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全基因组关联研究能告诉我们哪些关于维持数量性状遗传变异的进化力量?

What can genome-wide association studies tell us about the evolutionary forces maintaining genetic variation for quantitative traits?

机构信息

Department of Evolution and Ecology, University of California, Davis, One Shields Avenue, Davis, CA, 95616, USA.

Department of Ecology and Evolutionary Biology, University of Toronto, 25 Willcocks St., Toronto, ON, M5S 3B2, Canada.

出版信息

New Phytol. 2017 Apr;214(1):21-33. doi: 10.1111/nph.14410. Epub 2017 Feb 17.

DOI:10.1111/nph.14410
PMID:28211582
Abstract

Contents 21 I. 21 II. 22 III. 24 IV. 25 V. 29 30 References 30 SUMMARY: Understanding the evolutionary forces that shape genetic variation within species has long been a goal of evolutionary biology. Integrating data for the genetic architecture of traits from genome-wide association mapping studies (GWAS) along with the development of new population genetic methods for identifying selection in sequence data may allow us to evaluate the roles of mutation-selection balance and balancing selection in shaping genetic variation at various scales. Here, we review the theoretical predictions for genetic architecture and additional signals of selection on genomic sequence for the loci that affect traits. Next, we review how plant GWAS have tested for the signatures of various selective scenarios. Limited evidence to date suggests that within-population variation is maintained primarily by mutation-selection balance while variation across the landscape is the result of local adaptation. However, there are a number of inherent biases in these interpretations. We highlight these challenges and suggest ways forward to further understanding of the maintenance of variation.

摘要

内容 21 I. 21 II. 22 III. 24 IV. 25 V. 29 30 参考文献 30 摘要:理解塑造物种内遗传变异的进化力量一直是进化生物学的目标。将来自全基因组关联图谱研究(GWAS)的性状遗传结构数据与新的群体遗传方法相结合,用于识别序列数据中的选择,这可能使我们能够评估突变-选择平衡和平衡选择在不同尺度上塑造遗传变异的作用。在这里,我们回顾了影响性状的基因座的遗传结构和基因组序列上选择额外信号的理论预测。接下来,我们回顾了植物 GWAS 如何检验各种选择情景的特征。目前的有限证据表明,种群内的变异主要由突变-选择平衡维持,而景观上的变异则是局部适应的结果。然而,这些解释存在许多固有的偏见。我们强调了这些挑战,并提出了进一步理解变异维持的方法。

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