Genetic Variability in Groundnut: Decoding Effects on Yield, Disease Resistance and Nut Quality
A. Rupa *
Department of Genetics and Plant breeding, S.V. Agricultural College, Tirupati, ANGRAU, Andhra Pradesh, 517502, India.
B. Rupesh Kumar Reddy
Genetics and Plant Breeding, AICRP on Groundnut, RARS Tirupati, ANGRAU, Andhra Pradesh, 517502, India.
R. Narasimhulu
Genetics and Plant Breeding, AICRP on Castor, Agricultural Research Station Anantha puram, ANGRAU, Andhra Pradesh, 515001, India.
M. Pradeep
Department of Plant Pathology, S.V. Agricultural college, Tirupati, ANGRAU, Andhra Pradesh, 517502, India.
*Author to whom correspondence should be addressed.
Abstract
Groundnut (Arachis hypogaea L.) is a globally important oilseed and food legume whose productivity and end-use value are shaped by complex interactions among genetic variability, target trait architecture, and highly variable production environments. Despite the crop’s polyploid origin and historically perceived narrow diversity within elite gene pools, modern genomics has revealed extensive allelic, haplotypic, and structural variation—much of it traceable to diploid progenitors and wild Arachis relatives—and has enabled more systematic translation of diversity into improved yield stability, durable disease resistance, and enhanced nut quality. This review synthesizes recent advances in understanding and exploiting genetic variability for three tightly linked breeding targets: (i) yield and its component traits (pod number, seed size, partitioning, phenology, and environment responsiveness), (ii) resistance to major diseases (foliar fungal diseases such as late leaf spot and rust, bacterial wilt, emerging threats such as peanut smut, and resistance mechanisms that reduce aflatoxin risk), and (iii) nut quality (oil content, fatty acid composition, and trait combinations that improve nutrition and shelf-life). We highlight how high-quality reference genomes, pangenome resources, dense SNP arrays, GWAS in diverse panels and multi-parent populations, and genomic prediction approaches are reshaping trait dissection and breeding strategy. The evidence indicates that yield, resistance, and quality can be improved together when breeding programs explicitly manage trade-offs via multi-trait selection indices, environment-aware genomic prediction, and marker-assisted introgression for major-effect loci (e.g., high oleic acid alleles and key disease-resistance QTL). We conclude with practical recommendations for aligning germplasm design, phenotyping, and genomics to accelerate genetic gain while safeguarding farmer adoption and value-chain requirements.
Keywords: Arachis hypogaea, genetic diversity, genomic selection, pangenome, late leaf spot, rust, aflatoxin, fatty acid composition