Abstract
The peanut or groundnut (Arachis hypogaea L.) is a tetraploid legume, which originated around 4,000-6,000 years ago through the hybridization event between A. duranensis (A-genome) and A. ipaensis (B-genome), resulting in AABB genomic composition. Limited genetic diversity stemming from a short evolutionary history and hybridization barriers has impeded the development of extensive marker resources. To enhance peanut adaptability and resilience, a solution involving integrating diverse germplasm, prebreeding, and genomics would be required. Germplasm, encompassing wild relatives and landraces, offers essential genetic diversity for enhancing disease resistance and environmental adaptability. The Peanut Genome Consortium (PGC) strives to create high-quality reference genomes, analyze transcriptomes, and identify correlations between traits and markers to aid molecular breeding. This integration facilitates the transfer of beneficial wild-relative traits into cultivated varieties by utilizing marker-assisted selection and advanced phenotyping techniques. The approach conserves local landraces and wild species and strengthens genetic diversity and resilience. Genome sequencing advances have propelled high-resolution trait mapping and candidate gene identification. Single nucleotide polymorphisms (SNPs) are favored markers due to their prevalence. The availability of reference genomes for A. duranensis and A. ipaensis have enabled next-generation sequencing, empowering diverse genetic and breeding applications. While simple sequence repeat (SSR) markers remain important, cost-effective SNP genotyping platforms are under development. Peanut breeding targets challenges like drought, aflatoxin contamination, and oil content. Integration of sequencing technologies, precise phenotyping, and trait-focused research is pivotal to carry out effective breeding programmes to address the challenges associated with peanuts. The future of peanut genomics and molecular tools holds potential for addressing varied production and quality constraints.
