Journal of Advances in Biology & Biotechnology

Aims: This study focused on validating previously identified SSR markers associated with quantitative trait loci (QTLs) related to tolerance for anaerobic germination (AG). Additionally, it aimed to evaluate the genetic diversity of 34 rice genotypes, which included 31 local deepwater Bao cultivars from Assam. The findings could be valuable for breeding programs aimed at enhancing flood resilience in rice.

Study Design: All genotypes were assessed under controlled anaerobic conditions during germination and were genotyped using 38 SSR markers linked to quantitative trait loci (QTL).

Methodology: Genomic DNA was extracted from seedlings that were 7 days old and then amplified with specific SSR markers. The polymorphism data were analyzed using DARwin 5.0 to create a genetic dendrogram. Additionally, the phenotypic performance of AG was correlated with the SSR profiles through simple linear regression to find notable associations between markers and traits.

Results: Out of the 38 SSR markers we tested, 19 showed two alleles, 16 showed three alleles, and three displayed four alleles, highlighting a notable level of polymorphism. When we conducted a cluster analysis, we were able to group the 34 genotypes into three main clusters, effectively distinguishing between tolerant and susceptible lines. Among the 13 Bao cultivars we examined, we found varying degrees of AG tolerance. Notably, Rangdha Kekua Bao achieved 80% tolerance, while Rangoli showed 50%, making them the closest to the tolerant controls. Additionally, regression analysis pinpointed two specific markers, RM553_170 (qAG9) and RM5378_160 (qAG5), that showed a significant association with AG tolerance, with a p-value of 0.05 or less.

Conclusion: RM553 and RM5378 have been confirmed as effective markers for assessing anaerobic germination tolerance in rice breeding. The tolerant Bao cultivars identified in this study offer promising resources for developing rice varieties that can thrive in direct-seeded and flood-prone environments. However, the research has some limitations, including a relatively small sample size and a focus on just one environmental condition. To enhance the utility of these markers, future studies should consider multi-location validations and functional analyses.