Abstract:
Rice (Oryza sativa L.) is a staple crop vital to food security worldwide, particularly in South Asia. Enhancing its yield potential and
disease resistance through genomic tools is a pressing necessity for sustainable agriculture. This study aimed to identify genome-wide
Single Nucleotide Polymorphisms (SNPs) associated with key agronomic traits in Pakistani rice germplasm using Genotyping-By-Se
quencing (GBS) and Genome-Wide Association Studies (GWAS). Fourteen diverse rice varieties were genotyped using ApeKI-based
GBS, generating high-quality sequencing data. Following quality filtering and alignment to the MSU v6.0 Nipponbare reference genome,
a total of 208 highly significant SNPs (-log₁₀P ≥ 4) were identified. GWAS was conducted using a mixed linear model incorporating pop
ulation structure and kinship, while STRUCTURE analysis was employed to assess subpopulation stratification. Significant associations
were identified for fifteen agronomic and yield-related traits, including days to flowering, plant height, grain weight, and disease resistance.
Key pleiotropic loci such as OsGRb06041, OsGRg05186, and OsGRb22352 were linked with multiple traits. Population structure anal
ysis revealed two main genetic clusters (K=2), reflecting the divergence between traditional basmati types and improved cultivars. This
study delivers a robust genomic resource for Marker-Assisted Selection (MAS) and future functional studies in rice. It highlights specific
genomic regions with potential utility in breeding programs aimed at enhancing productivity, resilience, and quality traits in Pakistani rice
germplasm.
