{"title":"Rethinking GWAS: how lessons from genetic screens and artificial intelligence could reveal biological mechanisms.","authors":"Dennis J Hazelett","doi":"10.1093/bioinformatics/btaf153","DOIUrl":null,"url":null,"abstract":"<p><strong>Motivation: </strong>Modern single-cell omics data are key to unraveling the complex mechanisms underlying risk for complex diseases revealed by genome-wide association studies (GWAS). Phenotypic screens in model organisms have several important parallels to GWAS which I explore in this essay.</p><p><strong>Results: </strong>I provide the historical context of such screens, comparing and contrasting similarities to association studies, and how these screens in model organisms can teach us what to look for. Then I consider how the results of GWAS might be exhaustively interrogated to interpret the biological mechanisms underpinning disease processes. Finally, I propose a general framework for tackling this problem computationally, and explore the data, mechanisms, and technology (both existing and yet to be invented) that are necessary to complete the task.</p><p><strong>Availability and implementation: </strong>There are no data or code associated with this article.</p><p><strong>Supplementary information: </strong>Not applicable.</p>","PeriodicalId":93899,"journal":{"name":"Bioinformatics (Oxford, England)","volume":" ","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2025-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Bioinformatics (Oxford, England)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1093/bioinformatics/btaf153","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Rethinking GWAS: how lessons from genetic screens and artificial intelligence could reveal biological mechanisms.
Motivation: Modern single-cell omics data are key to unraveling the complex mechanisms underlying risk for complex diseases revealed by genome-wide association studies (GWAS). Phenotypic screens in model organisms have several important parallels to GWAS which I explore in this essay.
Results: I provide the historical context of such screens, comparing and contrasting similarities to association studies, and how these screens in model organisms can teach us what to look for. Then I consider how the results of GWAS might be exhaustively interrogated to interpret the biological mechanisms underpinning disease processes. Finally, I propose a general framework for tackling this problem computationally, and explore the data, mechanisms, and technology (both existing and yet to be invented) that are necessary to complete the task.
Availability and implementation: There are no data or code associated with this article.
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