Genome-wide DArTseq analysis reveals hidden genetic diversity and distinct trait-based lineages in traditional rice from Assam, Northeast India
Year: 2026
Type: preprint
Abstract: Northeast India is a hotspot of rice diversity, harbouring traditional rice genotypes with distinct grain-quality traits and adaptation to diverse agroecological conditions. In the present study, we investigated the genome-wide diversity, population structure, and trait-associated divergence among indigenous rice genotypes from Assam, Northeast India using 9,714 high-quality DArTseq SNP markers. Genome-wide analyses revealed extensive genetic variation and strong population subdivision, with phylogenetic, Bayesian clustering, and principal component analyses (PCA) consistently identifying three major genetic clusters and six ancestral subpopulations. Genetic structuring was driven predominantly by grain-quality traits, particularly aroma, stickiness (glutinous grain), and pigmentation, rather than geographic origin alone. Trait-specific identity-by-state analyses and chromosome-level investigations further revealed distinct patterns of allele sharing around genomic regions associated with fragrance, grain stickiness, and pigmentation, reflecting the influence of long-term farmer-mediated selection and local adaptation. Analysis of molecular variance showed that 60.49% of total genetic variation was distributed among subpopulations, accompanied by high levels of private allelic diversity, indicating restricted gene flow and independent evolutionary trajectories among rice groups. Aromatic Joha , glutinous Bora , and pigmented rice lineages emerged as genetically distinct reservoirs of unique variation. These findings provide new insights into the evolutionary diversification of Northeast Indian rice and establish a comprehensive genomic framework for germplasm conservation, genetic resource management, and the deployment of valuable alleles for grain quality, speciality rice markets, and climate-resilient rice breeding.
Source: Research Square
Language: English
Subfield: Genetics
Domain: Life Sciences
Open Access status: green
OpenAlex ID: W7225653713