Preprint Review Version 1 Preserved in Portico This version is not peer-reviewed

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

Version 1 : Received: 19 October 2021 / Approved: 21 October 2021 / Online: 21 October 2021 (12:35:16 CEST)

A peer-reviewed article of this Preprint also exists.

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. 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.

Journal reference: Cells 2021, 10, 3372
DOI: 10.3390/cells10123372

Abstract

Agronomic traits such as biomass yield and abiotic stress tolerance are genetically complex and challenging to improve by conventional breeding strategies. 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, 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 including alfalfa. Specifically, we used empirical data affiliated with alfalfa yield under salt stress to investigate approaches which 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. This is the first report in 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.

Keywords

Genomic selection; WGBLUP; Medicago sativa

Subject

BIOLOGY, Plant Sciences

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