{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["26(1)"],"submitter":["Nishino J"],"pubmed_abstract":["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 requires disease cases 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 a genome-wide association study of six cancer types in Japanese individuals and suggest DGKE and ANO3 as potential germline factors in colorectal cancer. Furthermore, FSTL4 was suggested through a combined analysis across the six cancer types, with particularly notable associations observed in colorectal and prostate cancers.Conclusions
These findings indicate the potential of gene-HWT to elucidate the genetic basis of complex diseases, including cancer."],"journal":["BMC genomics"],"pagination":["124"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC11809088"],"repository":["biostudies-literature"],"pubmed_title":["Gene-based Hardy-Weinberg equilibrium test using genotype count data: application to six types of cancers."],"pmcid":["PMC11809088"],"pubmed_authors":["Nishino J","Kato M","Miya F"],"additional_accession":[]},"is_claimable":false,"name":"Gene-based Hardy-Weinberg equilibrium test using genotype count data: application to six types of cancers.","description":"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 requires disease cases 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 a genome-wide association study of six cancer types in Japanese individuals and suggest DGKE and ANO3 as potential germline factors in colorectal cancer. Furthermore, FSTL4 was suggested through a combined analysis across the six cancer types, with particularly notable associations observed in colorectal and prostate cancers.Conclusions
These findings indicate the potential of gene-HWT to elucidate the genetic basis of complex diseases, including cancer.","dates":{"release":"2025-01-01T00:00:00Z","publication":"2025 Feb","modification":"2025-04-04T02:27:31.673Z","creation":"2025-04-04T02:27:31.673Z"},"accession":"S-EPMC11809088","cross_references":{"pubmed":["39930364"],"doi":["10.1186/s12864-025-11321-6"]}}