Effects of SNP marker density and training population size on prediction accuracy in alfalfa (Medicago sativa L.) genomic selection

• Published in: The plant genome Vol. 17; no. 1; pp. e20431 - n/a
• Main Authors: Wang, Hu, Bai, Yuguang, Biligetu, Bill
• Format: Journal Article
• Language: English
• Published: United States John Wiley & Sons, Inc 01.03.2024; Wiley
• Subjects: Accuracy; Agricultural production; Bayesian analysis; Corn; Data collection; Genomes; Genomics; Genotype & phenotype; Genotyping; Medicago sativa; Polymorphism; Predictions; Principal components analysis; Proteins; Single-nucleotide polymorphism; Software; Canada; Western Canada
• ISSN: 1940-3372; 1940-3372
• DOI: 10.1002/tpg2.20431

Effects of individual single‐nucleotide polymorphism (SNP) markers and the size of “training” and “test” populations affect prediction accuracy in genomic selection (GS). This study evaluated 11 subsets of 4932 SNPs using six genetic additive methods to understand marker density in GS prediction in alfalfa (Medicago sativa L.). In the GS methods, the effect of “training” to “test” population size was also evaluated. Fourteen alfalfa populations sampled from long‐term grazing sites were genotyped using genotyping by sequencing for the identification of SNPs. These populations were also phenotyped for six agromorphological and three nutritive traits from 2018 to 2020. The accuracy of GS prediction improved across six GS methods when the ratio of “training” to “test” population size increased. However, the prediction accuracy of the six GS methods reduced to a range of −0.27 to 0.11 when random, uninformative SNPs were used. In this study, five Bayesian methods and ridge‐regression best linear unbiased prediction (rrBLUP) method had similar GS accuracies for “training” sets, but rrBLUP tended to outperform Bayesian methods in independent “test” sets when SNP subsets with high mean‐squared‐estimated‐marker effect were used. These findings can enhance the application of GS in alfalfa genetic improvement. Core Ideas Selecting single‐nucleotide polymorphism (SNP) markers based on trait‐dependent, high mean‐squared‐estimated‐marker effect slightly increased the prediction accuracy of six additive genomic selection (GS) methods. Of the best 100 SNPs identified, 80%–90% are situated within protein‐coding regions associated with candidate genes regulating plant growth. The accuracy of GS prediction slightly improved across six GS methods when the ratio of “training” to “test” population size increased. The GS prediction accuracy of five Bayesian methods and ridge‐regression best linear unbiased prediction is trait dependent. Plain Language Summary Effect of individual SNPs markers and size of “training” and “test” populations affect prediction accuracy in genomic selection (GS). This study evaluated 11 sub‐sets of 4,932 SNPs using six genetic additive methods to understand marker density in GS prediction in alfalfa (Medicago sativa L.) from long‐term grazing sites. The accuracy of GS prediction improved across six GS methods when the ratio of “training” to “test” population size increased. However, the prediction accuracy of the six GS methods reduced to ‐0.27 to 0.11 when random, uninformative SNPs were used. In this study, five Bayesian methods and rrBLUP method had similar GS accuracies for “training” sets, but rrBLUP tended to outperform Bayesian methods in independent “Test” sets when SNPs subset with high mean‐squared‐estimated‐marker effect were used. These findings can enhance application of GS in alfalfa genetic improvement.