Data Science/Computational Biology

Recent genome-wide association studies for serious brain disorders like schizophrenia, bipolar disorder, and major depression have provided the first objective and bona fide clues, independent of symptomatology and epiphenomena, related to etiology. While these risk variants identify bases of DNA that differ in frequency between cases and controls, the biological mechanisms underlying any given risk variant are largely unknown. The Data Science group therefore focuses on generating and analyzing “big data” to better understand how different risk alleles affect brain development and function in order to identify how genetic risk manifests in the human brain.

Molecular readouts from human brain tissue allow for the analysis of genetic variation, regulatory epigenetic mechanisms like DNA methylation and chromatin accessibility, and resulting gene expression levels. These large and multi-dimensional datasets can be used to identify the causes and consequences of genetic risk in the human brain, and point to potential implicated gene and protein pathways and networks. The interplay between the genome, epigenome, and transcriptome in the human brain can therefore better highlight how dysregulation occurs in schizophrenia and related disorders.