Mental illnesses are some of the most devastating diseases affecting human populations, placing a huge burden on individuals, families and society. Genome-wide association studies (GWAS) have identified dozens of common single nucleotide polymorphisms (SNPs) that are associated with psychiatric diseases, but a majority of those SNPs have been mapped to intergenic or intronic regions and are functionally unclassified. The overall goal of this proposed study is to use genetic mapping of quantitative trait loci (QTL), including expression QTLs (eQTLs), protein QTLs (pQTLs), and DNase I sensitivity QTLs (dsQTLs), to map non-coding regulatory elements in human brain, then to use the QTL SNPs to uncover regulatory mechanisms underlying GWAS findings and to discover novel risk genes. Our previous studies have shown that psychiatric GWAS signals are enriched with brain eQTL SNPs (eSNPs), and these brain eSNPs are likely to be functional and contribute to disease susceptibilities. We hypothesize that other QTLs will similarly represent other levels of regulation. So, using QTL mapping, we will identify SNPs affecting chromatin accessibility in brain (dsQTLs), and downstream gene and protein level variations (eQTLs and pQTLs). We will use RNA-seq, micro-western arrays (MWAs), reverse phase protein arrays (RPPAs), and DNase-seq to profile prefrontal cortex and cerebellum of 432 postmortem brains, along with sorted NeuN+ and NeuN- nuclei. Using the optimal deconvolution method, all brain measures will be partitioned into neuronal and non-neuronal measures for QTL mapping. We will thereafter re-analyze existing GWAS data for seven psychiatric diseases, plus three non- psychiatric diseases/traits as controls, to understand the contributions of neuronal- and non- neuronal QTL SNPs to disease risks. We will also look for differential expressions of transcripts and proteins, as well as for differential DNA sensitivities, in patient brains, and use these molecular measures to construct novel regulatory networks. This integrative study represents a timely, novel and powerful approach that will transform our understanding of brain genomics and the genetic risks of psychiatric diseases. It is positioned to create a new paradigm for integrating brain genomics and psychiatric genetics that are truly distinct from current approaches.

Public Health Relevance

Mental illnesses are the leading cause of disabilities in the U.S. Genome-wide association studies (GWAS) have identified dozens of common single nucleotide polymorphisms (SNPs) that are associated with psychiatric diseases, but the functions effects of most of those SNPs are unknown. This study will use genetic association mapping of quantitative trait loci (QTL) of gene expression, protein abundance, and chromatin accessibility to map non-coding regulatory elements in human neuronal and non-neuronal brain cells. The identified QTLs will be used to clarify functionality of GWAS findings and discover novel risk genes. Additionally, we will construct novel regulatory networks using those brain molecular measures obtained, so that we can place susceptibility genes into a systems biology context. This innovative approach will improve our understanding of psychiatric diseases, and should lead to better diagnosis and treatment.

National Institute of Health (NIH)
National Institute of Mental Health (NIMH)
Research Project--Cooperative Agreements (U01)
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Special Emphasis Panel (ZMH1-ERB-C (03))
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Senthil, Geetha
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University of Illinois at Chicago
Schools of Medicine
United States
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Grennan, Kay S; Chen, Chao; Gershon, Elliot S et al. (2014) Molecular network analysis enhances understanding of the biology of mental disorders. Bioessays 36:606-16