Genome-Wide Association Mapping and Genomic Selection for Alfalfa (Medicago sativa) Forage Quality Traits

Abstract

Genetic progress for forage quality has been poor in alfalfa (Medicago sativa L.), the most-grown forage legume worldwide. This study aimed at exploring opportunities for marker-assisted selection (MAS) and genomic selection of forage quality traits based on breeding values of parent plants. Some 154 genotypes from a broadly-based reference population were genotyped by genotyping-by-sequencing (GBS), and phenotyped for leaf-to-stem ratio, leaf and stem contents of protein, neutral detergent fiber (NDF) and acid detergent lignin (ADL), and leaf and stem NDF digestibility after 24 hours (NDFD), of their dense-planted half-sib progenies in three growing conditions (summer harvest, full irrigation; summer harvest, suspended irrigation; autumn harvest). Trait-marker analyses were performed on progeny values averaged over conditions, owing to modest germplasm × condition interaction. Genomic selection exploited 11,450 polymorphic SNP markers, whereas a subset of 8,494 M. truncatula-aligned markers were used for a genome-wide association study (GWAS). GWAS confirmed the polygenic control of quality traits and, in agreement with phenotypic correlations, indicated substantially different genetic control of a given trait in stems and leaves. It detected several SNPs in different annotated genes that were highly linked to stem protein content. Also, it identified a small genomic region on chromosome 8 with high concentration of annotated genes associated with leaf ADL, including one gene probably involved in the lignin pathway. Three genomic selection models, i.e., Ridge-regression BLUP, Bayes B and Bayesian Lasso, displayed similar prediction accuracy, whereas SVR-lin was less accurate. Accuracy values were moderate (0.3–0.4) for stem NDFD and leaf protein content, modest for leaf ADL and NDFD, and low to very low for the other traits. Along with previous results for the same germplasm set, this study indicates that GBS data can be exploited to improve both quality traits (by genomic selection or MAS) and forage yield.

Evaluation of Different Fall Dormancy-Rating Alfalfa Cultivars for Forage Yield in a Semiarid Environment

“…Winter survival might play tremendous role in the alfalfa yield in consecutive years mostly under the semiarid dry climate of the north western region of New Mexico and similar regions. Wang et al [47] reported that the greatest average annual forage yield of 24.4 Mg ha −1 was achieved with 'Runner' (FD2), while the smallest yields were found in 'Defi' (FD5) with no statistical difference in annual forage yields of varieties among FD ratings 3 and 5-9 from the evaluation of 42 varieties of eight fall dormancy-ratings (2)(3)(4)(5)(6)(7)(8)(9). They pointed out the importance of early season management to achieve great annual total forage yields rather than considering fall dormancy-ratings as the main criteria for alfalfa variety choice and adoption in temperate regions.…”

“…The moderately dormant and the non-dormant cultivars showed the highest yield during the first harvest year while the very dormant cultivars and dormant cultivars had the lowest forage yield. Alfalfa cultivars with a fall dormancy range 4-5 may be considered for alfalfa production in northwest New Mexico however, the good agricultural practices (conservation tillage, fertilizer management based on soil residual available nutrient and crop requirement, recommended planting rate, weed and pest management, irrigation scheduling to match crop evapotranspiration) should be the most important to maximize alfalfa forage yield in the southwest US.advantages as it widely adapts to different climates, has good biological nitrogen fixation capacity and produces high biomass yield with exceptional nutritive value [3][4][5][6]. Fall dormancy is defined as the reduction in shoot growth in the autumn due to decreasing day length and air temperatures [7].Fall dormancy is considered an adaptation trait to different climatic conditions [8].…”

“…advantages as it widely adapts to different climates, has good biological nitrogen fixation capacity and produces high biomass yield with exceptional nutritive value [3][4][5][6]. Fall dormancy is defined as the reduction in shoot growth in the autumn due to decreasing day length and air temperatures [7].…”

Insights Into the Genetic Basis of Blueberry Fruit-Related Traits Using Diploid and Polyploid Models in a GWAS Context

“…We also tested "diploidized models" or "pseudodiploid models" using the tetraploid genotype calling, as they are widely-used in polyploid analyses due to straightforward implementation in diploid software (Li et al, 2014b;Biazzi et al, 2017). This parameterization disregards the allele dosage and all heterozygotes are grouped into the same genotypic class, which is at the midpoint between the two homozygotes (Rosyara et al, 2016;Slater et al, 2016).…”

Genomic Predictive Ability for Foliar Nutritive Traits in Perennial Ryegrass

“…Over the last decade the declining cost of genotyping single nucleotide polymorphisms (SNPs), largely through reduced representation sequencing approaches such as genotyping-bysequencing (GBS) (Elshire et al 2011), has made this tool feasible for plant breeding. GS is now being applied in major crop species, including wheat (Rutkoski et al 2011;Poland et al 2012;Lopez-Cruz et al 2015;Hayes et al 2017), maize (Zhao et al 2012;Fristche-Neto et al 2018) and barley (Zhong et al 2009;Lorenz et al 2012) and is under adoption in forage species, including perennial ryegrass (Fè et al 2016;Grinberg et al 2016;Byrne et al 2017;Arojju et al 2018;Faville et al 2018;Pembleton et al 2018), and alfalfa (Annicchiarico et al 2015;Li et al 2015;Biazzi et al 2017;Jia et al 2018), principally in terms of assessing the influence of training set size and composition, trait characteristics and genotyping approaches on predictive ability. In perennial ryegrass, so far only two studies have evaluated the possibility of using genome-wide markers to predict GEBVs for nutritive quality traits.…”