Interpreting the functional impacts of genetic variants (GVs) is an important challenge for functional genomic studies in crops and next-generation breeding. Previous studies in rice (Oryza sativa) have focused mainly on the identification of GVs, whereas systematic functional annotation of GVs has not yet been performed. Here, we present a functional impact map of GVs in rice. We curated haplotype information for 17 397 026 GVs from sequencing data of 4726 rice accessions. We quantitatively evaluated the effects of missense mutations in coding regions in each haplotype based on the conservation of amino acid residues and obtained the effects of 918 848 non-redundant missense GVs. Furthermore, we generated high-quality chromatin accessibility (CA) data from six representative rice tissues and used these data to train deep convolutional neural network models to predict the impacts of 5 067 405 GVs for CA in regulatory regions. We characterized the functional properties and tissue specificity of the GV effects and found that large-effect GVs in coding and regulatory regions may be subject to selection in different directions. Finally, we demonstrated how the functional impact map could be used to prioritize causal variants in mapping populations. This impact map will be a useful resource for accelerating gene cloning and functional studies in rice, and can be freely queried in RiceVarMap V2.0 (http://ricevarmap.ncpgr.cn).

解释遗传变异 (GV) 的功能影响是作物和下一代育种中功能基因组研究的重要挑战。以前的水稻研究（Oryza sativa) 主要集中在 GV 的识别上，而 GV 的系统功能注释尚未进行。在这里，我们展示了水稻中 GV 的功能影响图。我们从 4726 个水稻种质的测序数据中整理了 17397026 个 GV 的单倍型信息。我们基于氨基酸残基的保守性定量评估了每个单倍型编码区错义突变的影响，并获得了 918 848 个非冗余错义 GV 的影响。此外，我们从六个有代表性的水稻组织中生成了高质量的染色质可及性 (CA) 数据，并使用这些数据训练深度卷积神经网络模型，以预测 5 067 405 GV 对 CA 在监管区域的影响。我们对 GV 效应的功能特性和组织特异性进行了表征，发现编码和调节区域中的大效应 GV 可能会受到不同方向的选择。最后，我们展示了如何使用功能影响图来确定映射人群中的因果变异的优先级。该影响图将成为加速水稻基因克隆和功能研究的有用资源，并可在RiceVarMap V2.0（http://ricevarmap.ncpgr.cn）中自由查询。