Abstract
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.
Highlights
Case-control genetic association studies using unrelated individuals to find genetic variations associated with a particular disease are popular and useful
Simulation studies We performed simulation studies to investigate the performance of the proposed Extended likelihood ratio test (eLRT) and Extended mHWP (emHWP) approaches, and compared the proposed approaches to the existing approaches for Hardy-Weinberg proportion (HWP) testing: the likelihood ratio test (LRT) approach proposed by Li and Li [2] and the mixture HWP (mHWP) exact test proposed by Wang and Shete [3]
Four existing approaches for testing HWP and the two proposed approaches were studied: LRT_t and mHWP_t are the LRT approach [2] and the mHWP exact test [3], respectively, which use the presence and absence of the secondary phenotype as cases and controls; LRT_d and mHWP_d use the presence and absence of the primary disease as cases and controls; the eLRT approach proposed in this article is an extension of the LRT approach proposed by Li and Li [2]; and the emHWP exact test proposed in this article is an extension of the mHWP exact test proposed by Wang and Shete [3]
Summary
Case-control genetic association studies using unrelated individuals to find genetic variations associated with a particular disease are popular and useful. In a case-control study design, cases are those subjects with the primary disease (e.g., lung cancer, diabetes, breast cancer) and controls are those free of the primary disease. In addition to studies of primary diseases, there has been some interest in studying genetic variants associated with secondary phenotypes. Many case-control studies of primary diseases are frequency-matched on the secondary phenotypes. Frequency-matching on known risk confounders is an important and commonly used study design in case-control studies [1] to reduce the effects of confounding factors. Some lung cancer studies are frequency-matched on smoking behavior, as smoking is a known risk confounder for the association between lung cancer and other risk factors
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