As a result of the accelerated pace of development of technologies for characterizing the human genome, the rate-limiting step for large scale genomic investigation in clinical populations is now phenotyping. This is particularly the case for neuropsychiatric (NP) illness, where phenotypes are complex, biomarkers are lacking, and the primary cell types of interest are difficult to access directly. It has become apparent that both rare and common genetic variations contribute to disease risk and that this risk crosses traditional diagnostic boundaries in psychiatry. Taking advantage of a large, already-established NP biobank could dramatically accelerate progress toward understanding the cross-disorder mechanism of action of disease liability genes. This study proposes novel applications of emerging technologies in informatics and cellular neurobiology to eliminate this phenotyping bottleneck. In doing so, it will accelerate investigation of clinical and cellular phenotypes for understanding single and multilocus/polygenic associations.
Aim 1: Adapt and expand one of the largest NP cellular biobanks by parsing electronic health records with gold-standard assessment of cognition and other RDoC phenotypes.
Aim 2: Define the genome-wide multidimensional functional genomics (MFG) landscape in NP disease into which the transcriptomic signature (RNA-seq) of each induced neuron (iN) representing a clinically characterized individual is projected . The projection provides the mapping from molecular to phenotypic characterization and a directionality towards healthful/neurotypical states used in Aim 3.
Aim 3: Develp a probabilistic model of gene expression dependencies that will predict which small molecular perturbations are likely to shift the iN transcriptomic signature in a healthful direction in the MFG and to then update the model based on measured perturbations in the MFG.
Aim 4: Select patient samples to study in greater detail for epigenetic (DNA methylation, histone marks and RNA editing) and transcriptional control particularly with regard to activity dependent changes that have been implicated in many NP diseases.
Aim 5: Here we assess just how well the clinical phenotypes are informed by the genome-wide characterizations and assess which is more robust.
This study is designed to answer the question: Can we use the fruits of the first phases of the human genome project to create a new and more robust scheme of classifying neuropsychiatric disease, one that is more reliable with regard to prognosis of these diseases, more insightful as to the biological aberration in each category and, therefore, more effective in treating the patient?