Genome-wide association studies (GWAS) have been extremely successful in identifying variants associated to schizophrenia and other psychiatric disorders, but the molecular impact of these variants remains largely unknown. Meanwhile, an explosion of functional data has been generated by ENCODE, ROADMAP Epigenomics and other projects, making the development of new statistical methods to link associations in genetic data to biological interpretations from functional data a pressing priority Our specific aims are 1) To partition psychiatric disease heritability by functional category. We propose to understand at a global level the relative importance of different classes of variation i explaining heritability of mental illness. 2) To maximize biological inferences from expression data in brain tissues. We propose to extend current methods for the processing and analysis of RNA-sequencing data to improve identification of quantitative trait loci that influence expression levels and splicing diversity. 3) To use prioritized functional categories and QTLs to fine-map genome-wide significant loci. We will integrate the results of aims 1 and 2 to more thoroughly understand which genetic variants drive genome-wide significant associations to mental illness. We will guide our research using >200,000 samples from genome-wide associations studies of psychiatric disease. The methods we propose to develop will be implemented in software packages that we will make widely available to the community.
Significant progress has been made in the identification of genome-wide significant loci for mental disorders. However, in almost all instances the biological consequences of these loci are poorly understood. In this application, we propose to develop computational methods to link these associated variants to function, through the incorporation of genomics and expression data. We will make these tools widely available to the community for application across emerging genome-wide association analyses.
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