Yiguan Wang, Scott L. Allen, Adam J. Reddiex, Stephen F. Chenoweth
{"title":"正向选择对血清果蝇基因组变异的影响:深度学习方法的启示","authors":"Yiguan Wang, Scott L. Allen, Adam J. Reddiex, Stephen F. Chenoweth","doi":"10.1111/mec.17499","DOIUrl":null,"url":null,"abstract":"<p>This study explores the impact of positive selection on the genetic composition of a <i>Drosophila serrata</i> population in eastern Australia through a comprehensive analysis of 110 whole genome sequences. Utilizing an advanced deep learning algorithm (partialS/HIC) and a range of inferred demographic histories, we identified that approximately 14% of the genome is directly affected by sweeps, with soft sweeps being more prevalent (10.6%) than hard sweeps (2.1%), and partial sweeps being uncommon (1.3%). The algorithm demonstrated robustness to demographic assumptions in classifying complete sweeps but faced challenges in distinguishing neutral regions from partial sweeps and linked regions under demographic misspecification. The findings reveal the indirect influence of sweeps on nearly two-thirds of the genome through linkage, with an over-representation of putatively deleterious variants suggesting that positive selection drags deleterious variants to higher frequency due to hitchhiking with beneficial loci. Gene ontology enrichment analysis further supported our confidence in the accuracy of sweep detection as several traits expected to be under positive selection due to evolutionary arms races (e.g. immunity) were detected in hard sweeps. This study provides valuable insights into the direct and indirect contributions of positive selection in shaping genomic variation in natural populations.</p>","PeriodicalId":210,"journal":{"name":"Molecular Ecology","volume":null,"pages":null},"PeriodicalIF":4.5000,"publicationDate":"2024-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/mec.17499","citationCount":"0","resultStr":"{\"title\":\"The impacts of positive selection on genomic variation in Drosophila serrata: Insights from a deep learning approach\",\"authors\":\"Yiguan Wang, Scott L. Allen, Adam J. Reddiex, Stephen F. Chenoweth\",\"doi\":\"10.1111/mec.17499\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>This study explores the impact of positive selection on the genetic composition of a <i>Drosophila serrata</i> population in eastern Australia through a comprehensive analysis of 110 whole genome sequences. Utilizing an advanced deep learning algorithm (partialS/HIC) and a range of inferred demographic histories, we identified that approximately 14% of the genome is directly affected by sweeps, with soft sweeps being more prevalent (10.6%) than hard sweeps (2.1%), and partial sweeps being uncommon (1.3%). The algorithm demonstrated robustness to demographic assumptions in classifying complete sweeps but faced challenges in distinguishing neutral regions from partial sweeps and linked regions under demographic misspecification. The findings reveal the indirect influence of sweeps on nearly two-thirds of the genome through linkage, with an over-representation of putatively deleterious variants suggesting that positive selection drags deleterious variants to higher frequency due to hitchhiking with beneficial loci. Gene ontology enrichment analysis further supported our confidence in the accuracy of sweep detection as several traits expected to be under positive selection due to evolutionary arms races (e.g. immunity) were detected in hard sweeps. This study provides valuable insights into the direct and indirect contributions of positive selection in shaping genomic variation in natural populations.</p>\",\"PeriodicalId\":210,\"journal\":{\"name\":\"Molecular Ecology\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":4.5000,\"publicationDate\":\"2024-08-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://onlinelibrary.wiley.com/doi/epdf/10.1111/mec.17499\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Molecular Ecology\",\"FirstCategoryId\":\"99\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1111/mec.17499\",\"RegionNum\":1,\"RegionCategory\":\"生物学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"BIOCHEMISTRY & MOLECULAR BIOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Molecular Ecology","FirstCategoryId":"99","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1111/mec.17499","RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"BIOCHEMISTRY & MOLECULAR BIOLOGY","Score":null,"Total":0}
The impacts of positive selection on genomic variation in Drosophila serrata: Insights from a deep learning approach
This study explores the impact of positive selection on the genetic composition of a Drosophila serrata population in eastern Australia through a comprehensive analysis of 110 whole genome sequences. Utilizing an advanced deep learning algorithm (partialS/HIC) and a range of inferred demographic histories, we identified that approximately 14% of the genome is directly affected by sweeps, with soft sweeps being more prevalent (10.6%) than hard sweeps (2.1%), and partial sweeps being uncommon (1.3%). The algorithm demonstrated robustness to demographic assumptions in classifying complete sweeps but faced challenges in distinguishing neutral regions from partial sweeps and linked regions under demographic misspecification. The findings reveal the indirect influence of sweeps on nearly two-thirds of the genome through linkage, with an over-representation of putatively deleterious variants suggesting that positive selection drags deleterious variants to higher frequency due to hitchhiking with beneficial loci. Gene ontology enrichment analysis further supported our confidence in the accuracy of sweep detection as several traits expected to be under positive selection due to evolutionary arms races (e.g. immunity) were detected in hard sweeps. This study provides valuable insights into the direct and indirect contributions of positive selection in shaping genomic variation in natural populations.
期刊介绍:
Molecular Ecology publishes papers that utilize molecular genetic techniques to address consequential questions in ecology, evolution, behaviour and conservation. Studies may employ neutral markers for inference about ecological and evolutionary processes or examine ecologically important genes and their products directly. We discourage papers that are primarily descriptive and are relevant only to the taxon being studied. Papers reporting on molecular marker development, molecular diagnostics, barcoding, or DNA taxonomy, or technical methods should be re-directed to our sister journal, Molecular Ecology Resources. Likewise, papers with a strongly applied focus should be submitted to Evolutionary Applications. Research areas of interest to Molecular Ecology include:
* population structure and phylogeography
* reproductive strategies
* relatedness and kin selection
* sex allocation
* population genetic theory
* analytical methods development
* conservation genetics
* speciation genetics
* microbial biodiversity
* evolutionary dynamics of QTLs
* ecological interactions
* molecular adaptation and environmental genomics
* impact of genetically modified organisms