Association Mapping of Seed Quality Traits Under Varying Conditions of Nitrogen Application in Brassica juncea L. Czern & Coss - Akhatar, Javed; Singh, Mohini Prabha; Sharma, Anju; Kaur, Harjeevan; Kaur, Navneet; Sharma, Sanjula; Bharti, Baudh; Sardana, V. K.; Banga, Surinder S. (Frontiers in Genetics, 2020)
Indian mustard (Brassica juncea) is a premier oilseed crop of Indian subcontinent and it is also an important source of vegetable proteins for cattle feed. We investigated the genetic architecture of oil, protein and glucosinolates by conducting a genome-wide association study (GWAS), using an association panel comprising 92 diverse genotypes. Trait phenotyping was conducted over two years at two levels of nitrogen (N) application. Genotyping by sequencing was used to identify 66,835 loci, spread over 18 chromosomes. Genetic diversity and phenotypic variations were generally high for the studied traits. Trait performances were generally stable, when averaged over years and N levels. However, individual performances differed as reflected in high genotype x environment interactions. Correlation analysis revealed negative correlation between oil and protein. Positive correlation existed between glucosinolate and protein contents. General and mixed linear model were used to estimate the association between the SNP markers and the seed quality traits. Population structure, principal components (PCs) analysis and discriminant analysis of principal components (DAPCs) analysis were used as covariates to overcome the bias due to the population stratification on outcomes from GWAS. Quantile-Quantile (QQ) plots were performed with -log10(P) of each observed SNP and expected P value. These allowed us to select best fit algorithm. The meta-analysis was conducted to test for differences in trait associations between N-levels over the years. Sixteen, 23 and 27 loci were detected to be associated with oil, protein and glucosinolates in multiple environments, respectively. Annotation of the genomic region around the identified SNPs allowed us to identify many important genes related to oil content and fatty acid synthesis. Important trait related genes such as LACS5 (oil biosynthesis), GlcNAc1pUT-1 (glycoprotein synthesis), ASN1 (nitrogen storage and transport), GTR2 (glucosinolate-specific transporter), CYP81G1, (glucosinolate metabolic process) and many more were predicted from our association study. The important loci contributing to the variation will be of use in the breeding programmes directed at improving oil and protein contents in B. juncea.