Where once a single ‘omics type was used, studies that capture multiple ‘omics data types in the same dataset are emerging. As these datasets become available, concurrent integration that jointly assess all data may unveil relationships not evident when each data type is analyzed separately by virtue of increased statistical power and their explicit biological relationships (Figure 1). It is expected that genetic variants with large effect sizes are identifiable in sequential analyses but that concurrent integration will enable the identification of genetic variants with moderate-sized, but multi-faceted, functional or regulatory effects. This approach can operate in a bidirectional fashion, with datasets rich in ‘omics informing follow-up in large-scale GWAS of addiction phenotypes/biomarkers and vice versa (Figure 1), and accelerate understanding of the biology underlying statistical associations with addiction phenotypes.
