Integration of GWAS with other ‘omics data types (such as, DNA methylation or RNA expression) commonly happens in a sequential fashion to infer functional or regulatory effects of top GWAS findings. Each ‘omics type is analyzed separately, and comparisons are made across results. For example, rs910083 was identified in the largest GWAS meta-analysis of nicotine dependence, and its association was extended to heavy vs. never smoking in the UK Biobank (Table 1); rs910083 was then followed-up via a cis-eQTL analysis using RNA expression data measured across multiple postmortem human brain tissues in the Genotype-Tissue Expression (GTEx) and Brain eQTL Almanac projects, leading to the implication of rs910083 as a DNMT3B cis-eQTL in cerebellum [26]. This type of sequential integration in moving from GWAS to functional or regulatory characterization has also yielded other important discoveries in addiction [21, 48, 119].
