Genetic diversity and population structure of the USDA collection of Brassica juncea L.

The success of plant biofuels relies on finding inexpensive feedstocks that do not compete with food crops and can be cultivated economically in diverse geographical regions and agricultural production systems. Brassica juncea L. is a native crop of the western and central Asia, is considered a good biodiesel candidate due to its high oil content with high unsaturated fatty acids that can be refined into biofuels that equal petroleum-based fuels characteristics. To build genomic resources for B.juncea, a diversity panel consisting of 340 of accessions, were collected from 22 countries and stored at the USDA repository, and were genotyped to explore genetic diversity, relatedness, and population structure. A total of 99030 high-quality single nucleotide polymorphisms (SNP) markers were identified using genotyping-by-sequencing (GBS) technology. Those SNP were distributed over the 18 chromosomes with an average of 5000 SNP per chromosome, an average polymorphism information content (PIC) value of 0.23 and an expected heterozygosity (He) value of 0.281 indicating the genetic diversity within the USDA collection of B. juncea. Population structure and principal components analyses (PCA) based on identified SNPs revealed five distinct subpopulations, with the largest subpopulation containing accessions traced back to Pakistan and India. Analysis of molecular variance (AMOVA) revealed that 40% of the variation in USDA collection was among the five subpopulations, while 49% of the variation was due to the variation among accessions used in the analysis. High fixation index (FST) among distinguished subpopulations indicates a wide genetic diversity and high genetic differentiation among subpopulations. The results explored the genetic diversity in the USDA collection of B. juncea that could be used to genetically improve the crop. This information and accessions provide tools to enhance genetic gain in B. juncea breeding programs through genome-wide association analysis studies (GWAS) and marker-assisted selection (MAS) approaches.