Complementary to the genome, the concept of exposome has been proposed to capture the totality of human environmental exposures. While there has been some recent progress on the construction of the exposome, few tools exist that can integrate the genome and exposome for complex trait analyses. Here we propose a linear mixed model approach to bridge this gap, which jointly models the random effects of the two omics layers on phenotypes of complex traits. We illustrate our approach using traits from the UK Biobank (e.g., BMI and height for N ~ 35,000) with a small fraction of the exposome that comprises 28 lifestyle factors. The joint model of the genome and exposome explains substantially more phenotypic variance and significantly improves phenotypic prediction accuracy, compared to the model based on the genome alone. The additional phenotypic variance captured by the exposome includes its additive effects as well as non-additive effects such as genome-exposome (gxe) and exposome-exposome (exe) interactions. For example, 19% of variation in BMI is explained by additive effects of the genome, while additional 7.2% by additive effects of the exposome, 1.9% by exe interactions and 4.5% by gxe interactions. Correspondingly, the prediction accuracy for BMI, computed using Pearson's correlation between the observed and predicted phenotypes, improves from 0.15 (based on the genome alone) to 0.35 (based on the genome and exposome). We also show, using established theories, that integrating genomic and exposomic data can be an effective way of attaining a clinically meaningful level of prediction accuracy for disease traits. In conclusion, the genomic and exposomic effects can contribute to phenotypic variation via their latent relationships, i.e. genome-exposome correlation, and gxe and exe interactions, and modelling these effects has a potential to improve phenotypic prediction accuracy and thus holds a great promise for future clinical practice.