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

Abstract

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.

Acknowledgements

This work was supported by the National Key Research and Development Program of China (2022YFF1003203), the Key Research Project of Ningxia Province for Alfalfa Breeding Program (2022BBF02029), and the Agricultural Science and Technology Innovation Program (ASTIP-IAS14).

Author contributions

J.M.K. and Q.C.Y. conceived and designed the experiments. X.Q.J., X.C.Z., M.X., M.N.L., F.Z., F.H., T.H.Y., C.W., T.G., and R.C.L. were involved in the association population construction and phenotyping. X.Q.J. performed genotyping, genomic and statistical analysis. X.Q.J. and X.C.Z. wrote the draft of the manuscript. X.C.Z., M.X., M.N.L., and F.Z. assisted in genotyping and data analysis. All authors have edited, reviewed and approved the manuscript.

Data availability

All RAD raw sequence data were uploaded to the National Genomics Data Center (NGDC, https://bigd.big.ac.cn/) under BioProject PRJCA018485 and NCBI Sequence Read Archive with Bioproject ID: PRJNA995892. The datasets used and analyzed during the current study are available from the corresponding author on reasonable request.

Conflict of interest

The authors declare that they have no conflicts of interest.

Supplementary Data

Supplementary data is available at Horticulture Research online.

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