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Strategies to Increase Prediction Accuracy in Genomic Selection of Complex Traits in Alfalfa (Medicago sativa L.)

1
United States Department of Agriculture-Agricultural Research Service, Plant Germplasm Introduction and Testing Research, Prosser, WA 99350, USA
2
Department of Plant and Environmental Sciences, New Mexico State University, Las Cruces, NM 88003, USA
*
Author to whom correspondence should be addressed.
Academic Editors: Francesco Mercati and Francesco Carimi
Cells 2021, 10(12), 3372; https://doi.org/10.3390/cells10123372
Received: 19 October 2021 / Revised: 19 November 2021 / Accepted: 24 November 2021 / Published: 30 November 2021
(This article belongs to the Special Issue Omics in Plant Genetics and Breeding)
Agronomic traits such as biomass yield and abiotic stress tolerance are genetically complex and challenging to improve through conventional breeding approaches. Genomic selection (GS) is an alternative approach in which genome-wide markers are used to determine the genomic estimated breeding value (GEBV) of individuals in a population. In alfalfa (Medicago sativa L.), previous results indicated that low to moderate 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, such as alfalfa and potato. Specifically, we used empirical data affiliated with alfalfa yield under salt stress to investigate approaches that use DNA marker importance values derived from machine learning models, and genome-wide association studies (GWAS) of marker-trait association scores based on different GWASpoly models, in weighted GBLUP analyses. This approach increased prediction accuracies from 50% to more than 80% for alfalfa yield under salt stress. Finally, we expended the weighted GBLUP approach to potato and analyzed 13 phenotypic traits and obtained similar results. This is the first report on alfalfa to use variable importance and GWAS-assisted approaches to increase the prediction accuracy of GS, thus helping to select superior alfalfa lines based on their GEBVs. View Full-Text
Keywords: genomic selection; WGBLUP; Medicago sativa genomic selection; WGBLUP; Medicago sativa
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    Doi: DOI: 10.20944/preprints202110.0305.v1
MDPI and ACS Style

Medina, C.A.; Kaur, H.; Ray, I.; Yu, L.-X. Strategies to Increase Prediction Accuracy in Genomic Selection of Complex Traits in Alfalfa (Medicago sativa L.). Cells 2021, 10, 3372. https://doi.org/10.3390/cells10123372

AMA Style

Medina CA, Kaur H, Ray I, Yu L-X. Strategies to Increase Prediction Accuracy in Genomic Selection of Complex Traits in Alfalfa (Medicago sativa L.). Cells. 2021; 10(12):3372. https://doi.org/10.3390/cells10123372

Chicago/Turabian Style

Medina, Cesar A., Harpreet Kaur, Ian Ray, and Long-Xi Yu. 2021. "Strategies to Increase Prediction Accuracy in Genomic Selection of Complex Traits in Alfalfa (Medicago sativa L.)" Cells 10, no. 12: 3372. https://doi.org/10.3390/cells10123372

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