anthony R, Liu Jinze, Mirea Lucia, Infante-Rivard Claire, Breheny Patrick
Department of Biostatistics, University of Kentucky College of Public Health, 121 Washington Avenue, Lexington, KY 40536, USA.
BMC Proc. 2011 Nov 29;5 Suppl 9(Suppl 9):S28. doi: 10.1186/1753-6561-5-S9-S28.
We examine the performance of various methods for combining family- and population-based genetic association data. Several approaches have been proposed for situations in which information is collected from both a subset of unrelated subjects and a subset of family members. Analyzing these samples separately is known to be inefficient, and it is important to determine the scenarios for which differing methods perform well. Others have investigated this question; however, no extensive simulations have been conducted, nor have these methods been applied to mini-exome-style data such as that provided by Genetic Analysis Workshop 17. We quantify the empirical power and false-positive rates for three existing methods applied to the Genetic Analysis Workshop 17 mini-exome data and compare relative performance. We use knowledge of the underlying data simulation model to make these assessments.
我们研究了用于合并基于家系和群体的基因关联数据的各种方法的性能。对于从一部分无关个体和一部分家庭成员中收集信息的情况，已经提出了几种方法。已知分别分析这些样本效率低下，确定不同方法在哪些情况下表现良好很重要。其他人已经研究过这个问题；然而，尚未进行广泛的模拟，这些方法也未应用于诸如遗传分析研讨会17提供的微型外显子数据类型。我们对应用于遗传分析研讨会17微型外显子数据的三种现有方法的经验效能和假阳性率进行了量化，并比较了相对性能。我们利用基础数据模拟模型的知识来进行这些评估。
