Abstract:

Breeding programs aim to improve the yield and quality of peanut (Arachis hypogaea L.); using association mapping to identify genetic markers linked to these quantitative traits could facilitate selection efficiency. A peanut association panel was established consisting of 268 lines with extensive phenotypic and genetic variation, meeting the requirements for association analysis. These lines were grown over 3 years and the key agronomic traits, including protein and oil content were examined. Population structure (Q) analysis showed two subpopulations and clustering analysis was consistent with Q-based membership assignment and closely related to botanical type. Relative Kinship (K) indicated that most of the panel members have no or weak familial relatedness, with 52.78% of lines showing K = 0. Linkage disequilibrium (LD) analysis showed a high level of LD occurs in the panel. Model comparisons indicated false positives can be effectively controlled by taking Q and K into consideration and more false positives were generated by K than Q. A preliminary association analysis using a Q + K model found markers significantly associated with oil, protein, oleic acid, and linoleic acid, and identified a set of alleles with positive and negative effects. These results show that this panel is suitable for association analysis, providing a resource for marker-assisted selection for peanut improvement.