Journal of Integrative Agriculture ›› 2026, Vol. 25 ›› Issue (4): 1359-1372.DOI: 10.1016/j.jia.2024.07.026

• • 上一篇    下一篇

多性状全基因组关联分析揭示水稻产量及其相关性状的新型多效应位点

  

  • 收稿日期:2024-03-28 修回日期:2024-07-19 接受日期:2024-06-07 出版日期:2026-04-20 发布日期:2026-03-09

Multi-trait genome-wide association studies reveal novel pleiotropic loci associated with yield and yield-related traits in rice

Chunhai Liu1*, Chao Wu1*, Zheming Yuan1, Bingchuan Tian2, Peiyi Yu2, Deze Xu3, Xingfei Zheng3, Lanzhi Li1#   

  1. 1 Hunan Engineering & Technology Research Center for Agricultural Big Data Analysis & Decision-making/College of Plant Protection, Hunan Agricultural University, Changsha 410128, China

    2 Huazhi Biotechnology Co. Ltd., Changsha 410125, China

    3 Hubei Key Laboratory of Food Crop Germplasm and Genetic Improvement/Food Crop Institute, Hubei Academy of Agricultural Sciences, Wuhan 430064, China

  • Received:2024-03-28 Revised:2024-07-19 Accepted:2024-06-07 Online:2026-04-20 Published:2026-03-09
  • About author:Chunhai Liu, E-mail: chunhai.liu@stu.hunau.edu.cn; Chao Wu, E-mail: 1481014337@qq.com; #Correspondence Lanzhi Li, Tel: +86-731-84618163, E-mail: lancy0829@163.com * These authors contributed equally to this study.
  • Supported by:
    This work was supported by the Science and Technology Innovation Program of Hunan Province, China (2023NK2001), the Hubei Key Laboratory of Food Crop Germplasm and Genetic Improvement, China (2022LZJJ08), and the Changsha Science and Technology Project, China (kq2402109). 

摘要:

水稻产量是一个受多个相关性状影响的复杂性状。传统的单性状全基因组关联分析(GWAS)在研究复杂性状时存在局限性,无法考虑多个性状之间的遗传关系。相比之下,多性状GWAS能够同时考虑多个性状之间的关系,识别多效应位点,因此更适合研究像水稻产量这样的复杂性状。本研究对575个杂交种在两个环境中的11对产量及其相关性状的双性状组合进行了多性状GWAS分析。这些相关性状均与产量(YD)呈显著的遗传相关性,包括每穗实粒数(FGPP)、千粒重(KGW)、每株分蘖数(TP)、一次枝梗数(PB)、二次枝梗数(SB)和主穗长度(MPL。共鉴定了44个多效应数量性状位点(pQTL),其中29个为单性状GWAS未发现的新pQTL。筛选出23个在双性状中表现出同向效应的pQTL作为关键pQTL,并通过单倍型分析鉴定到13个多效应候选基因。最终,通过聚合优势单倍型,鉴定出两个最佳增产等位基因型组合:YD-KGW组合的GS3-GL3.1-OsCIPK17,以及YD-FGPPYD-SB组合的GNP12。本研究提供了在产量及产量相关性状均表现优势差异的多效应候选基因和等位基因组合,为未来高产水稻的育种提供了宝贵的信息。

Abstract:

Rice yield is a complex trait affected by many related traits.  Traditional single-trait genome-wide association studies (GWAS) have limitations when studying complex traits, as they cannot account for the genetic relationships among multiple traits.  Multi-trait GWAS can consider the relationships among multiple traits and identify pleiotropic loci, so it is more suitable for complex traits such as rice yield than single-trait GWAS.  In this study, we conducted a multi-trait GWAS on 11 two-trait combinations of yield and yield-related traits with 575 hybrid rice varieties across two environments.  All these yield-related traits showed significant genetic correlations with yield (YD), including filled grains per panicle (FGPP), 1,000-grain weight (KGW), tillers per plant (TP), primary branch number (PB), secondary branch number (SB), and main panicle length (MPL).  In total, we identified 44 pleiotropic quantitative trait loci (pQTLs), including 29 new pQTLs not found in a single-trait GWAS.  We then screened 23 pQTLs showing common effects in two traits as key pQTLs.  These key pQTLs were subsequently analyzed by haplotype analysis, which identified 13 pleiotropic candidate genes.  Finally, we identified two optimal yield-enhancing allele combinations by pyramiding the superior haplotypes: GS3-GL3.1-OsCIPK17 for the YD-KGW combination and GNP12 for the YD-FGPP and YD-SB combinations.  This study provides pleiotropic candidate genes and allele combinations that exhibit superior differences in both yield and yield-related traits, offering valuable information for future high-yielding rice breeding.

Key words: rice ,  yield ,  multi-trait GWAS ,  genetic correlation ,  pleiotropic SNP