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Abstract Background Prairie grass (Bromus catharticus) is a typical cool-season forage crop with high biomass production and fast growth rate during winter and spring. However, its genetic research and breeding has remained stagnant due to limited available genomic resources. The aim of this study was to generate large-scale genomic data using high-throughput transcriptome sequencing, and perform a preliminary validation of EST-SSR markers of B. catharticus. Results Eleven tissue samples including seeds, leaves, and stems were collected from a new high-yield strain of prairie grass BCS1103. A total of 257,773 unigenes were obtained, of which 193,082 (74.90%) were annotated. Comparison analysis between tissues identified 1803, 3030, and 1570 genes specifically and highly expressed in seed, leaf, and stem, respectively. A total of 37,288 EST-SSRs were identified from unigene sequences, and more than 80,000 primer pairs were designed. We synthesized 420 primer pairs and selected 52 ones with high polymorphisms to estimate genetic diversity and population structure in 24 B. catharticus accessions worldwide. Despite low diversity indicated by an average genetic distance of 0.364, the accessions from South America and Asia and wild accessions showed higher genetic diversity. Moreover, South American accessions showed a pure ancestry, while Asian accessions demonstrated mixed internal relationships, which indicated a different probability of gene flow. Phylogenetic analysis clustered the studied accessions into four clades, being consistent with phenotypic clustering results. Finally, Mantel analysis suggested the total phenotypic variation was mostly contributed by genetic component. Stem diameter, plant height, leaf width, and biomass yield were significantly correlated with genetic data (r > 0.6, P

Abstract Background Narrow genetic base, complex allo-tetraploid genome and presence of repetitive elements have led the discovery of single nucleotide polymorphisms (SNPs) in Brassica juncea (AABB; 2n = 4x = 36) at a slower pace. Double digest RAD (ddRAD) - a genome complexity reduction technique followed by NGS was used to generate a total of 23 million paired-end reads from three genotypes each of Indian (Pusa Tarak, RSPR-01 and Urvashi) and Exotic (Donskaja IV, Zem 1 and EC287711) genepools. Results Sequence data analysis led to the identification of 10,399 SNPs in six genotypes at a read depth of 10x coverage among the genotypes of two genepools. A total of 44 hyper-variable regions (nucleotide variation hotspots) were also found in the genome, of which 93% were found to be a part of coding genes/regions. The functionality of the identified SNPs was estimated by genotyping a subset of SNPs on MassARRAY® platform among a diverse set of B. juncea genotypes. SNP genotyping-based genetic diversity and population studies placed the genotypes into two distinct clusters based mostly on the place of origin. The genotypes were also characterized for six morphological traits, analysis of which revealed a significant difference in the mean values between Indian and Exotic genepools for six traits. The association analysis for six traits identified a total of 45 significant marker-trait associations on 11 chromosomes of A- and B- group of progenitor genomes. Conclusions Despite narrow diversity, the ddRAD sequencing was able to identify large number of nucleotide polymorphisms between the two genepools. Association analysis led to the identification of common SNPs/genomic regions associated between flowering and maturity traits, thereby underscoring the possible role of common chromosomal regions-harboring genes controlling flowering and maturity in Brassica juncea.

Background: Prairie grass (Bromus catharticus) is a typical cool-season forage crop with high biomass production and fast growth rate during winter and spring. However, its genetic research and breeding has remained stagnant due to limited available genomic resources. The aim of this study was to generate large-scale genomic data using high-throughput transcriptome sequencing, and perform a preliminary validation of EST-SSR markers of B. catharticus. Results: Eleven tissue samples including seeds, leaves, and stems were collected from a new high-yield strain of prairie grass BCS1103. A total of 257,773 unigenes were obtained, of which 193,082 (74.90%) were annotated. Comparison analysis between tissues identified 1803, 3030, and 1570 genes specifically and highly expressed in seed, leaf, and stem, respectively. A total of 37,288 EST-SSRs were identified from unigene sequences, and more than 80,000 primer pairs were designed. We synthesized 420 primer pairs and selected 52 ones with high polymorphisms to estimate genetic diversity and population structure in 24 B. catharticus accessions worldwide. Despite low diversity indicated by an average genetic distance of 0.364, the accessions from South America and Asia and wild accessions showed higher genetic diversity. Moreover, South American accessions showed a pure ancestry, while Asian accessions demonstrated mixed internal relationships, which indicated a different probability of gene flow. Phylogenetic analysis clustered the studied accessions into four clades, being consistent with phenotypic clustering results. Finally, Mantel analysis suggested the total phenotypic variation was mostly contributed by genetic component. Stem diameter, plant height, leaf width, and biomass yield were significantly correlated with genetic data (r > 0.6, P < 0.001), and might be used in the future selection and breeding. Conclusion: A genomic resource was generated that could benefit genetic and taxonomic studies, as well as molecular breeding for B. catharticus and its relatives in the future. [ABSTRACT FROM AUTHOR]

Background: Narrow genetic base, complex allo-tetraploid genome and presence of repetitive elements have led the discovery of single nucleotide polymorphisms (SNPs) in Brassica juncea (AABB; 2n = 4x = 36) at a slower pace. Double digest RAD (ddRAD) - a genome complexity reduction technique followed by NGS was used to generate a total of 23 million paired-end reads from three genotypes each of Indian (Pusa Tarak, RSPR-01 and Urvashi) and Exotic (Donskaja IV, Zem 1 and EC287711) genepools. Results: Sequence data analysis led to the identification of 10,399 SNPs in six genotypes at a read depth of 10x coverage among the genotypes of two genepools. A total of 44 hyper-variable regions (nucleotide variation hotspots) were also found in the genome, of which 93% were found to be a part of coding genes/regions. The functionality of the identified SNPs was estimated by genotyping a subset of SNPs on MassARRAY® platform among a diverse set of B. juncea genotypes. SNP genotyping-based genetic diversity and population studies placed the genotypes into two distinct clusters based mostly on the place of origin. The genotypes were also characterized for six morphological traits, analysis of which revealed a significant difference in the mean values between Indian and Exotic genepools for six traits. The association analysis for six traits identified a total of 45 significant marker-trait associations on 11 chromosomes of A- and B- group of progenitor genomes. Conclusions: Despite narrow diversity, the ddRAD sequencing was able to identify large number of nucleotide polymorphisms between the two genepools. Association analysis led to the identification of common SNPs/genomic regions associated between flowering and maturity traits, thereby underscoring the possible role of common chromosomal regions-harboring genes controlling flowering and maturity in Brassica juncea. [ABSTRACT FROM AUTHOR]

Background: Cotton germplasm resources contain beneficial alleles that can be exploited to develop germplasm adapted to emerging environmental and climate conditions. Accessions and lines have traditionally been characterized based on phenotypes, but phenotypic profiles are limited by the cost, time, and space required to make visual observations and measurements. With advances in molecular genetic methods, genotypic profiles are increasingly able to identify differences among accessions due to the larger number of genetic markers that can be measured. A combination of both methods would greatly enhance our ability to characterize germplasm resources. Recent efforts have culminated in the identification of sufficient SNP markers to establish high-throughput genotyping systems, such as the CottonSNP63K array, which enables a researcher to efficiently analyze large numbers of SNP markers and obtain highly repeatable results. In the current investigation, we have utilized the SNP array for analyzing genetic diversity primarily among cotton cultivars, making comparisons to SSR-based phylogenetic analyses, and identifying loci associated with seed nutritional traits. Results: The SNP markers distinctly separated G. hirsutum from other Gossypium species and distinguished the wild from cultivated types of G. hirsutum. The markers also efficiently discerned differences among cultivars, which was the primary goal when designing the CottonSNP63K array. Population structure within the genus compared favorably with previous results obtained using SSR markers, and an association study identified loci linked to factors that affect cottonseed protein content. Conclusions: Our results provide a large genome-wide variation data set for primarily cultivated cotton. Thousands of SNPs in representative cotton genotypes provide an opportunity to finely discriminate among cultivated cotton from around the world. The SNPs will be relevant as dense markers of genome variation for association mapping approaches aimed at correlating molecular polymorphisms with variation in phenotypic traits, as well as for molecular breeding approaches in cotton. [ABSTRACT FROM AUTHOR]