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ARS Home » Pacific West Area » Pullman, Washington » Plant Germplasm Introduction and Testing Research » Research » Publications at this Location » Publication #394982
Research Project: Enhancing Resistance to Biotic and Abiotic Stresses in Alfalfa

Location: Plant Germplasm Introduction and Testing Research

Title: Accuracy of genomic selection for alfalfa biomass yield in two full-sib populations

Author
item HE, XIAOFAN - Beijing Forestry University
item ZHANG, FAN - China Agricultural University
item HE, FEI - China Agricultural University
item SHEN, YUHUA - Chifeng University
item Yu, Long-Xi
item ZHANG, TIEJUN - Beijing Forestry University
item KANG, JUMEI - Chinese Academy Of Agricultural Sciences

Submitted to: Frontiers in Plant Science
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 10/4/2022
Publication Date: 10/28/2022
Citation: He, X., Zhang, F., He, F., Shen, Y., Yu, L., Zhang, T., Kang, J. 2022. Accuracy of genomic selection for alfalfa biomass yield in two full-sib populations. Frontiers in Plant Science. 13. Article 1037272. https://doi.org/10.3389/fpls.2022.1037272.
DOI: https://doi.org/10.3389/fpls.2022.1037272

Interpretive Summary: Medicago are legumes related to common clovers, and alfalfa (Medicago sativa) is one of the most commercially important forage crops in the world. In this study, we developed two alfalfa populations with 149 and 392 individual plants by crossing two pairs of parents with differences in yield traits. The two populations were genotyped to provide us with molecular genetic markers, which help us determine where important genes are on the chromosomes and what effect they have on the traits. The yield traits of the populations were measured for several years in multiple environments. Genotypic and phenotypic data from multi-year and multi-point alfalfa yields were analyzed together for genomic selection, which chooses plants based on predicted yield using markers, rather than actual yield. The prediction accuracy was moderately good for both populations. The accuracy of the prediction models for the same location was greater than that of the cross-location. The results suggest that selection based on genomic prediction can be more efficient than selection based on measuring the traits in the field, which can accelerate the selection cycle for improvement of alfalfa yield traits.

Technical Abstract: The complexity of alfalfa genetic background has hindered efforts to improve yield attributes via conventional breeding methods such as phenotypic selection. Genomic selection (GS) can significantly improve breeding efficiency by using high-density molecular markers that cover the whole genome to assess genomic breeding values. In this study, two full-sib populations, consisting of 149 and 392 individual plants (P149 and P392, respectively), were constructed using parents with differences in yield traits, and the yield traits of F1 populations were measured for several years in multiple environments. The two populations were genotyped using GBS and RAD-seq techniques, respectively, and 47,367 and 161,170 SNP markers were obtained. The genotypic and phenotypic data from multi-year and multi-point alfalfa yields were combined and analyzed for yield prediction using various prediction models. The prediction accuracy was 0.11 to 0.69 for the P149 population, and 0.42 to 0.64 for the 392 population. The accuracy of the prediction models for the same location was greater than that of the cross-location. The accuracy of the prediction models for P392 was higher than that of P149. The result suggests that GS selection was more efficient than phenotypic selection, and can accelerate the breeding cycle for improvement of alfalfa yield traits.