{"title":"Genetic interaction network of quantitative trait genes for heading date in rice.","authors":"Mengjiao Chen, Yifeng Hong, Jiongjiong Fan, Dengyi Cao, Chong Yin, Anjie Yu, Jie Qiu, Xuehui Huang, Xin Wei","doi":"10.1016/j.jgg.2024.12.019","DOIUrl":null,"url":null,"abstract":"<p><p>Several quantitative trait genes (QTGs) related to rice heading date, a key factor for crop development and yield, have been identified, along with complex interactions among genes. However, a comprehensive genetic interaction network for these QTGs has not yet been established. In this study, we use 18K-rice lines to identify QTGs and their epistatic interactions affecting rice heading date. We identify 264 pairs of interacting QTL and construct a comprehensive genetic network of these QTL. On average, the epistatic effects of QTL pairs are estimated to be approximately 12.5% of additive effects of identified QTL. Importantly, epistasis vary among different alleles of several heading date genes. Additionally, 57 pairs of interacting QTGs are also significant in their epistatic effects on 12 other agronomic traits. The identified QTL genetic interactions are further validated using near-isogenic lines, yeast two-hybrid, and split-luciferase complementation assays. Overall, this study provides a genetic network of rice heading date genes, which plays a crucial role in regulating rice heading date and influencing multiple related agronomic traits. This network serves as a foundation for understanding the genetic mechanisms of rice quantitative traits and for advancing molecular breeding efforts.</p>","PeriodicalId":54825,"journal":{"name":"Journal of Genetics and Genomics","volume":" ","pages":""},"PeriodicalIF":6.6000,"publicationDate":"2025-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Genetics and Genomics","FirstCategoryId":"99","ListUrlMain":"https://doi.org/10.1016/j.jgg.2024.12.019","RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"BIOCHEMISTRY & MOLECULAR BIOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
Several quantitative trait genes (QTGs) related to rice heading date, a key factor for crop development and yield, have been identified, along with complex interactions among genes. However, a comprehensive genetic interaction network for these QTGs has not yet been established. In this study, we use 18K-rice lines to identify QTGs and their epistatic interactions affecting rice heading date. We identify 264 pairs of interacting QTL and construct a comprehensive genetic network of these QTL. On average, the epistatic effects of QTL pairs are estimated to be approximately 12.5% of additive effects of identified QTL. Importantly, epistasis vary among different alleles of several heading date genes. Additionally, 57 pairs of interacting QTGs are also significant in their epistatic effects on 12 other agronomic traits. The identified QTL genetic interactions are further validated using near-isogenic lines, yeast two-hybrid, and split-luciferase complementation assays. Overall, this study provides a genetic network of rice heading date genes, which plays a crucial role in regulating rice heading date and influencing multiple related agronomic traits. This network serves as a foundation for understanding the genetic mechanisms of rice quantitative traits and for advancing molecular breeding efforts.
期刊介绍:
The Journal of Genetics and Genomics (JGG, formerly known as Acta Genetica Sinica ) is an international journal publishing peer-reviewed articles of novel and significant discoveries in the fields of genetics and genomics. Topics of particular interest include but are not limited to molecular genetics, developmental genetics, cytogenetics, epigenetics, medical genetics, population and evolutionary genetics, genomics and functional genomics as well as bioinformatics and computational biology.