GWAS revealed a novel resistance locus on chromosome 4D for the quarantine disease Karnal bunt in diverse wheat pre-breeding germplasm.

This study was initiated to identify genomic regions conferring resistance to Karnal Bunt (KB) disease in wheat through a genome-wide association study (GWAS) on a set of 179 pre-breeding lines (PBLs). A GWAS of 6,382 high-quality DArTseq SNPs revealed 15 significant SNPs (P-value <10-3) on chromosomes 2D, 3B, 4D and 7B that were associated with KB resistance in individual years. In particular, two SNPs (chromosome 4D) had the maximum R2 values: SNP 1114200 | F | 0-63:T > C at 1.571 cM and R2 of 12.49% and SNP 1103052 | F | 0-61:C > A at 1.574 cM and R2 of 9.02%. These two SNPs displayed strong linkage disequilibrium (LD). An in silico analysis of SNPs on chromosome 4D identified two candidate gene hits, TraesCS4D02G352200 (TaNox8; an NADPH oxidase) and TraesCS4D02G350300 (a rhomboid-like protein belonging to family S54), with SNPs 1103052 | F | 0-61:C > A and 1101835 | F | 0-5:C > A, respectively, both of which function in biotic stress tolerance. The epistatic interaction analysis revealed significant interactions among 4D and 7B loci. A pedigree analysis of confirmed resistant PBLs revealed that Aegilops species is one of the parents and contributed the D genome in these resistant PBLs. These identified lines can be crossed with any elite cultivar across the globe to incorporate novel KB resistance identified on 4B.

Genomic prediction models for grain yield of spring bread wheat in diverse agro-ecological zones.

Genomic and pedigree predictions for grain yield and agronomic traits were carried out using high density molecular data on a set of 803 spring wheat lines that were evaluated in 5 sites characterized by several environmental co-variables. Seven statistical models were tested using two random cross-validations schemes. Two other prediction problems were studied, namely predicting the lines' performance at one site with another (pairwise-site) and at untested sites (leave-one-site-out). Grain yield ranged from 3.7 to 9.0 t ha(-1) across sites. The best predictability was observed when genotypic and pedigree data were included in the models and their interaction with sites and the environmental co-variables. The leave-one-site-out increased average prediction accuracy over pairwise-site for all the traits, specifically from 0.27 to 0.36 for grain yield. Days to anthesis, maturity, and plant height predictions had high heritability and gave the highest accuracy for prediction models. Genomic and pedigree models coupled with environmental co-variables gave high prediction accuracy due to high genetic correlation between sites. This study provides an example of model prediction considering climate data along-with genomic and pedigree information. Such comprehensive models can be used to achieve rapid enhancement of wheat yield enhancement in current and future climate change scenario.