Abstract
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.
Highlights
Alfalfa (Medicago sativa L.) is an autotetraploid (2n = 4x = 32) perennial forage crop with a genome size of 800–1000 Mb [1]
Results indicated that estimated marker-variance-weighted (EVW)-genomic best linear unbiased prediction (GBLUP) was superior for traits controlled by loci of a large effect, and absolute value of the estimated markereffect-weighted (AEW)-GBLUP was better for traits controlled by loci with moderate effect [19]
Our results suggest that including single nucleotide polymorphism (SNP) marker −log10 p-values derived from the additive GWASpoly model in a Weighted Genomic Best Linear Unbiased Prediction (WGBLUP) model may benefit prediction accuracy and selection for improvement of complex traits in alfalfa breeding programs
Summary
GS is a promising alternative to determine the genetic potential or breeding value of an individual based on whole-genome markers (Figure 1a) This method follows the infinitesimal model, which assumes that a quantitative trait is determined by an infinite number of unlinked and non-epistatic loci, each one with a very small effect that satisfies normality and linearity [7]. This technique uses both parametric and non-parametric statistical models to determine associations of phenotypic trait values with genome-wide molecular markers. We demonstrate the implementation of GS models on a real dataset of alfalfa and potato to identify improved approaches to implement GS in different breeding programs
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
