Department of Agronomy, Iowa State University, Ames, IA 50011.
Department of Agronomy, Iowa State University, Ames, IA 50011
Proc Natl Acad Sci U S A. 2018 Jun 26;115(26):6679-6684. doi: 10.1073/pnas.1718326115. Epub 2018 Jun 11.
Observed phenotypic variation in living organisms is shaped by genomes, environment, and their interactions. Flowering time under natural conditions can showcase the diverse outcome of the gene-environment interplay. However, identifying hidden patterns and specific factors underlying phenotypic plasticity under natural field conditions remains challenging. With a genetic population showing dynamic changes in flowering time, here we show that the integrated analyses of genomic responses to diverse environments is powerful to reveal the underlying genetic architecture. Specifically, the effect continuum of individual genes ( , , , and ) was found to vary in size and in direction along an environmental gradient that was quantified by photothermal time, a combination of two environmental factors (photoperiod and temperature). Gene-gene interaction was also contributing to the observed phenotypic plasticity. With the identified environmental index to quantitatively connect environments, a systematic genome-wide performance prediction framework was established through either genotype-specific reaction-norm parameters or genome-wide marker-effect continua. These parallel genome-wide approaches were demonstrated for in-season and on-target performance prediction by simultaneously exploiting genomics, environment profiling, and performance information. Improved understanding of mechanisms for phenotypic plasticity enables a concerted exploration that turns challenge into opportunity.
生物体的表型变异是由基因组、环境及其相互作用塑造的。在自然条件下的开花时间可以展示基因-环境相互作用的多样结果。然而,在自然野外条件下识别表型可塑性背后隐藏的模式和特定因素仍然具有挑战性。在一个表现出开花时间动态变化的遗传群体中,我们展示了对不同环境的基因组反应的综合分析具有揭示潜在遗传结构的强大能力。具体来说,发现个体基因( 、 、 、和 )的效应连续体在大小和方向上沿着光热时间(光周期和温度两个环境因素的组合)的环境梯度上变化。基因-基因相互作用也对观察到的表型可塑性有贡献。通过确定的环境指数来定量连接环境,建立了一个系统的全基因组性能预测框架,方法是通过基因型特异性反应规范参数或全基因组标记效应连续体。通过同时利用基因组学、环境特征分析和性能信息,这些平行的全基因组方法展示了在季内和目标性能预测中的应用。对表型可塑性机制的更好理解使我们能够协调探索,将挑战转化为机遇。