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The whole-genome dissection of root system architecture provides new insights for the genetic improvement of alfalfa (Medicago sativa L.)

文献类型： 外文期刊

作者： Jiang, Xueqian 1 ; Zeng, Xiangcui 1 ; Xu, Ming 1 ; Li, Mingna 1 ; Zhang, Fan 1 ; He, Fei 1 ; Yang, Tianhui 3 ; Wang, Chuan 3 ; Gao, Ting 3 ; Long, Ruicai 1 ; Yang, Qingchuan 1 ; Kang, Junmei 1 ;

作者机构： 1.Chinese Acad Agr Sci, Inst Anim Sci, Beijing 100193, Peoples R China

2.Qingdao Agr Univ, Coll Grassland Sci, Qingdao 266109, Shandong, Peoples R China

3.Ningxia Acad Agr & Forestry Sci, Inst Anim Sci, Yinchuan 750000, Ningxia, Peoples R China

4.Chinese Acad Agr Sci, Inst Grassland Res, Hohhot 010000, Inner Mongolia, Peoples R China

期刊名称：HORTICULTURE RESEARCH （ 影响因子：8.5； 五年影响因子：9.1 ）

ISSN： 2662-6810

年卷期： 2025 年 12 卷 1 期

页码：

收录情况： SCI

摘要： Appropriate root system architecture (RSA) can improve alfalfa yield, yet its genetic basis remains largely unexplored. This study evaluated six RSA traits in 171 alfalfa genotypes grown under controlled greenhouse conditions. We also analyzed five yield-related traits in normal and drought stress environments and found a significant correlation (0.50) between root dry weight (RDW) and alfalfa dry weight under normal conditions (N_DW). A genome-wide association study (GWAS) was performed using 1 303 374 single-nucleotide polymorphisms (SNPs) to explore the relationships between RSA traits. Sixty significant SNPs (-log10(P) >= 5) were identified, with genes within the 50 kb upstream and downstream ranges primarily enriched in GO terms related to root development, hormone synthesis, and signaling, as well as morphological development. Further analysis identified 19 high-confidence candidate genes, including AUXIN RESPONSE FACTORs (ARFs), LATERAL ORGAN BOUNDARIES-DOMAIN (LBD), and WUSCHEL-RELATED HOMEOBOX (WOX). We verified that the forage dry weight under both normal and drought conditions exhibited significant differences among materials with different numbers of favorable haplotypes. Alfalfa containing more favorable haplotypes exhibited higher forage yields, whereas favorable haplotypes were not subjected to human selection during alfalfa breeding. Genomic prediction (GP) utilized SNPs from GWAS and machine learning for each RSA trait, achieving prediction accuracies ranging from 0.70 for secondary root position (SRP) to 0.80 for root length (RL), indicating robust predictive capability across the assessed traits. These findings provide new insights into the genetic underpinnings of root development in alfalfa, potentially informing future breeding strategies aimed at improving yield.

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