Joint analysis of phenotype-effect-generation identifies loci associated with grain quality traits in rice hybrids
Abstract Genetic improvement of grain quality is more challenging in hybrid rice than in inbred rice due to additional nonadditive effects such as dominance. Here, we describe a pipeline developed for joint analysis of phenotypes, effects, and generations (JPEG). As a demonstration, we analyze 12 gr...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2023-07-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-023-39534-x |
| _version_ | 1797784587287396352 |
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| author | Lanzhi Li Xingfei Zheng Jiabo Wang Xueli Zhang Xiaogang He Liwen Xiong Shufeng Song Jing Su Ying Diao Zheming Yuan Zhiwu Zhang Zhongli Hu |
| author_facet | Lanzhi Li Xingfei Zheng Jiabo Wang Xueli Zhang Xiaogang He Liwen Xiong Shufeng Song Jing Su Ying Diao Zheming Yuan Zhiwu Zhang Zhongli Hu |
| author_sort | Lanzhi Li |
| collection | DOAJ |
| description | Abstract Genetic improvement of grain quality is more challenging in hybrid rice than in inbred rice due to additional nonadditive effects such as dominance. Here, we describe a pipeline developed for joint analysis of phenotypes, effects, and generations (JPEG). As a demonstration, we analyze 12 grain quality traits of 113 inbred lines (male parents), five tester lines (female parents), and 565 (113×5) of their hybrids. We sequence the parents for single nucleotide polymorphisms calling and infer the genotypes of the hybrids. Genome-wide association studies with JPEG identify 128 loci associated with at least one of the 12 traits, including 44, 97, and 13 loci with additive effects, dominant effects, and both additive and dominant effects, respectively. These loci together explain more than 30% of the genetic variation in hybrid performance for each of the traits. The JEPG statistical pipeline can help to identify superior crosses for breeding rice hybrids with improved grain quality. |
| first_indexed | 2024-03-13T00:42:01Z |
| format | Article |
| id | doaj.art-da5e5c4584fe4eefb997a8f393155916 |
| institution | Directory Open Access Journal |
| issn | 2041-1723 |
| language | English |
| last_indexed | 2024-03-13T00:42:01Z |
| publishDate | 2023-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj.art-da5e5c4584fe4eefb997a8f3931559162023-07-09T11:17:52ZengNature PortfolioNature Communications2041-17232023-07-011411910.1038/s41467-023-39534-xJoint analysis of phenotype-effect-generation identifies loci associated with grain quality traits in rice hybridsLanzhi Li0Xingfei Zheng1Jiabo Wang2Xueli Zhang3Xiaogang He4Liwen Xiong5Shufeng Song6Jing Su7Ying Diao8Zheming Yuan9Zhiwu Zhang10Zhongli Hu11Hunan Engineering & Technology Research Center for Agricultural Big Data Analysis & Decision-making, College of Plant Protection, Hunan Agricultural UniversityHubei Key Laboratory of Food Crop Germplasm and Genetic Improvement, Food Crop Institute, Hubei Academy of Agricultural SciencesKey Laboratory of Qinghai-Tibetan Plateau Animal Genetic Resource Reservation and Utilization of Ministry of Education and Sichuan province, Southwest Minzu UniversityHunan Engineering & Technology Research Center for Agricultural Big Data Analysis & Decision-making, College of Plant Protection, Hunan Agricultural UniversityHunan Engineering & Technology Research Center for Agricultural Big Data Analysis & Decision-making, College of Plant Protection, Hunan Agricultural UniversityHunan Engineering & Technology Research Center for Agricultural Big Data Analysis & Decision-making, College of Plant Protection, Hunan Agricultural UniversityState Key Laboratory of Hybrid Rice, Hunan Hybrid Rice Research Center, Hunan Academy of Agricultural SciencesHunan Engineering & Technology Research Center for Agricultural Big Data Analysis & Decision-making, College of Plant Protection, Hunan Agricultural UniversitySchool of Life Science and Technology, Wuhan Polytechnic UniversityHunan Engineering & Technology Research Center for Agricultural Big Data Analysis & Decision-making, College of Plant Protection, Hunan Agricultural UniversityDepartment of Crop and Soil Sciences, Washington State UniversityState Key Laboratory of Hybrid Rice, College of Life Science, Wuhan UniversityAbstract Genetic improvement of grain quality is more challenging in hybrid rice than in inbred rice due to additional nonadditive effects such as dominance. Here, we describe a pipeline developed for joint analysis of phenotypes, effects, and generations (JPEG). As a demonstration, we analyze 12 grain quality traits of 113 inbred lines (male parents), five tester lines (female parents), and 565 (113×5) of their hybrids. We sequence the parents for single nucleotide polymorphisms calling and infer the genotypes of the hybrids. Genome-wide association studies with JPEG identify 128 loci associated with at least one of the 12 traits, including 44, 97, and 13 loci with additive effects, dominant effects, and both additive and dominant effects, respectively. These loci together explain more than 30% of the genetic variation in hybrid performance for each of the traits. The JEPG statistical pipeline can help to identify superior crosses for breeding rice hybrids with improved grain quality.https://doi.org/10.1038/s41467-023-39534-x |
| spellingShingle | Lanzhi Li Xingfei Zheng Jiabo Wang Xueli Zhang Xiaogang He Liwen Xiong Shufeng Song Jing Su Ying Diao Zheming Yuan Zhiwu Zhang Zhongli Hu Joint analysis of phenotype-effect-generation identifies loci associated with grain quality traits in rice hybrids Nature Communications |
| title | Joint analysis of phenotype-effect-generation identifies loci associated with grain quality traits in rice hybrids |
| title_full | Joint analysis of phenotype-effect-generation identifies loci associated with grain quality traits in rice hybrids |
| title_fullStr | Joint analysis of phenotype-effect-generation identifies loci associated with grain quality traits in rice hybrids |
| title_full_unstemmed | Joint analysis of phenotype-effect-generation identifies loci associated with grain quality traits in rice hybrids |
| title_short | Joint analysis of phenotype-effect-generation identifies loci associated with grain quality traits in rice hybrids |
| title_sort | joint analysis of phenotype effect generation identifies loci associated with grain quality traits in rice hybrids |
| url | https://doi.org/10.1038/s41467-023-39534-x |
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