{"title":"Alternative mutational architectures producing identical M-matrices can lead to different patterns of evolutionary divergence.","authors":"Daohan Jiang, Matt Pennell","doi":"10.1093/gbe/evaf099","DOIUrl":null,"url":null,"abstract":"<p><p>Explaining macroevolutionary divergence in light of population genetics requires understanding the extent to which the patterns of mutational input contribute to long-term trends. In the context of quantitative traits, mutational input is typically described by the mutational variance-covariance matrix, the M-matrix, which summarizes phenotypic variances and covariances introduced by new mutations per generation. However, as a summary statistic, the M-matrix does not fully capture all the relevant information from the underlying mutational architecture, and there exist a myriad of possible underlying mutational architectures that give rise to the same M-matrix. Using individual-based simulations, we demonstrate mutational architectures that produce the same M-matrix can lead to different levels of constraint on evolution and result in difference in within-population genetic variance, between-population divergence, and rate of adaptation. In particular, the rate of adaptation and that of neutral evolution are both reduced when a greater proportion of loci are pleiotropic. Our results reveal that aspects of mutational input not reflected by the M-matrix can have a profound impact on long-term evolution, and suggest it is important to take them into account in order to connect patterns of long-term phenotypic evolution to underlying microevolutionary mechanisms.</p>","PeriodicalId":12779,"journal":{"name":"Genome Biology and Evolution","volume":" ","pages":""},"PeriodicalIF":3.2000,"publicationDate":"2025-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Genome Biology and Evolution","FirstCategoryId":"99","ListUrlMain":"https://doi.org/10.1093/gbe/evaf099","RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"EVOLUTIONARY BIOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
Explaining macroevolutionary divergence in light of population genetics requires understanding the extent to which the patterns of mutational input contribute to long-term trends. In the context of quantitative traits, mutational input is typically described by the mutational variance-covariance matrix, the M-matrix, which summarizes phenotypic variances and covariances introduced by new mutations per generation. However, as a summary statistic, the M-matrix does not fully capture all the relevant information from the underlying mutational architecture, and there exist a myriad of possible underlying mutational architectures that give rise to the same M-matrix. Using individual-based simulations, we demonstrate mutational architectures that produce the same M-matrix can lead to different levels of constraint on evolution and result in difference in within-population genetic variance, between-population divergence, and rate of adaptation. In particular, the rate of adaptation and that of neutral evolution are both reduced when a greater proportion of loci are pleiotropic. Our results reveal that aspects of mutational input not reflected by the M-matrix can have a profound impact on long-term evolution, and suggest it is important to take them into account in order to connect patterns of long-term phenotypic evolution to underlying microevolutionary mechanisms.
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About the journal
Genome Biology and Evolution (GBE) publishes leading original research at the interface between evolutionary biology and genomics. Papers considered for publication report novel evolutionary findings that concern natural genome diversity, population genomics, the structure, function, organisation and expression of genomes, comparative genomics, proteomics, and environmental genomic interactions. Major evolutionary insights from the fields of computational biology, structural biology, developmental biology, and cell biology are also considered, as are theoretical advances in the field of genome evolution. GBE’s scope embraces genome-wide evolutionary investigations at all taxonomic levels and for all forms of life — within populations or across domains. Its aims are to further the understanding of genomes in their evolutionary context and further the understanding of evolution from a genome-wide perspective.