Analysis of large-scale systems of biomedical data provides a perspective on neuropsychiatric disease that may be otherwise elusive. Described here is an analysis of three large-scale systems of data from autism spectrum disorder (ASD) and of ASD research as an exemplar of what might be achieved from study of such data. First is the biomedical literature that highlights the fact that there are two very successful but quite separate research communities and findings pertaining to genetics and the molecular biology of ASD. There are those studies positing ASD causes that are related to immunological dysregulation and those related to disorders of synaptic function and neuronal connectivity. Second is the emerging use of electronic health record systems and other large clinical databases that allow the data acquired during the course of care to be used to identify distinct subpopulations, clinical trajectories, and pathophysiological substructures of ASD. These systems reveal subsets of patients with distinct clinical trajectories, some of which are immunologically related and others which follow pathologies conventionally thought of as neurological. The third is genome-wide genomic and transcriptomic analyses which show molecular pathways that overlap neurological and immunological mechanisms. The convergence of these three large-scale data perspectives illustrates the scientific leverage that large-scale data analyses can provide in guiding researchers in an approach to the diagnosis of neuropsychiatric disease that is inclusive and comprehensive.
Kohane, Isaac SENGP50 MH094267/MH/NIMH NIH HHS/U01 HG006828/HG/NHGRI NIH HHS/REVIEW2014/07/19 06:00Biol Psychiatry. 2015 Jan 1;77(1):59-65. doi: 10.1016/j.biopsych.2014.05.024. Epub 2014 Jun 12.