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酵母对外界环境干扰响应的动态遗传结构揭示了隐性遗传变异的起源。

Dynamic genetic architecture of yeast response to environmental perturbation shed light on origin of cryptic genetic variation.

机构信息

Department of Medical Biochemistry and Microbiology, Uppsala University, Uppsala, Sweden.

出版信息

PLoS Genet. 2020 May 11;16(5):e1008801. doi: 10.1371/journal.pgen.1008801. eCollection 2020 May.

Abstract

Cryptic genetic variation could arise from, for example, Gene-by-Gene (G-by-G) or Gene-by-Environment (G-by-E) interactions. The underlying molecular mechanisms and how they influence allelic effects and the genetic variance of complex traits is largely unclear. Here, we empirically explored the role of environmentally influenced epistasis on the suppression and release of cryptic variation by reanalysing a dataset of 4,390 haploid yeast segregants phenotyped on 20 different media. The focus was on 130 epistatic loci, each contributing to segregant growth in at least one environment and that together explained most (69-100%) of the narrow sense heritability of growth in the individual environments. We revealed that the epistatic growth network reorganised upon environmental changes to alter the estimated marginal (additive) effects of the individual loci, how multi-locus interactions contributed to individual segregant growth and the level of expressed genetic variance in growth. The estimated additive effects varied most across environments for loci that were highly interactive network hubs in some environments but had few or no interactors in other environments, resulting in changes in total genetic variance across environments. This environmentally dependent epistasis was thus an important mechanism for the suppression and release of cryptic variation in this population. Our findings increase the understanding of the complex genetic mechanisms leading to cryptic variation in populations, providing a basis for future studies on the genetic maintenance of trait robustness and development of genetic models for studying and predicting selection responses for quantitative traits in breeding and evolution.

摘要

隐匿遗传变异可能来自基因-基因(G-by-G)或基因-环境(G-by-E)相互作用。潜在的分子机制以及它们如何影响等位基因效应和复杂性状的遗传方差在很大程度上尚不清楚。在这里,我们通过重新分析 4390 个在 20 种不同培养基上表型的单倍体酵母分离子数据集,实证探讨了环境影响的上位性在隐匿变异的抑制和释放中的作用。重点是 130 个上位性位点,每个位点在至少一种环境中对分离子的生长有贡献,并且共同解释了个体环境中生长的狭义遗传率的大部分(69-100%)。我们发现,上位性生长网络在环境变化时重新组织,以改变个体位点的估计边缘(加性)效应、多基因相互作用对个体分离子生长的贡献以及生长中表达遗传方差的水平。对于那些在某些环境中是高度相互作用网络枢纽但在其他环境中几乎没有或没有相互作用者的位点,其估计的加性效应在环境中变化最大,导致总遗传方差在环境中发生变化。因此,这种环境依赖性上位性是该群体中隐匿变异抑制和释放的重要机制。我们的研究结果增加了对导致群体中隐匿变异的复杂遗传机制的理解,为未来研究性状稳健性的遗传维持以及研究和预测定量性状选择响应的遗传模型提供了基础,这些模型在育种和进化中。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b800/7241848/0f10feead41a/pgen.1008801.g001.jpg

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