|MEDINA, CESAR AUGUSTO - Washington State University|
|KAUR, HERPREET - New Mexico State University|
|RAY, IAN - New Mexico State University|
Submitted to: Cells
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 11/24/2021
Publication Date: 11/30/2021
Citation: Medina, C., Kaur, H., Ray, I., Yu, L. 2021. Strategies to increase prediction accuracy in genomic selection of complex traits in alfalfa (Medicago sativa L.). Cells. 10(12). Article 3372. https://doi.org/10.3390/cells10123372.
Interpretive Summary: The goal of alfalfa breeding is primarily to develop new varieties with high forage yield and quality. Breeding programs have focused on phenotypic recurrent selection, which is time consuming and labor intensive. A promising alternative, called genomic selection (GS), is indirect selection based on genome-wide genetic markers. GS uses statistical models to predict breeding values of individuals in breeding populations. In this paper, we tested different GS models and performed a case study in alfalfa using data from salt tolerance populations to increase the prediction accuracy of GS for selecting salt tolerant plants. This study demonstrated that GS can be used to effectively identify plants that are salt tolerant and can be used to develop improved alfalfa varieties.
Technical Abstract: Genomic selection (GS) is an alternative approach to phenotypic recurrent selection. GS uses genome-wide markers to determine the genomic estimated breeding value (GEBV) of individuals in a breeding population. In alfalfa, previous results indicated that low prediction accuracy values (<70%) were obtained in complex traits such as yield and abiotic stress resistance. There is a need to increase the prediction value in order to employ GS in breeding programs. In this paper we reviewed different statistic models and their applications in polyploid crops including alfalfa. We tested and validated a new approach called weighted GBLUP to increase the prediction accuracy using SNP markers associated with salt tolerance in alfalfa populations. This approach increased the prediction accuracy more than 80% of alfalfa yield under salt stress. This is the first report to use the weighted GBLUP approach to increase prediction accuracy of GEBV in alfalfa. This approach can be applied to breeding programs to accelerate selection gains in other polyploid crops.