Department of Epidemiology, The University of Texas MD Anderson Cancer Center, Houston, Texas, United States of America.
PLoS One. 2011;6(11):e27642. doi: 10.1371/journal.pone.0027642. Epub 2011 Nov 14.
In case-control genetic association studies, cases are subjects with the disease and controls are subjects without the disease. At the time of case-control data collection, information about secondary phenotypes is also collected. In addition to studies of primary diseases, there has been some interest in studying genetic variants associated with secondary phenotypes. In genetic association studies, the deviation from Hardy-Weinberg proportion (HWP) of each genetic marker is assessed as an initial quality check to identify questionable genotypes. Generally, HWP tests are performed based on the controls for the primary disease or secondary phenotype. However, when the disease or phenotype of interest is common, the controls do not represent the general population. Therefore, using only controls for testing HWP can result in a highly inflated type I error rate for the disease- and/or phenotype-associated variants. Recently, two approaches, the likelihood ratio test (LRT) approach and the mixture HWP (mHWP) exact test were proposed for testing HWP in samples from case-control studies. Here, we show that these two approaches result in inflated type I error rates and could lead to the removal from further analysis of potential causal genetic variants associated with the primary disease and/or secondary phenotype when the study of primary disease is frequency-matched on the secondary phenotype. Therefore, we proposed alternative approaches, which extend the LRT and mHWP approaches, for assessing HWP that account for frequency matching. The goal was to maintain more (possible causative) single-nucleotide polymorphisms in the sample for further analysis. Our simulation results showed that both extended approaches could control type I error probabilities. We also applied the proposed approaches to test HWP for SNPs from a genome-wide association study of lung cancer that was frequency-matched on smoking status and found that the proposed approaches can keep more genetic variants for association studies.
在病例对照遗传关联研究中，病例是患有该疾病的受试者，对照是未患该疾病的受试者。在病例对照数据收集时，还收集了次要表型的信息。除了对主要疾病的研究外，人们还对与次要表型相关的遗传变异进行了一些研究。在遗传关联研究中，评估每个遗传标记偏离 Hardy-Weinberg 比例（HWP）作为初始质量检查，以识别可疑基因型。通常，基于主要疾病或次要表型的对照进行 HWP 测试。然而，当感兴趣的疾病或表型很常见时，对照并不能代表一般人群。因此，仅使用对照进行 HWP 测试可能会导致与疾病和/或表型相关的变异的Ⅰ型错误率高度膨胀。最近，提出了两种方法，似然比检验（LRT）方法和混合 HWP（mHWP）精确检验方法，用于检测病例对照研究样本中的 HWP。在这里，我们表明这两种方法会导致Ⅰ型错误率膨胀，并可能导致与主要疾病和/或次要表型相关的潜在因果遗传变异从进一步分析中删除，当主要疾病的研究与次要表型的频率匹配时。因此，我们提出了替代方法，扩展了 LRT 和 mHWP 方法，用于评估考虑到频率匹配的 HWP。目标是在样本中保留更多（可能是因果关系的）单核苷酸多态性，以便进一步分析。我们的模拟结果表明，这两种扩展方法都可以控制Ⅰ型错误概率。我们还将提出的方法应用于对肺癌全基因组关联研究中 SNPs 的 HWP 测试，该研究基于吸烟状况进行了频率匹配，发现提出的方法可以为关联研究保留更多的遗传变异。
