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Success in the identification of genetic variants associated with neuropsychiatric disorders is one of the major achievements in contemporary biomedical research. Most genetic variants identified in Genome-Wide Association Studies (GWAS) of complex traits are thought to act via effects on gene regulation rather than directly altering the protein product. As a consequence, the actual genes involved in disease are not necessarily the most proximal to the associated variants. By integrating data from GWAS analyses with that from genetic studies of regulatory variation, it is possible to identify variants pleiotropically-associated with both a complex trait and measures of gene regulation. In this study, we use Summary data–based Mendelian Randomization (SMR), a method developed to identify variants pleiotropically associated with both complex traits and gene expression, to identify associations between neuropsychiatric disorders and DNA methylation. Building on our previous efforts, we increased our catalogue of DNA Methylation Quantitative Trait Loci (mQTL) in whole blood using the Illumina EPIC HumanMethylation array that interrogates over 800,000 genomic loci. These data along with mQTL data identified previously in human fetal brain were used to prioritize genes for psychiatric disorders using GWAS data from the Psychiatric Genomics Consortium (PGC). In this study, we apply the SMR approach to test 129,469 DNA methylation sites against five psychiatric phenotypes (schizophrenia, bipolar disorder, major depressive disorder, autism, ADHD) with robust GWAS data available from the PGC using mQTLs identified in whole blood (n=1,175; mQTL P < 1×10-10) to identify novel associations with psychiatric traits. In addition, we tested 9,261 DNA methylation sites using mQTL identified in fetal brain (n=166; mQTL P < 1×10-8). In total, we identified 107 associations with 37 satisfied addition criteria to be defined as pleiotropic and not an artefact of linkage disequilibrium. We identify multiple examples of variable DNA methylation associated with GWAS variants across the five psychiatric disorders, demonstrating the utility of the SMR approach for refining genetic association signals. [ABSTRACT FROM AUTHOR]