{"title":"万物是如何相互联系的——适应性进化、疾病易感性和药物反应之间的群体特异性联系","authors":"Ji Tang, Hao Zhu","doi":"10.1002/ggn2.202500018","DOIUrl":null,"url":null,"abstract":"<p>The genome is like a kaleidoscope through which researchers have obtained varied findings, including favored mutations, disease susceptibility sites, and drug-responsive sites. Whether these findings have inherent connections is a question deserving investigation. Favored mutations enable humans to adapt to changing environments and lifestyles; however, the adaptation may come with some costs. This is because a favored mutation can change the frequency of varied neutral nucleotides across a large genomic region, and a favored mutation may become disfavored as environments and lifestyles change further. These are the best-known classes of connections whose causes and consequences have been understood. However, many favored mutations remain unidentified. Using a deep learning network (<i>DeepFavored</i>) that integrates statistical tests and is trained on large datasets, favored mutations are recently identified in 17 human populations. The analyses of the results, in conjunction with genome-wide association study (GWAS) data, suggest that the connection between adaptive evolution, disease susceptibility, and drug responsiveness (referred to as a trade-off) is extensive and highly population-specific. The analyses, along with other emerging evidence, suggest that there are other types of connections. In this commentary, these issues are discussed from both retrospective and prospective views, including current challenges and future directions.</p>","PeriodicalId":72071,"journal":{"name":"Advanced genetics (Hoboken, N.J.)","volume":"6 3","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2025-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://advanced.onlinelibrary.wiley.com/doi/epdf/10.1002/ggn2.202500018","citationCount":"0","resultStr":"{\"title\":\"How Everything Is Connected to Everything Else – Population-Specific Connections between Adaptive Evolution, Disease Susceptibility, and Drug Responsiveness\",\"authors\":\"Ji Tang, Hao Zhu\",\"doi\":\"10.1002/ggn2.202500018\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>The genome is like a kaleidoscope through which researchers have obtained varied findings, including favored mutations, disease susceptibility sites, and drug-responsive sites. Whether these findings have inherent connections is a question deserving investigation. Favored mutations enable humans to adapt to changing environments and lifestyles; however, the adaptation may come with some costs. This is because a favored mutation can change the frequency of varied neutral nucleotides across a large genomic region, and a favored mutation may become disfavored as environments and lifestyles change further. These are the best-known classes of connections whose causes and consequences have been understood. However, many favored mutations remain unidentified. Using a deep learning network (<i>DeepFavored</i>) that integrates statistical tests and is trained on large datasets, favored mutations are recently identified in 17 human populations. The analyses of the results, in conjunction with genome-wide association study (GWAS) data, suggest that the connection between adaptive evolution, disease susceptibility, and drug responsiveness (referred to as a trade-off) is extensive and highly population-specific. The analyses, along with other emerging evidence, suggest that there are other types of connections. In this commentary, these issues are discussed from both retrospective and prospective views, including current challenges and future directions.</p>\",\"PeriodicalId\":72071,\"journal\":{\"name\":\"Advanced genetics (Hoboken, N.J.)\",\"volume\":\"6 3\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2025-09-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://advanced.onlinelibrary.wiley.com/doi/epdf/10.1002/ggn2.202500018\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Advanced genetics (Hoboken, N.J.)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://advanced.onlinelibrary.wiley.com/doi/10.1002/ggn2.202500018\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Advanced genetics (Hoboken, N.J.)","FirstCategoryId":"1085","ListUrlMain":"https://advanced.onlinelibrary.wiley.com/doi/10.1002/ggn2.202500018","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
How Everything Is Connected to Everything Else – Population-Specific Connections between Adaptive Evolution, Disease Susceptibility, and Drug Responsiveness
The genome is like a kaleidoscope through which researchers have obtained varied findings, including favored mutations, disease susceptibility sites, and drug-responsive sites. Whether these findings have inherent connections is a question deserving investigation. Favored mutations enable humans to adapt to changing environments and lifestyles; however, the adaptation may come with some costs. This is because a favored mutation can change the frequency of varied neutral nucleotides across a large genomic region, and a favored mutation may become disfavored as environments and lifestyles change further. These are the best-known classes of connections whose causes and consequences have been understood. However, many favored mutations remain unidentified. Using a deep learning network (DeepFavored) that integrates statistical tests and is trained on large datasets, favored mutations are recently identified in 17 human populations. The analyses of the results, in conjunction with genome-wide association study (GWAS) data, suggest that the connection between adaptive evolution, disease susceptibility, and drug responsiveness (referred to as a trade-off) is extensive and highly population-specific. The analyses, along with other emerging evidence, suggest that there are other types of connections. In this commentary, these issues are discussed from both retrospective and prospective views, including current challenges and future directions.