Genome-wide association study (GWAS) is a standard approach for studying the genetics of natural variation, typically focusing on association between single-nucleotide polymorphisms (SNPs) and traits. For GWAS, if multiple traits are also correlated, some essential and important information among multi-traits can be lost in marginal analysis. Therefore, fully parameterized multi-trait mixed-model is emerged as a flexible approach that considers both the within-trait and between-trait variance components simultaneously for multiple traits. The phenotype data used in this research is 1100 barley entries with reaction of leaf spot diseases. In this dataset, there are two different traits, Rate (reaction of spot form net) and Rating (reaction of spot blotch). 3941 SNPs makers dataset is used as genotype data. A multi-trait mixed-model was applied for genome-wide association study. The results show that the multi-trait mixed model can detect much more associations between single-nucleotide polymorphisms (SNPs) and traits than single-trait analysis can do.