Gene-based Hardy–Weinberg equilibrium test using genotype count data identifies novel cancer-related genes
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Background
An alternative approach to investigate associations between genetic variants and disease is to examine deviations from the Hardy–Weinberg equilibrium (HWE) in genotype frequencies within a case population, instead of case-control association analysis. The HWE analysis distinctively requires disease cases without the need for controls and demonstrates a notable ability in mapping recessive variants. Allelic heterogeneity is a common phenomenon in diseases. While gene-based case-control association analysis successfully incorporates this heterogeneity, there are no such approaches for HWE analysis. Therefore, we proposed a gene-based HWE test (gene-HWT) by aggregating single-nucleotide polymorphism (SNP)-level HWE test statistics in a gene to address allelic heterogeneity.
Results
This method used only genotype count data and publicly available linkage disequilibrium information and has a very low computational cost. Extensive simulations demonstrated that gene-HWT effectively controls the type I error at a low significance level and outperforms SNP-level HWE test in power when there are multiple causal variants within a gene. Using gene-HWT, we analyzed genotype count data from genome-wide association study for six types of cancers in Japanese individuals and found that most of the genes detected are associated with cancers. In addition, we identified novel genes ( AGBL3 and PSORS1C1 ), novel variants in CTSO known to be associated with breast cancer prognosis and drug sensitivity, and novel genes as germline factors, which have associations in gene expression or methylation status with cancers in the combined analysis of six types of cancers.
Conclusions
These findings indicate the potential of gene-HWT to elucidate the genetic basis of complex diseases, including cancer.
