Dr. Kiran B. GaikwadAMIT KUMAR MAZUMDERT-112832024-10-242024-10-242023https://krishikosh.egranth.ac.in/handle/1/5810215816Wheat (Triticum aestivum L. em. Thell) is the second most important global cereal crop, grown in diverse agro-climatic conditions. To sustainably increase wheat yield to meet the growing world population's food demand in the face of climate change, Conservation Agriculture (CA) is a promising approach. However, the adoption of CA is slow, primarily due to the lack of wheat varieties specifically designed for it. Previous studies have mainly focused on understanding genotype, tillage, and genotype-tillage interactions, with limited research on how wheat adapts to optimal CA conditions. Additionally, there's a lack of genomic studies investigating the genetic basis of crop adaptation to CA. To dissect the genetic architecture of 19 morpho-physiological traits that could be involved in the enhanced adaptation and performance of genotypes under CA, we performed genome wide association studies (GWAS) to identify marker trait associations (MTAs) under four contrasting production regimes viz., conventional tillage timely sown (CT-TS), conservation agriculture timely sown (CA-TS), conventional tillage late sown (CT-LS) and conservation agriculture late sown (CA-LS) using an association panel comprising of 183 advanced wheat breeding lines along with 5 checks. The physiological and agronomic evaluation of morpho-physiological traits under each production environment identified presence of ample amount of variations for the target traits among the experimental materials used in the present study. Through assessment of genotype by-environment (GxE) interaction for all morpho-physiological traits, the presence of GxE interaction was ascertained. The GGE biplot analysis summarised the best performing genotypes with high stability and mean for all target traits. Ideal genotypes for few of the important morpho-physiological traits identified as Phi2 (408), NPQ (311), RC (335), CTD (240), PS1 (424), DTH (359), DTM (165), TGW (404) and GY (434). The genotyping of the association panel was done using 35K Breeders’ Axiom array. The initial SNP data was filtered to obtained 9,771 highly informative SNPs which were utilised for further analyses. The population structure and kinship analyses identified the presence of two sub-populations in the association panel. Furthermore, LD decay rate was observed to be fastest for A subgenome (4.63Mb) followed by D subgenome (5.40Mb) and B subgenome (7.41Mb) with a whole genome LD decay of 3.75Mb. GWAS was performed using the BLINK model in R Studio. A - log10P value above 5.0 (Bonferroni threshold) was used as the significance criterion. In total, 80 MTAs were discovered for 19 morpho-physiological traits across the four production environments. CT-TS had the most significant MTAs (35), followed by CA-LS (25), CA-TS (11), and CT-LS (9). The phenotypic variation explained by these QTNs ranged from 2.15 to 40.22%. Gene annotation provided highly informative SNPs that associated with Phi2, NPQ, PS1 and RC which were linked with genes that play crucial roles in the physiological adaptation under both CA and CT. A highly significant SNP AX94651261 (9.43% PVE) was identified to be associated with Phi2, while two SNP markers AX94730536 (30.90% PVE) and AX94683305 (16.99% PVE) were associated with NPQ. Also, SNPs AX94476007 (19.45% PVE), AX94658713 (22.87%PVE) and AX94525104 (31.33% PVE) linked with stress tolerance were identified for DTH, DTM and GL, respectively. In addition, highly significant and informative SNPs were identified for DTH, DTM, PH, GY and GL being linked to genes, the products of which have been reported to play pivotal roles in stress tolerance.EnglishThesis