Suppr超能文献

复杂性状变异的维持:经典理论与现代数据

Maintenance of Complex Trait Variation: Classic Theory and Modern Data.

作者信息

Koch Evan M, Sunyaev Shamil R

机构信息

Department of Biomedical Informatics, Harvard Medical School, Boston, MA, United States.

Division of Genetics, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States.

出版信息

Front Genet. 2021 Nov 12;12:763363. doi: 10.3389/fgene.2021.763363. eCollection 2021.

Abstract

Numerous studies have found evidence that GWAS loci experience negative selection, which increases in intensity with the effect size of identified variants. However, there is also accumulating evidence that this selection is not entirely mediated by the focal trait and contains a substantial pleiotropic component. Understanding how selective constraint shapes phenotypic variation requires advancing models capable of balancing these and other components of selection, as well as empirical analyses capable of inferring this balance and how it is generated by the underlying biology. We first review the classic theory connecting phenotypic selection to selection at individual loci as well as approaches and findings from recent analyses of negative selection in GWAS data. We then discuss geometric theories of pleiotropic selection with the potential to guide future modeling efforts. Recent findings revealing the nature of pleiotropic genetic variation provide clues to which genetic relationships are important and should be incorporated into analyses of selection, while findings that effect sizes vary between populations indicate that GWAS measurements could be misleading if effect sizes have also changed throughout human history.

摘要

众多研究已发现证据表明全基因组关联研究(GWAS)位点经历负选择,且这种选择强度会随着所识别变异的效应大小而增加。然而,也有越来越多的证据表明,这种选择并非完全由目标性状介导,而是包含大量的多效性成分。理解选择性约束如何塑造表型变异,需要改进能够平衡这些选择成分及其他选择成分的模型,以及能够推断这种平衡及其如何由基础生物学产生的实证分析。我们首先回顾将表型选择与单个位点选择联系起来的经典理论,以及近期对GWAS数据中负选择分析的方法和发现。然后,我们讨论多效性选择的几何理论,其有可能为未来的建模工作提供指导。揭示多效性遗传变异本质的近期发现,为哪些遗传关系重要以及应纳入选择分析提供了线索,而效应大小在不同人群间存在差异的发现表明,如果效应大小在人类历史进程中也发生了变化,那么GWAS测量结果可能会产生误导。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/72f1/8636146/e6abf4017c22/fgene-12-763363-g001.jpg

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验