Exploration of genetic variance has mostly been limited to additive effects estimated using pedigree data and non-additive effects have been ignored. This study aimed to evaluate the performance of single nucleotide polymorphisms (SNPs) marker models in the mixed and orthogonal framework including both additive and non-additive effects for estimating variances and genomic prediction in four diabetes-related traits in heterogeneous stock mice. Models have performed differently in detecting SNPs

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... ffecting traits. Dominance variances explained over 14.7 and 3.8% of genetic and phenotype variance in a Genomic prediction and variance component estimation method (GVCBLUP) framework. Reliabilities of additive Genomic best linear unbiased prediction model (GBLUP) in different traits ranged from 44.8 to 66.6%, for GVCBLUPs framework including both additive and dominance effects (MAD), and 46.1 to 69% for the model including additive effect (MA). Dominance GBLUP reliabilities ranged from 6 to 26.4% for MAD and from 22.5 to 50.5% in the model including dominance (MD). MA and MD had higher reliability for additive and dominance GBLUPs compared to MAD. Reliabilities of GBLUPs in MAD and MA for all traits were not significant except for growth slope (P < 0.01). In orthogonal framework models, epistasis variances accounted for a greater proportion (87.3, 89.1, 95.5, and 77.2%) of genetic variation for end weight, growth slope, body mass index, and body length, respectively. Heritability in a broad sense was estimated at 1.12, 1.67, 3.64, and 2.0%, in which non-additive heritability had a significant contribution. Genetic variances explained by dominance using GVCBLUPs were 16.8, 29.4, 14.6, and 14.9% for the traits. Generally, the non-additive models had a lower value of deviance information criterion (DIC) and performed better in estimating the variance component. Comparing the estimated variance by orthogonal framework models confirmed the results previously estimated by GVCBLUPs, with the difference that the estimates were shrinking. Following significant SNPs affecting diabetes-related traits by post-genome-wide studies could reveal unknown aspects and contribute to genetic control of the disease.