Genome-Wide Association Studies Identifying Multiple Loci Associated With Alfalfa Forage Quality

Medicago2035: Genomes, functional genomics, and molecular breeding

Medicago, a genus in the Leguminosae or Fabaceae family, includes the most globally significant forage crops, notably alfalfa (Medicago sativa). Its close diploid relative Medicago truncatula serves as an exemplary model plant for investigating legume growth and development, as well as symbiosis with rhizobia. Over the past decade, advances in Medicago genomics have significantly deepened our understanding of the molecular regulatory mechanisms that underlie various traits. In this review, we comprehensively summarize research progress on Medicago genomics, growth and development (including compound leaf development, shoot branching, flowering time regulation, inflorescence development, floral organ development, and seed dormancy), resistance to abiotic and biotic stresses, and symbiotic nitrogen fixation with rhizobia, as well as molecular breeding. We propose avenues for molecular biology research on Medicago in the coming decade, highlighting those areas that have yet to be investigated or that remain ambiguous.

Analyzing Medicago spp. seed morphology using GWAS and machine learning

Alfalfa is widely recognized as an important forage crop. To understand the morphological characteristics and genetic basis of seed morphology in alfalfa, we screened 318 Medicago spp., including 244 Medicago sativa subsp. sativa (alfalfa) and 23 other Medicago spp., for seed area size, length, width, length-to-width ratio, perimeter, circularity, the distance between the intersection of length & width (IS) and center of gravity (CG), and seed darkness & red-green-blue (RGB) intensities. The results revealed phenotypic diversity and correlations among the tested accessions. Based on the phenotypic data of M. sativa subsp. sativa, a genome-wide association study (GWAS) was conducted using single nucleotide polymorphisms (SNPs) called against the Medicago truncatula genome. Genes in proximity to associated markers were detected, including CPR1, MON1, a PPR protein, and Wun1(threshold of 1E-04). Machine learning models were utilized to validate GWAS, and identify additional marker-trait associations for potentially complex traits. Marker S7_33375673, upstream of Wun1, was the most important predictor variable for red color intensity and highly important for brightness. Fifty-two markers were identified in coding regions. Along with strong correlations observed between seed morphology traits, these genes will facilitate the process of understanding the genetic basis of seed morphology in Medicago spp.

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

Salinity and Phoma medicaginis infection represent significant challenges for alfalfa cultivation in South Africa, Europe, Australia, and, particularly, Tunisia. These constraints have a severe impact on both yield and quality. The primary aim of this study was to establish the genetic basis of traits associated with biomass and growth of 129 Medicago sativa genotypes through genome-wide association studies (GWAS) under combined salt and P. medicaginis infection stresses. The results of the analysis of variance (ANOVA) indicated that the variation in these traits could be primarily attributed to genotype effects. Among the test genotypes, the length of the main stem, the number of ramifications, the number of chlorotic leaves, and the aerial fresh weight exhibited the most significant variation. The broad-sense heritability (H²) was relatively high for most of the assessed traits, primarily due to genetic factors. Cluster analysis, applied to morpho-physiological traits under the combined stresses, revealed three major groups of accessions. Subsequently, a GWAS analysis was conducted to validate significant associations between 54,866 SNP-filtered single-nucleotide polymorphisms (SNPs) and seven traits. The study identified 27 SNPs that were significantly associated with the following traits: number of healthy leaves (two SNPs), number of chlorotic leaves (five SNPs), number of infected necrotic leaves (three SNPs), aerial fresh weight (six SNPs), aerial dry weight (nine SNPs), number of ramifications (one SNP), and length of the main stem (one SNP). Some of these markers are related to the ionic transporters, cell membrane rigidity (related to salinity tolerance), and the NBS_LRR gene family (associated with disease resistance). These findings underscore the potential for selecting alfalfa genotypes with tolerance to the combined constraints of salinity and P. medicaginis infection.

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

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.

Ideotype breeding and genome engineering for legume crop improvement

Ideotype breeding is a strategy whereby traits are modeled a priori and then introduced into a model or crop species to assess their impact on yield. Thus, knowledge about the connection between genotype and phenotype is required for ideotype breeding to be deployed successfully. The growing understanding of the genetic basis of yield-related traits, combined with increasingly efficient genome engineering tools, improved transformation efficiency, and high-throughput genotyping of regenerants paves the way for the widespread adoption of ideotype breeding as a complement to conventional breeding. We briefly discuss how ideotype breeding, coupled with such state-of-the-art biotechnological tools, could contribute to knowledge-based legume breeding and accelerate yield gains to ensure food security in the coming decades.

Identification of genetic loci associated with five agronomic traits in alfalfa using multi-environment trials

The use of multi-environment trials to test yield-related traits in a diverse alfalfa panel allowed to find multiple molecular markers associated with complex agronomic traits. Yield is one of the most important target traits in alfalfa breeding; however, yield is a complex trait affected by genetic and environmental factors. In this study, we used multi-environment trials to test yield-related traits in a diverse panel composed of 200 alfalfa accessions and varieties. Phenotypic data of maturity stage measured as mean stage by count (MSC), dry matter content, plant height (PH), biomass yield (Yi), and fall dormancy (FD) were collected in three locations in Idaho, Oregon, and Washington from 2018 to 2020. Single-trial and stagewise analyses were used to obtain estimated trait means of entries by environment. The plants were genotyped using a genotyping by sequencing approach and obtained a genotypic matrix with 97,345 single nucleotide polymorphisms. Genome-wide association studies identified a total of 84 markers associated with the traits analyzed. Of those, 29 markers were in noncoding regions and 55 markers were in coding regions. Ten significant SNPs at the same locus were associated with FD and they were linked to a gene annotated as a nuclear fusion defective 4-like (NFD4). Additional SNPs associated with MSC, PH, and Yi were annotated as transcription factors such as Cysteine3Histidine (C3H), Hap3/NF-YB family, and serine/threonine-protein phosphatase 7 proteins, respectively. Our results provide insight into the genetic factors that influence alfalfa maturity, yield, and dormancy, which is helpful to speed up the genetic gain toward alfalfa yield improvement.

Genome-wide association study identified the candidate genes associated with angel wing trait in Pekin duck

Angel wing is a developmental wing deformity that can influence breeding and reproduction in the commercial duck industry. The nutrition foundation of angel wing trait was initially explored, but the genetic basic remains poorly understood. In this study, we identified candidate genes and single-nucleotide polymorphisms (SNPs) associated with angel wing trait in Pekin ducks using a genome-wide association study (GWAS) and selective sweep analysis. The GWAS results showed that nine SNPs across five chromosomes were significantly correlated with the angel wing trait. In total, 468 selection signals were shown between the angel wing ducks and normal ducks, and these signals harbored 154 genes, which were enriched in the nervous system and metabolism. This study provides the new insights into the genetic factors that may influence duck angel wing.