{"title":"GeneRiskCalc: a web-based tool for genetic risk association analysis in case-control studies.","authors":"Amrit Sudershan, Kuljeet Singh, Parvinder Kumar","doi":"10.1186/s12859-025-06207-z","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Genetic association studies play a pivotal role in identifying disease-associated variants, but researchers face challenges in performing essential calculations like Hardy-Weinberg equilibrium testing, odds ratios, and confidence intervals due to reliance on manual methods or multiple software tools. We aimed to develop GeneRiskCalc, an integrated web-based platform that simplifies genetic association analysis by automating Hardy-Weinberg equilibrium assessment, odds ratios with confidence interval calculation, and visual data presentation in case-control studies. Using an HTML/CSS/JavaScript framework, we developed online software with three core functionalities: (1) automated HWE evaluation, (2) odds ratio with 95% confidence interval computation with statistical validation, and (3) dynamic Forest Plot generation for data visualization. The tool was designed with an intuitive interface to minimize prerequisite statistical expertise.</p><p><strong>Results: </strong>The tool, named the Genetic Risk Association Calculator (GeneRiskCalc), demonstrated high computational accuracy in HWE testing (χ<sup>2</sup> validation) and association metrics (odds ratio and confidence interval). The results were cross-validated against established statistical methods, confirming their reliability. Furthermore, the integrated Forest Plotter enabled immediate visualization of effect sizes across multiple genetic models, facilitating a comprehensive interpretation of genetic associations.</p><p><strong>Conclusion: </strong>By integrating essential analytical steps into a single platform, the GeneRiskCalc, streamlines genetic epidemiology workflows, addressing key challenges in data analysis. Its user-friendly interface enhances accessibility, promotes reproducibility, and accelerates research in genetic association studies. The tool is freely available at GeneRiskCalc ( https://sites.google.com/view/GeneRiskCalc/home?authuser=0 ).</p>","PeriodicalId":8958,"journal":{"name":"BMC Bioinformatics","volume":"26 1","pages":"213"},"PeriodicalIF":3.3000,"publicationDate":"2025-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12363000/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"BMC Bioinformatics","FirstCategoryId":"99","ListUrlMain":"https://doi.org/10.1186/s12859-025-06207-z","RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"BIOCHEMICAL RESEARCH METHODS","Score":null,"Total":0}
引用次数: 0
Abstract
Background: Genetic association studies play a pivotal role in identifying disease-associated variants, but researchers face challenges in performing essential calculations like Hardy-Weinberg equilibrium testing, odds ratios, and confidence intervals due to reliance on manual methods or multiple software tools. We aimed to develop GeneRiskCalc, an integrated web-based platform that simplifies genetic association analysis by automating Hardy-Weinberg equilibrium assessment, odds ratios with confidence interval calculation, and visual data presentation in case-control studies. Using an HTML/CSS/JavaScript framework, we developed online software with three core functionalities: (1) automated HWE evaluation, (2) odds ratio with 95% confidence interval computation with statistical validation, and (3) dynamic Forest Plot generation for data visualization. The tool was designed with an intuitive interface to minimize prerequisite statistical expertise.
Results: The tool, named the Genetic Risk Association Calculator (GeneRiskCalc), demonstrated high computational accuracy in HWE testing (χ2 validation) and association metrics (odds ratio and confidence interval). The results were cross-validated against established statistical methods, confirming their reliability. Furthermore, the integrated Forest Plotter enabled immediate visualization of effect sizes across multiple genetic models, facilitating a comprehensive interpretation of genetic associations.
Conclusion: By integrating essential analytical steps into a single platform, the GeneRiskCalc, streamlines genetic epidemiology workflows, addressing key challenges in data analysis. Its user-friendly interface enhances accessibility, promotes reproducibility, and accelerates research in genetic association studies. The tool is freely available at GeneRiskCalc ( https://sites.google.com/view/GeneRiskCalc/home?authuser=0 ).
期刊介绍：
BMC Bioinformatics is an open access, peer-reviewed journal that considers articles on all aspects of the development, testing and novel application of computational and statistical methods for the modeling and analysis of all kinds of biological data, as well as other areas of computational biology.
BMC Bioinformatics is part of the BMC series which publishes subject-specific journals focused on the needs of individual research communities across all areas of biology and medicine. We offer an efficient, fair and friendly peer review service, and are committed to publishing all sound science, provided that there is some advance in knowledge presented by the work.
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