Breeding Informatics Group, Department of Animal Sciences, Georg-August University, Margarethe von Wrangell-Weg 7, 37075 Göttingen, Germany.
Faculty of Agriculture, South Westphalia University of Applied Sciences, Lübecker Ring 2, 59494 Soest, Germany.
Int J Mol Sci. 2022 May 4;23(9):5121. doi: 10.3390/ijms23095121.
Maize is one of the most widely grown cereals in the world. However, to address the challenges in maize breeding arising from climatic anomalies, there is a need for developing novel strategies to harness the power of multi-omics technologies. In this regard, pleiotropy is an important genetic phenomenon that can be utilized to simultaneously enhance multiple agronomic phenotypes in maize. In addition to pleiotropy, another aspect is the consideration of the regulatory SNPs (rSNPs) that are likely to have causal effects in phenotypic development. By incorporating both aspects in our study, we performed a systematic analysis based on multi-omics data to reveal the novel pleiotropic signatures of rSNPs in a global maize population. For this purpose, we first applied Random Forests and then Markov clustering algorithms to decipher the pleiotropic signatures of rSNPs, based on which hierarchical network models are constructed to elucidate the complex interplay among transcription factors, rSNPs, and phenotypes. The results obtained in our study could help to understand the genetic programs orchestrating multiple phenotypes and thus could provide novel breeding targets for the simultaneous improvement of several agronomic traits.
玉米是世界上种植最广泛的谷物之一。然而，为了应对气候异常给玉米育种带来的挑战，我们需要开发新的策略来利用多组学技术的力量。在这方面，多效性是一种重要的遗传现象，可以用来同时提高玉米的多个农艺表型。除了多效性，另一个方面是考虑可能对表型发育具有因果影响的调控 SNP（rSNP）。在我们的研究中，我们综合考虑了这两个方面，基于多组学数据进行了系统分析，揭示了 rSNP 在全球玉米群体中的新的多效性特征。为此，我们首先应用随机森林算法，然后应用马尔可夫聚类算法，根据 rSNP 的多效性特征进行解码，在此基础上构建层次网络模型，以阐明转录因子、rSNP 和表型之间的复杂相互作用。我们研究中获得的结果有助于理解协调多个表型的遗传程序，从而为同时提高几个农艺性状提供新的育种目标。
