A fundamental goal in plant biology is to identify and understand the variation underlying plants' adaptation to their environment. Climate change has given new urgency to this goal, as society aims to accelerate adaptation of ecologically important plant species, endangered plant species, and crops to hotter, less predictable climates. In the pre-genomic era, identifying adaptive alleles was painstaking work, leveraging genetics, molecular biology, physiology, and ecology. Now, the rise of genomics and new computational approaches may facilitate this research. Genotype-environment associations (GEAs) use statistical associations between allele frequency and environment of origin to test the hypothesis that allelic variation at a given gene is adapted to local environments. Researchers may scan the genome for GEAs to generate hypotheses on adaptive genetic variants (environmental genome-wide association studies). Despite the rapid adoption of these methods, many important questions remain about the interpretation of GEA findings, which arise from fundamental unanswered questions on the genetic architecture of adaptation and limitations inherent to association-based analyses. We outline strategies to ground GEAs in the underlying hypotheses of genetic architecture and better test GEA-generated hypotheses using genetics and ecophysiology. We provide recommendations for new users who seek to learn about the molecular basis of adaptation. When combined with a rigorous hypothesis testing framework, GEAs may facilitate our understanding of the molecular basis of climate adaptation for plant improvement.

中文翻译：

---

基因型-环境关联揭示环境适应的分子基础。

植物生物学的一个基本目标是识别和理解植物适应环境的变化。气候变化给这一目标带来了新的紧迫性，因为社会旨在加速对生态重要的植物物种、濒危植物物种和农作物适应更炎热、更难以预测的气候。在前基因组时代，利用遗传学、分子生物学、生理学和生态学，识别适应性等位基因是一项艰苦的工作。现在，基因组学和新计算方法的兴起可能会促进这项研究。基因型-环境关联 (GEA) 使用等位基因频率和起源环境之间的统计关联来检验给定基因的等位基因变异适应当地环境的假设。研究人员可以扫描基因组中的 GEA，以生成关于适应性遗传变异的假设（环境全基因组关联研究）。尽管这些方法得到了迅速采用，但关于 GEA 研究结果的解释仍然存在许多重要问题，这些问题源于关于适应遗传结构的基本未解答问题以及基于关联的分析固有的局限性。我们概述了将 GEA 置于遗传结构的基本假设中的策略，并使用遗传学和生态生理学更好地测试 GEA 生成的假设。我们为寻求了解适应分子基础的新用户提供建议。当与严格的假设检验框架相结合时，GEA 可能有助于我们理解气候适应以促进植物改良的分子基础。