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Genome wide association studies allowed prediction of 17 candidate genes for association with nitrogen use efficiency. Novel information obtained may provide better understanding of genomic controls underlying germplasm variations for this trait in Indian mustard. Nitrogen use efficiency (NUE) of Indian mustard (Brassica juncea (L.) Czern & Coss.) is low and most breeding efforts to combine NUE with crop performance have not succeeded. Underlying genetics also remain unexplored. We tested 92 SNP-genotyped inbred lines for yield component traits, N uptake efficiency (NUPEFF), nitrogen utilization efficiency (NUTEFF), nitrogen harvest index (NHI) and NUE for two years at two nitrogen doses (No without added N and N100 added @100 kg/ha). Genotypes IC-2489-88, M-633, MCP-632, HUJM 1080, GR-325 and DJ-65 recorded high NUE at low N. These also showed improved crop performance under high N. One determinate mustard genotype DJ-113 DT-3 revealed maximum NUTEFF. Genome wide association studies (GWAS) facilitated recognition of 17 quantitative trait loci (QTLs). Environment specificity was high. B-genome chromosomes (B02, B03, B05, B07 and B08) harbored many useful loci. We also used regional association mapping (RAM) to supplement results from GWAS. Annotation of the genomic regions around peak SNPs helped to predict several gene candidates for root architecture, N uptake, assimilation and remobilization. CAT9 (At1g05940) was consistently envisaged for both NUE and NUPEFF. Major N transporter genes, NRT1.8 and NRT3.1 were predicted for explaining variation for NUTEFF and NUPEFF, respectively. Most significant amino acid transporter gene, AAP1 appeared associated with NUE under limited N conditions. All these candidates were predicted in the regions of high linkage disequilibrium. Sequence information of the predicted candidate genes will permit development of molecular markers to aid breeding for high NUE.

Timely transition to flowering, maturity and plant height are important for agronomic adaptation and productivity of Indian mustard (B. juncea), which is a major edible oilseed crop of low input ecologies in Indian subcontinent. Breeding manipulation for these traits is difficult because of the involvement of multiple interacting genetic and environmental factors. Here, we report a genetic analysis of these traits using a population comprising 92 diverse genotypes of mustard. These genotypes were evaluated under deficient (N75), normal (N100) or excess (N125) conditions of nitrogen (N) application. Lower N availability induced early flowering and maturity in most genotypes, while high N conditions delayed both. A genotyping-by-sequencing approach helped to identify 406,888 SNP markers and undertake genome wide association studies (GWAS). 282 significant marker-trait associations (MTA's) were identified. We detected strong interactions between GWAS loci and nitrogen levels. Though some trait associated SNPs were detected repeatedly across fertility gradients, majority were identified under deficient or normal levels of N applications. Annotation of the genomic region (s) within ± 50 kb of the peak SNPs facilitated prediction of 30 candidate genes belonging to light perception, circadian, floral meristem identity, flowering regulation, gibberellic acid pathways and plant development. These included over one copy each of AGL24, AP1, FVE, FRI, GID1A and GNC. FLC and CO were predicted on chromosomes A02 and B08 respectively. CDF1, CO, FLC, AGL24, GNC and FAF2 appeared to influence the variation for plant height. Our findings may help in improving phenotypic plasticity of mustard across fertility gradients through marker-assisted breeding strategies.

Indian mustard (Brassica juncea) is a major source of vegetable oil in the Indian subcontinent. The seed cake left after the oil extraction is used as livestock feed. We examined the genetic architecture of oil, protein, and glucosinolates by conducting a genome-wide association study (GWAS), using an association panel comprising 92 diverse genotypes. We conducted trait phenotyping over 2 years at two levels of nitrogen (N) application. Genotyping by sequencing was used to identify 66,835 loci, covering 18 chromosomes. Genetic diversity and phenotypic variations were high for the studied traits. Trait performances were stable when averaged over years and N levels. However, individual performances differed. General and mixed linear models 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) were included as covariates to overcome the bias due to the population stratification. We identified 16, 23, and 27 loci associated with oil, protein, and glucosinolates, respectively. We also established LD patterns and haplotype structures for the candidate genes. The average block sizes were larger on A-genome chromosomes as compared to the B- genome chromosomes. Genetic associations differed over N levels. However, meta-analysis of GWAS datasets not only improved the power to recognize associations but also helped to identify common SNPs for oil and protein contents. Annotation of the genomic region around the identified SNPs led to the prediction of 21 orthologs of the functional candidate genes related to the biosynthesis of oil, protein, and glucosinolates. Notable among these are: LACS5 (A09), FAD6 (B05), ASN1 (A06), GTR2 (A06), CYP81G1 (B06), and MYB44 (B06). The identified loci will be very useful for marker-aided breeding for seed quality modifications in B. juncea.