{"title":"心脏性状的上位性。","authors":"Julian Stamp, Lorin Crawford","doi":"10.1038/s44161-024-00595-w","DOIUrl":null,"url":null,"abstract":"Finding phenotypic variance that results from gene interactions (epistasis) has been a longstanding challenge in human genetics. The combination of a new machine learning framework with functional genomics now provides evidence that cardiac hypertrophy is regulated by non-additive genetic interactions.","PeriodicalId":74245,"journal":{"name":"Nature cardiovascular research","volume":"4 6","pages":"655-656"},"PeriodicalIF":10.8000,"publicationDate":"2025-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Epistasis in cardiac traits\",\"authors\":\"Julian Stamp, Lorin Crawford\",\"doi\":\"10.1038/s44161-024-00595-w\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Finding phenotypic variance that results from gene interactions (epistasis) has been a longstanding challenge in human genetics. The combination of a new machine learning framework with functional genomics now provides evidence that cardiac hypertrophy is regulated by non-additive genetic interactions.\",\"PeriodicalId\":74245,\"journal\":{\"name\":\"Nature cardiovascular research\",\"volume\":\"4 6\",\"pages\":\"655-656\"},\"PeriodicalIF\":10.8000,\"publicationDate\":\"2025-06-05\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Nature cardiovascular research\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.nature.com/articles/s44161-024-00595-w\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"CARDIAC & CARDIOVASCULAR SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Nature cardiovascular research","FirstCategoryId":"1085","ListUrlMain":"https://www.nature.com/articles/s44161-024-00595-w","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CARDIAC & CARDIOVASCULAR SYSTEMS","Score":null,"Total":0}
Finding phenotypic variance that results from gene interactions (epistasis) has been a longstanding challenge in human genetics. The combination of a new machine learning framework with functional genomics now provides evidence that cardiac hypertrophy is regulated by non-additive genetic interactions.
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