Improvement of agricultural species has exploited the genetic variation responsible for complex quantitative traits. Much of the functional variation is regulatory, in cis-regulatory elements and trans-acting factors that ultimately contribute to gene expression differences. However, the identification of gene regulatory network components that, when modulated, will increase plant productivity or resilience, is challenging, yet essential to provide increased predictive power for genome engineering approaches that are likely to benefit useful traits. Here, we discuss the opportunities and limitations of using data obtained from gene coexpression, transcription factor binding, and genome-wide association mapping analyses to predict regulatory interactions that impact crop improvement. It is apparent that a combination of information from these data types is necessary for the reliable identification and utilization of important regulatory interactions that underlie complex agronomic traits.

农业物种的改良利用了导致复杂的数量性状的遗传变异。在顺式-调节元件和反式-调节元件中，许多功能性变化是调节性的。-最终导致基因表达差异的作用因子。然而，对基因调控网络成分的识别将具有挑战性，但如果对其进行调制，将能够提高植物的生产力或韧性，但对于为可能有益于性状的基因组工程方法提供增强的预测能力而言，这是必不可少的。在这里，我们讨论了使用从基因共表达，转录因子结合和全基因组关联映射分析获得的数据来预测影响作物改良的调控相互作用的机会和局限性。显然，对这些数据类型的信息进行组合对于可靠地识别和利用构成复杂农艺性状的重要调控相互作用是必不可少的。