On December 30, a research result of National Key Laboratory of Crop Genetic Improvement of HZAU and the Hubei Hongshan Laboratory was published online in Nature Genetics, an international academic journal. This study constructed the first-generation multi-group integrated network map of maize, providing a new path to systematically analyze the inheritance of complex characters, and discussed its application scenario by taking the flowering period of maize as an example.
In 2016, Prof. Li Lin’s research group, together with Prof. Yang Fang’s and Prof. Yan Jianbing’s research groups from HAZU, officially started the construction of maize multidimensional network map at the level of genome, transcriptome, translation and proteomics. After six years of hard work, the research groups finished constructing a “relationship network” about genes and gene regulation from the perspective of multidimensional biology by using the biological network big data. The network involves 2 million interactions, which lays a foundation for a comprehensive and systematic analysis of the mechanism of genetic variation in maize.
The genes within a living body are like people in human society – you may learn the person through his relatives, friends, and colleagues. The first-generation multi-group integrated network map of maize is a map that describes the hierarchical relationships of global genes in maize, according to Prof. Li Lin.
The research team explored the functional differentiation of repetitive genes in the network at the whole genome level, based on the successfully constructed big data map of multidimensional network of maize. They found that there were significant differences in the functional differentiation of duplicate genes in terms of types and ages, which revealed that the two ancient sub-genomes of maize showed gradual functional differentiation from transcriptome to protein interaction group. At the same time, the molecular networks of functional genes related to plant type, grain quality and grain size were reconstructed, and the new genes and molecular networks affecting the target traits were predicted.
The research group has carried out a large number of explorations based on artificial intelligence algorithms in order to further use millions of molecular regulatory networks to analyze the genetic mysteries of complex quantitative genetic traits in maize. For example, speaking of important agronomic characters of maize in flowering stage, several artificial intelligence algorithms for predicting functional genes at flowering stage were developed, and related studies were carried out, which lays a theoretical foundation and provides genetic resources for intelligently designed breeding at flowering stage.
Source: http://news.hzau.edu.cn/2022/1231/65457.shtml
Translated by: Sun Gaojing
Supervised by: Xie Lujie
