Association mapping is a powerful approach for exploring the molecular basis of phenotypic variations in plants.A peanut(Arachis hypogaea L.)mini-core collection in China comprising 298 accessions was genotyped using 109 simple sequence repeat(SSR)markers,which identified 554 SSR alleles and phenotyped for 15 agronomic traits in three different environments,exhibiting abundant genetic and phenotypic diversity within the panel.A model-based structure analysis assigned all accessions to three groups.Most of the accessions had the relative kinship of less than 0.05,indicating that there were no or weak relationships between accessions of the mini-core collection.For 15 agronomic traits in the peanut panel,generally the Q t K model exhibited the best performance to eliminate the false associated positives compared to the Q model and the general linear model-simple model.In total,89SSR alleles were identified to be associated with 15 agronomic traits of three environments by the Q t K model-based association analysis.Of these,eight alleles were repeatedly detected in two or three environments,and 15 alleles were commonly detected to be associated with multiple agronomic traits.Simple sequence repeat allelic effects confirmed significant differences between different genotypes of these repeatedly detected markers.Our results demonstrate the great potential of integrating the association analysis and marker-assisted breeding by utilizing the peanut mini-core collection. Association mapping is a powerful approach for exploring the molecular basis of phenotypic variations in plants. A peanut (Arachis hypogaea L.) mini-core collection in China comprising 298 accessions was genotyped using 109 simple sequence repeat (SSR) markers, which identified 554 SSR alleles and phenotyped for 15 agronomic traits in three different environments, exhibiting abundant genetic and phenotypic diversity within the panel. A model-based structure analysis assigned all accessions to three groups. Host of the accessions had the relative kinship of less than 0.05, indicating that there are no or weak relationships between accessions of the mini-core collection. For 15 agronomic traits in the peanut panel, generally the Q t K model exhibits the best performance to eliminate the false associated positives compared to the Q model and the general linear model -simple model. In total, 89 SSR alleles were identified to be associated with 15 agronomic traits of three environments by the Q t K model -based association analysis. Of these eight alleles were repeatedly detected in two or three environments, and 15 alleles were commonly detected to be associated with multiple agronomic traits. Simple sequences repeat allelic effects are significant differences between different genotypes of these repeatedly detected markers. Our results demonstrate the great potential of integrating the association analysis and marker-assisted breeding by utilizing the peanut mini-core collection.