Genomic dissection of genetic effects on desirable traitsthe subsequent use of genomic selection hold great promise for accelerating the rate of genetic improvement of forest tree species. In this study, a total of 661 offspring trees from 66 open-pollinated families of Japanese larch (Larix kaempferi (Lam.) Carrière) were sampled at a test site. The contributions of additivenon-additive effects (dominance, imprintingepistasis) were evaluated for nine valuable traits related to growth, wood physicalchemical properties,competitive ability using three pedigree-basedfour Genomics-based Best Linear Unbiased Predictions (GBLUP) modelsused to determine the genetic model. The predictive ability (PA) of two genomic predictionmethods, GBLUPReproducing Kernel Hilbert Spaces (RKHS), was compared. The traits could be classifiedtwo types based on different quantitative genetic architectures: for type I, including wood chemical propertiesPilodyn penetration, additive effect is the main source of variation (38.20-67.46%); for type II, including growth, competitive abilityacoustic velocity, epistasis plays a significant role (50.76-91.26%). Dominanceimprinting showed low to moderate contributions (< 36.26%). GBLUP was more suitable for traits of type I (PAs = 0.37-0.39 vs. 0.14-0.25),RKHS was more suitable for traits of type II (PAs = 0.23-0.37 vs. 0.07-0.23). Non-additive effects make no meaningful contribution to the enhancement of PA of GBLUP method for all traits. These findings enhance our current understanding of the architecture of quantitative traitslay the foundation for the development of genomic selection strategies in Japanese larch.