Junrong Liu, Xinyi Lou, Lin Zhang, Tiangang Hou, Xin Xin, Yan Wang, Shu Wang, Yuancai Huang, Chanchan Zhou, Baoyan Jia, Yue Feng
{"title":"基于ril群体QTL分析的水稻产量相关性研究。","authors":"Junrong Liu, Xinyi Lou, Lin Zhang, Tiangang Hou, Xin Xin, Yan Wang, Shu Wang, Yuancai Huang, Chanchan Zhou, Baoyan Jia, Yue Feng","doi":"10.1186/s12863-025-01316-3","DOIUrl":null,"url":null,"abstract":"<p><p>Rice production has been a primary concern in crop quality breeding. In this study, India japonica variety M494 and indica variety Z9B were used as parents. Hybridization and selfing were conducted to obtain recombinant inbred lines (RILs) as the experimental material. The F<sub>3</sub> and F<sub>7</sub> populations were analyzed to determine six yield-related traits, including panicle length, effective panicle number, number of grains per panicle, seed setting rate, yield per plant, and grain density. QTL mapping of rice yield-related traits and tillering angle was performed using the SSR molecular marker linkage map, resulting in the identification of 19 QTLs controlling panicle length, grain number per panicle, effective panicle number, seed setting rate, grain density.Additionally, multiple regression analysis and path analysis were employed to investigate the relationship between different agronomic traits and rice yield in the F<sub>7</sub> population. An optimal regression equation, Y<sub>YPP</sub> = -24.515 + 0.694X<sub>PL</sub> + 1.273X<sub>PN</sub> + 0.007X<sub>PPG</sub> + 18.981X<sub>SSR</sub> was derived, and it was concluded that SSR was the trait with the greatest impact on YPP, followed by PL.</p>","PeriodicalId":72427,"journal":{"name":"BMC genomic data","volume":"26 1","pages":"27"},"PeriodicalIF":1.9000,"publicationDate":"2025-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11995468/pdf/","citationCount":"0","resultStr":"{\"title\":\"Correlation of rice yield based on RILs population QTL analysis.\",\"authors\":\"Junrong Liu, Xinyi Lou, Lin Zhang, Tiangang Hou, Xin Xin, Yan Wang, Shu Wang, Yuancai Huang, Chanchan Zhou, Baoyan Jia, Yue Feng\",\"doi\":\"10.1186/s12863-025-01316-3\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Rice production has been a primary concern in crop quality breeding. In this study, India japonica variety M494 and indica variety Z9B were used as parents. Hybridization and selfing were conducted to obtain recombinant inbred lines (RILs) as the experimental material. The F<sub>3</sub> and F<sub>7</sub> populations were analyzed to determine six yield-related traits, including panicle length, effective panicle number, number of grains per panicle, seed setting rate, yield per plant, and grain density. QTL mapping of rice yield-related traits and tillering angle was performed using the SSR molecular marker linkage map, resulting in the identification of 19 QTLs controlling panicle length, grain number per panicle, effective panicle number, seed setting rate, grain density.Additionally, multiple regression analysis and path analysis were employed to investigate the relationship between different agronomic traits and rice yield in the F<sub>7</sub> population. An optimal regression equation, Y<sub>YPP</sub> = -24.515 + 0.694X<sub>PL</sub> + 1.273X<sub>PN</sub> + 0.007X<sub>PPG</sub> + 18.981X<sub>SSR</sub> was derived, and it was concluded that SSR was the trait with the greatest impact on YPP, followed by PL.</p>\",\"PeriodicalId\":72427,\"journal\":{\"name\":\"BMC genomic data\",\"volume\":\"26 1\",\"pages\":\"27\"},\"PeriodicalIF\":1.9000,\"publicationDate\":\"2025-04-14\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11995468/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"BMC genomic data\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1186/s12863-025-01316-3\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"GENETICS & HEREDITY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"BMC genomic data","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1186/s12863-025-01316-3","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"GENETICS & HEREDITY","Score":null,"Total":0}
Correlation of rice yield based on RILs population QTL analysis.
Rice production has been a primary concern in crop quality breeding. In this study, India japonica variety M494 and indica variety Z9B were used as parents. Hybridization and selfing were conducted to obtain recombinant inbred lines (RILs) as the experimental material. The F3 and F7 populations were analyzed to determine six yield-related traits, including panicle length, effective panicle number, number of grains per panicle, seed setting rate, yield per plant, and grain density. QTL mapping of rice yield-related traits and tillering angle was performed using the SSR molecular marker linkage map, resulting in the identification of 19 QTLs controlling panicle length, grain number per panicle, effective panicle number, seed setting rate, grain density.Additionally, multiple regression analysis and path analysis were employed to investigate the relationship between different agronomic traits and rice yield in the F7 population. An optimal regression equation, YYPP = -24.515 + 0.694XPL + 1.273XPN + 0.007XPPG + 18.981XSSR was derived, and it was concluded that SSR was the trait with the greatest impact on YPP, followed by PL.