Joint analysis of phenotype-effect-generation identifies loci associated with grain quality traits in rice hybrids

Lanzhi Li, Xingfei Zheng, Jiabo Wang, Xueli Zhang, Xiaogang He, Liwen Xiong, Shufeng Song, Jing Su, Ying Diao, Zheming Yuan, Zhiwu Zhang () and Zhongli Hu ()
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Lanzhi Li: Hunan Agricultural University
Xingfei Zheng: Food Crop Institute, Hubei Academy of Agricultural Sciences
Jiabo Wang: Southwest Minzu University
Xueli Zhang: Hunan Agricultural University
Xiaogang He: Hunan Agricultural University
Liwen Xiong: Hunan Agricultural University
Shufeng Song: Hunan Hybrid Rice Research Center, Hunan Academy of Agricultural Sciences
Jing Su: Hunan Agricultural University
Ying Diao: Wuhan Polytechnic University
Zheming Yuan: Hunan Agricultural University
Zhiwu Zhang: Washington State University
Zhongli Hu: Wuhan University

Nature Communications, 2023, vol. 14, issue 1, 1-9

Abstract: 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.

Date: 2023
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DOI: 10.1038/s41467-023-39534-x

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