Ananta Acharya

A Saturated Genetic Linkage Map of Autotetraploid Alfalfa (Medicago sativa L.) Developed Using Genotyping-by-Sequencing Is Highly Syntenous with the Medicago truncatula Genome

A genetic linkage map is a valuable tool for quantitative trait locus mapping, map-based gene cloning, comparative mapping, and whole-genome assembly. Alfalfa, one of the most important forage crops in the world, is autotetraploid, allogamous, and highly heterozygous, characteristics that have impeded the construction of a high-density linkage map using traditional genetic marker systems. Using genotyping-by-sequencing (GBS), we constructed low-cost, reasonably high-density linkage maps for both maternal and paternal parental genomes of an autotetraploid alfalfa F1 population. The resulting maps contain 3591 single-nucleotide polymorphism markers on 64 linkage groups across both parents, with an average density of one marker per 1.5 and 1.0 cM for the maternal and paternal haplotype maps, respectively. Chromosome assignments were made based on homology of markers to the M. truncatula genome. Four linkage groups representing the four haplotypes of each alfalfa chromosome were assigned to each of the eight Medicago chromosomes in both the maternal and paternal parents. The alfalfa linkage groups were highly syntenous with M. truncatula, and clearly identified the known translocation between Chromosomes 4 and 8. In addition, a small inversion on Chromosome 1 was identified between M. truncatula and M. sativa. GBS enabled us to develop a saturated linkage map for alfalfa that greatly improved genome coverage relative to previous maps and that will facilitate investigation of genome structure. GBS could be used in breeding populations to accelerate molecular breeding in alfalfa.

Development of an Alfalfa SNP Array and Its Use to Evaluate Patterns of Population Structure and Linkage Disequilibrium

A large set of genome-wide markers and a high-throughput genotyping platform can facilitate the genetic dissection of complex traits and accelerate molecular breeding applications. Previously, we identified about 0.9 million SNP markers by sequencing transcriptomes of 27 diverse alfalfa genotypes. From this SNP set, we developed an Illumina Infinium array containing 9,277 SNPs. Using this array, we genotyped 280 diverse alfalfa genotypes and several genotypes from related species. About 81% (7,476) of the SNPs met the criteria for quality control and showed polymorphisms. The alfalfa SNP array also showed a high level of transferability for several closely related Medicago species. Principal component analysis and model-based clustering showed clear population structure corresponding to subspecies and ploidy levels. Within cultivated tetraploid alfalfa, genotypes from dormant and nondormant cultivars were largely assigned to different clusters; genotypes from semidormant cultivars were split between the groups. The extent of linkage disequilibrium (LD) across all genotypes rapidly decayed to 26 Kbp at r2 = 0.2, but the rate varied across ploidy levels and subspecies. A high level of consistency in LD was found between and within the two subpopulations of cultivated dormant and nondormant alfalfa suggesting that genome-wide association studies (GWAS) and genomic selection (GS) could be conducted using alfalfa genotypes from throughout the fall dormancy spectrum. However, the relatively low LD levels would require a large number of markers to fully saturate the genome.

Association Mapping of Biomass Yield and Stem Composition in a Tetraploid Alfalfa Breeding Population

Alfalfa (Medicago sativa L.), an important forage crop that is also a potential biofuel crop, has advantages of high yield, high lignocellulose concentration in stems, and has low input costs. In this study, we investigated population structure and linkage disequilibrium (LD) patterns in a tetraploid alfalfa breeding population using genome‐wide simple sequence repeat (SSR) markers and identified markers related to yield and cell wall composition by association mapping. No obvious population structure was found in our alfalfa breeding population, which could be due to the relatively narrow genetic base of the founders and/or due to two generations of random mating. We found significant LD (p < 0.001) between 61.5% of SSR marker pairs separated by less than 1 Mbp. The observed large extent of LD could be explained by the effect of bottlenecking and selection or the high mutation rates of SSR markers. Total marker heterozygosity was positively related to biomass yield in each of five environments, but no relationship was noted for stem composition traits. Of a total of 312 nonrare (frequency >10%) alleles across the 71 SSR markers, 15 showed strong association (p < 0.005) with yield in at least one of five environments, and most of the 15 alleles were identified in multiple environments. Only one allele showed strong association with acid detergent fiber (ADF) and one allele with acid detergent lignin (ADL). Alleles associated with traits could be directly applied in a breeding program using marker‐assisted selection. However, based on our estimated LD level, we would need about 1000 markers to explore the whole alfalfa genome for association between markers and traits.

Prevalence of single nucleotide polymorphism among 27 diverse alfalfa genotypes as assessed by transcriptome sequencing

BackgroundAlfalfa, a perennial, outcrossing species, is a widely planted forage legume producing highly nutritious biomass. Currently, improvement of cultivated alfalfa mainly relies on recurrent phenotypic selection. Marker assisted breeding strategies can enhance alfalfa improvement efforts, particularly if many genome-wide markers are available. Transcriptome sequencing enables efficient high-throughput discovery of single nucleotide polymorphism (SNP) markers for a complex polyploid species.ResultThe transcriptomes of 27 alfalfa genotypes, including elite breeding genotypes, parents of mapping populations, and unimproved wild genotypes, were sequenced using an Illumina Genome Analyzer IIx. De novo assembly of quality-filtered 72-bp reads generated 25,183 contigs with a total length of 26.8 Mbp and an average length of 1,065 bp, with an average read depth of 55.9-fold for each genotype. Overall, 21,954 (87.2%) of the 25,183 contigs represented 14,878 unique protein accessions. Gene ontology (GO) analysis suggested that a broad diversity of genes was represented in the resulting sequences. The realignment of individual reads to the contigs enabled the detection of 872,384 SNPs and 31,760 InDels. High resolution melting (HRM) analysis was used to validate 91% of 192 putative SNPs identified by sequencing. Both allelic variants at about 95% of SNP sites identified among five wild, unimproved genotypes are still present in cultivated alfalfa, and all four US breeding programs also contain a high proportion of these SNPs. Thus, little evidence exists among this dataset for loss of significant DNA sequence diversity from either domestication or breeding of alfalfa. Structure analysis indicated that individuals from the subspecies falcata, the diploid subspecies caerulea, and the tetraploid subspecies sativa (cultivated tetraploid alfalfa) were clearly separated.ConclusionWe used transcriptome sequencing to discover large numbers of SNPs segregating in elite breeding populations of alfalfa. Little loss of SNP diversity was evident between unimproved and elite alfalfa germplasm. The EST and SNP markers generated from this study are publicly available at the Legume Information System (http://medsa.comparative-legumes.org/) and can contribute to future alfalfa research and breeding applications.

Genomic Prediction of Biomass Yield in Two Selection Cycles of a Tetraploid Alfalfa Breeding Population

Alfalfa (Medicago sativa L.) is a widely planted perennial forage legume grown throughout temperate and dry subtropical regions in the world. Long breeding cycles limit genetic improvement of alfalfa, particularly for complex traits such as biomass yield. Genomic selection (GS), based on predicted breeding values obtained using genome‐wide molecular markers, could enhance breeding efficiency in terms of gain per unit time and cost. In this study, we genotyped tetraploid alfalfa plants that had previously been evaluated for yield during two cycles of phenotypic selection using genotyping‐by‐sequencing (GBS). We then developed prediction equations using yield data from three locations. Approximately 10,000 single nucleotide polymorphism (SNP) markers were used for GS modeling. The genomic prediction accuracy of total biomass yield ranged from 0.34 to 0.51 for the Cycle 0 population and from 0.21 to 0.66 for the Cycle 1 population, depending on the location. The GS model developed using Cycle 0 as the training population in predicting total biomass yield in Cycle 1 resulted in accuracies up to 0.40. Both genotype × environment interaction and the number of harvests and years used to generate yield phenotypes had effects on prediction accuracy across generations and locations, Based on our results, the selection efficiency per unit time for GS is higher than phenotypic selection, although accuracies will likely decline across multiple selection cycles. This study provided evidence that GS can accelerate genetic gain in alfalfa for biomass yield.

Extensive genetic diversity is present within north American Switchgrass Germplasm

This was the largest panel of switchgrass genetic diversity generated to date. The Gulf coast of the United States is the center of genetic diversity for switchgrass. There was a genetic bottleneck in upland switchgrass.