Correlation of rice yield based on RILs population QTL analysis
Abstract 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 populat...
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BMC
2025-04-01
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| Series: | BMC Genomic Data |
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| Online Access: | https://doi.org/10.1186/s12863-025-01316-3 |
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| author | Junrong Liu Xinyi Lou Lin Zhang Tiangang Hou Xin Xin Yan Wang Shu Wang Yuancai Huang Chanchan Zhou Baoyan Jia Yue Feng |
| author_facet | Junrong Liu Xinyi Lou Lin Zhang Tiangang Hou Xin Xin Yan Wang Shu Wang Yuancai Huang Chanchan Zhou Baoyan Jia Yue Feng |
| author_sort | Junrong Liu |
| collection | DOAJ |
| description | Abstract 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. |
| format | Article |
| id | doaj-art-33e55697145b4a158fa14a21944ff0a7 |
| institution | OA Journals |
| issn | 2730-6844 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Genomic Data |
| spelling | doaj-art-33e55697145b4a158fa14a21944ff0a72025-08-20T02:27:13ZengBMCBMC Genomic Data2730-68442025-04-0126111410.1186/s12863-025-01316-3Correlation of rice yield based on RILs population QTL analysisJunrong Liu0Xinyi Lou1Lin Zhang2Tiangang Hou3Xin Xin4Yan Wang5Shu Wang6Yuancai Huang7Chanchan Zhou8Baoyan Jia9Yue Feng10Agricultural CollegeAgricultural CollegeAgricultural CollegeCollege of Engineering, Shenyang Agricultural UniversityAgricultural CollegeAgricultural CollegeAgricultural CollegeAgricultural CollegeAgricultural CollegeAgricultural CollegeChinese National Center for Rice Improvement and State Key Laboratory of Rice Biology, China National Rice Research InstituteAbstract 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.https://doi.org/10.1186/s12863-025-01316-3RiceYield related traitsQTLMultiple regression analysisPath analysis |
| spellingShingle | Junrong Liu Xinyi Lou Lin Zhang Tiangang Hou Xin Xin Yan Wang Shu Wang Yuancai Huang Chanchan Zhou Baoyan Jia Yue Feng Correlation of rice yield based on RILs population QTL analysis BMC Genomic Data Rice Yield related traits QTL Multiple regression analysis Path analysis |
| title | Correlation of rice yield based on RILs population QTL analysis |
| title_full | Correlation of rice yield based on RILs population QTL analysis |
| title_fullStr | Correlation of rice yield based on RILs population QTL analysis |
| title_full_unstemmed | Correlation of rice yield based on RILs population QTL analysis |
| title_short | Correlation of rice yield based on RILs population QTL analysis |
| title_sort | correlation of rice yield based on rils population qtl analysis |
| topic | Rice Yield related traits QTL Multiple regression analysis Path analysis |
| url | https://doi.org/10.1186/s12863-025-01316-3 |
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