Abstract Motivation Transcriptome-wide association studies (TWAS) have successfully facilitated the discovery of novel genetic risk loci for many complex traits, including late-onset Alzheimer’s disease (AD). However, most existing TWAS methods rely only on gene expression and ignore epigenetic modification (i.e. DNA methylation) and functional regulatory information (i.e. enhancer-promoter interactions), both of which contribute significantly to the genetic basis of AD. Results We develop a novel gene-level association testing method that integrates genetically regulated DNA methylation and enhancer–target gene pairs with genome-wide association study (GWAS) summary results. Through simulations, we show that our approach, referred to as the CMO (cross methylome omnibus) test, yielded well controlled type I error rates and achieved much higher statistical power than competing methods under a wide range of scenarios. Furthermore, compared with TWAS, CMO identified an average of 124% more associations when analyzing several brain imaging-related GWAS results. By analyzing to date the largest AD GWAS of 71 880 cases and 383 378 controls, CMO identified six novel loci for AD, which have been ignored by competing methods. Availabilityand implementation The data used in this work were obtained from the following publicly available datasets: IGAP1, GWAX, UK Biobank, a 2019 meta-analyzed AD GWAS results and a imaging-derived phenotype GWAS results. The data resources are summarized in Supplementary Table S7. We used the publicly available software and tools for competing methods. All codes used to generate results that are reported in this manuscript and software for our newly proposed method CMO are available at https://github.com/ChongWuLab/CMO. Supplementary information Supplementary data are available at Bioinformatics online.