Alfalfa genomic selection for different stress‐prone growing regions

Abstract

Alfalfa (Medicago sativa L.) selection for stress-prone regions has high priority for sustainable crop-livestock systems. This study assessed the genomic selection (GS) ability to predict alfalfa breeding values for drought-prone agricultural sites of Algeria, Morocco, and Argentina; managed-stress (MS) environments of Italy featuring moderate or intense drought; and one Tunisian site irrigated with moderately saline water. Additional aims were to investigate genotype × environment interaction (GEI) patterns and the effect on GS predictions of three single-nucleotide polymorphism (SNP) calling procedures, 12 statistical models that exclude or incorporate GEI, and allele dosage information. Our study included 127 genotypes from a Mediterranean reference population originated from three geographically contrasting populations, genotyped via genotyping-by-sequencing and phenotyped based on multi-year biomass dry matter yield of their dense-planted half-sib progenies. The GEI was very large, as shown by 27-fold greater additive genetic variance × environment interaction relative to the additive genetic variance and low genetic correlation for progeny yield responses across environments. The predictive ability of GS (using

Discovering new genes for alfalfa (Medicago sativa) growth and biomass resilience in combined salinity and Phoma medicaginis infection through GWAS

“…Our findings revealed a remarkable range of morpho-physiological variation within the examined collection of alfalfa genotypes when subjected to simultaneous stressors (salt and strain Pm8 infection) and, overall, the current results demonstrated significant genetic variation for most of the recorded traits. These findings are in accordance with the conclusions drawn by Annicchiarico et al. (2022) on the same population grown under drought stress.…”

“…Analyzing the morpho-physiological variation in alfalfa plants under stress conditions has been regarded as a reliable method for assessing its stress tolerance. This process represents a pivotal phase in the development of forthcoming breeding programs ( Annicchiarico, 2006 ; Annicchiarico et al., 2022 ). Salinity and Phoma medicaginis infection are major issues for alfalfa that affect its growth and productivity.…”

The Effect of Genome Parametrization and SNP Marker Subsetting on Genomic Selection in Autotetraploid Alfalfa

“…Alfalfa is known for its fairly high resilience to drought, but further genetic improvement of its drought tolerance would be needed in several regions because of the changing climate, a process that is challenged, among other reasons, by the difficulty of coping with an autotetraploid genome. In this study, the definitely lower genomic prediction ability for total dry matter yield observed in the drought-prone environment relative to the moisture-favorable one (0.17 vs. 0.41) agrees with earlier findings [7,32]. In particular, for a Mediterranean alfalfa reference population using diploid genome parametrization, a progressive decrease in predictive ability was found across the managed environments, ranging from moisture-favorable (0.35) to moderately stressed (0.26), to heavily stressed (0.03), as well as prediction abilities in the range of 0.12-0.23 for drought-prone agricultural environments.…”

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

“…Annicchiarico et al (2015) and indicated that GS can be more efficient than conventional plant selection in improving alfalfa forage yield if it can display a moderate to high prediction accuracy. However, many studies reported negative (−0.03), low (0.05-0.13), or moderate (0.28-0.51) GS accuracies for alfalfa forage yield and nutritive traits (Annicchiarico et al, 2022(Annicchiarico et al, , 2015He et al, 2022;Jia et al, 2018;Murad Leite Andrade et al, 2022). He et al (2022) reported prediction accuracy at a range of 0.11-0.70 for alfalfa forage yield measured over several years in multiple growth environments using two full-sib populations.…”

“…(2015) indicated that GS can be more efficient than conventional plant selection in improving alfalfa forage yield if it can display a moderate to high prediction accuracy. However, many studies reported negative (−0.03), low (0.05–0.13), or moderate (0.28‐0.51) GS accuracies for alfalfa forage yield and nutritive traits (Annicchiarico et al., 2022, 2015; He et al., 2022; Jia et al., 2018; Murad Leite Andrade et al., 2022). He et al.…”