{"title":"通过多基因风险评分预测年龄相关特征的陷阱","authors":"Valentina Escott-Price, Karl Michael Schmidt","doi":"10.1111/ahg.12520","DOIUrl":null,"url":null,"abstract":"<p>Polygenic risk scores (PRS) are a method increasingly used to capture the combined effect of genome-wide significant variants and those which individually do not show genome-wide significant association but are likely to contribute to the risk of developing diseases. However, their practical use incurs complications and inconsistencies that so far limit their clinical applicability. The aims of the present review are to discuss the PRS for age-related diseases and to highlight pitfalls and limitations of PRS prediction accuracy due to ageing and mortality effects. We argue that the PRS is widely used but the individual's PRS values differ substantially depending on the number of genetic variants included, the discovery GWAS and the method employed to generate them. Moreover, for neurodegenerative disorders, although an individual's genetics do not change with age, the actual score depends on the age of the sample used in the discovery GWAS and is likely to reflect the individual's disease risk at this particular age. Improvement of PRS prediction accuracy for neurodegenerative disorders will come from two sides, both the precision of clinical diagnoses, and a careful attention to the age distribution in the underlying samples and validation of the prediction in longitudinal studies.</p>","PeriodicalId":8085,"journal":{"name":"Annals of Human Genetics","volume":"87 5","pages":"203-209"},"PeriodicalIF":1.0000,"publicationDate":"2023-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/ahg.12520","citationCount":"2","resultStr":"{\"title\":\"Pitfalls of predicting age-related traits by polygenic risk scores\",\"authors\":\"Valentina Escott-Price, Karl Michael Schmidt\",\"doi\":\"10.1111/ahg.12520\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>Polygenic risk scores (PRS) are a method increasingly used to capture the combined effect of genome-wide significant variants and those which individually do not show genome-wide significant association but are likely to contribute to the risk of developing diseases. However, their practical use incurs complications and inconsistencies that so far limit their clinical applicability. The aims of the present review are to discuss the PRS for age-related diseases and to highlight pitfalls and limitations of PRS prediction accuracy due to ageing and mortality effects. We argue that the PRS is widely used but the individual's PRS values differ substantially depending on the number of genetic variants included, the discovery GWAS and the method employed to generate them. Moreover, for neurodegenerative disorders, although an individual's genetics do not change with age, the actual score depends on the age of the sample used in the discovery GWAS and is likely to reflect the individual's disease risk at this particular age. Improvement of PRS prediction accuracy for neurodegenerative disorders will come from two sides, both the precision of clinical diagnoses, and a careful attention to the age distribution in the underlying samples and validation of the prediction in longitudinal studies.</p>\",\"PeriodicalId\":8085,\"journal\":{\"name\":\"Annals of Human Genetics\",\"volume\":\"87 5\",\"pages\":\"203-209\"},\"PeriodicalIF\":1.0000,\"publicationDate\":\"2023-07-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://onlinelibrary.wiley.com/doi/epdf/10.1111/ahg.12520\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Annals of Human Genetics\",\"FirstCategoryId\":\"99\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1111/ahg.12520\",\"RegionNum\":4,\"RegionCategory\":\"生物学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"GENETICS & HEREDITY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Annals of Human Genetics","FirstCategoryId":"99","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1111/ahg.12520","RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"GENETICS & HEREDITY","Score":null,"Total":0}
Pitfalls of predicting age-related traits by polygenic risk scores
Polygenic risk scores (PRS) are a method increasingly used to capture the combined effect of genome-wide significant variants and those which individually do not show genome-wide significant association but are likely to contribute to the risk of developing diseases. However, their practical use incurs complications and inconsistencies that so far limit their clinical applicability. The aims of the present review are to discuss the PRS for age-related diseases and to highlight pitfalls and limitations of PRS prediction accuracy due to ageing and mortality effects. We argue that the PRS is widely used but the individual's PRS values differ substantially depending on the number of genetic variants included, the discovery GWAS and the method employed to generate them. Moreover, for neurodegenerative disorders, although an individual's genetics do not change with age, the actual score depends on the age of the sample used in the discovery GWAS and is likely to reflect the individual's disease risk at this particular age. Improvement of PRS prediction accuracy for neurodegenerative disorders will come from two sides, both the precision of clinical diagnoses, and a careful attention to the age distribution in the underlying samples and validation of the prediction in longitudinal studies.
期刊介绍:
Annals of Human Genetics publishes material directly concerned with human genetics or the application of scientific principles and techniques to any aspect of human inheritance. Papers that describe work on other species that may be relevant to human genetics will also be considered. Mathematical models should include examples of application to data where possible.
Authors are welcome to submit Supporting Information, such as data sets or additional figures or tables, that will not be published in the print edition of the journal, but which will be viewable via the online edition and stored on the website.