Schaid Daniel J, Sinnwell Jason P, Jenkins Gregory D
Division of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic, Rochester, MN 55905, USA.
Hum Hered. 2012;74(2):71-82. doi: 10.1159/000345846. Epub 2013 Jan 11.
BACKGROUND/AIMS: Tests for whether observed genotype proportions fit Hardy Weinberg Equilibrium (HWE) are widely used in population genetics analyses, as well as to evaluate quality of genotype data. To date, all methods testing for HWE require subjects to be classified into discrete categories, yet it is becoming clear that the distribution of allele frequencies tends to be smooth over geographic regions.
To evaluate the HWE assumption, we develop new approaches to model allele frequencies as functions of covariates, and use these models to test whether there is residual correlation between the two alleles of subjects; lack of residual correlation supports the null hypothesis of HWE, but conditional on how the covariates influence the allele frequencies.
By simulations, we illustrate that a simple statistical test of residual correlation of alleles adequately controls the type I error rate, while maintaining power that is comparable to standard tests for HWE.
Our approach can be implemented in standard software, enabling more flexible and powerful ways to evaluate the association of covariates with allele frequencies and whether these associations 'explain' departures from HWE when the covariates are ignored, opening new strategies to evaluate the quality of genotype data generated by next-generation sequencing assays.
背景/目的:检验观察到的基因型比例是否符合哈迪-温伯格平衡(HWE)的测试在群体遗传学分析以及评估基因型数据质量方面被广泛应用。迄今为止,所有检验HWE的方法都要求将研究对象分类为离散类别,但越来越明显的是,等位基因频率的分布在地理区域上往往是平滑的。
为了评估HWE假设,我们开发了新的方法,将等位基因频率建模为协变量的函数,并使用这些模型来检验研究对象的两个等位基因之间是否存在残余相关性;不存在残余相关性支持HWE的零假设,但这取决于协变量如何影响等位基因频率。
通过模拟,我们表明对等位基因残余相关性的简单统计检验能够充分控制I型错误率,同时保持与HWE标准检验相当的检验效能。
我们的方法可以在标准软件中实现,从而能够以更灵活、更强大的方式评估协变量与等位基因频率的关联,以及当忽略协变量时这些关联是否“解释”了偏离HWE的情况,为评估下一代测序检测产生的基因型数据质量开辟了新策略。