Creating a network of the genetic relationships between multiple psychiatric and medical traits, during a critical developmental period, can enhance our understanding of risk for psychopathology.
Methylome-wide association studies are typically performed using microarray technologies that only assay a very small fraction of the CG methylome and entirely miss two forms of methylation that are common in brain and likely of particular relevance for neuroscience and psychiatric disorders.
The extent to which earlier age of onset (AO) is a reflection of increased genetic risk for major depression (MD) is still unknown. Previous biometrical research has provided mixed empirical evidence for the genetic overlap of AO with MD.
Previous genomewide association studies (GWASs) have identified a number of putative risk loci for alcohol dependence (AD). However, only a few loci have replicated and these replicated variants only explain a small proportion of AD risk.
The application of machine learning methods to EHRs, and the potential of extending such analyses to other sources of big medical data (e.g., genomics and imaging), could generate enormous—yes, even paradigm-shifting—returns in improved diagnosis and treatment.