Defense Date

2014

Document Type

Dissertation

Degree Name

Doctor of Philosophy

Department

Clinical and Translational Sciences

First Advisor

Brien Riley

Abstract

Schizophrenia demonstrates high heritability in part accounted for by common simple nucleotide variants (SNV), rare copy number variants (CNV) and, most recently, rare SNVs Although heritability explained by rare SNVs and CNVs is small compared to that explained by common SNVs, rare SNVs in functional sequences may identify specific disease mechanisms. However, current exome methods do not capture a large proportion of potentially functional bases where rare variation may impact disease risk: as much as two-thirds of conserved sequences lie outside the exome in non-coding regions of cross-species evolutionary constraint. We reasoned that the candidate loci from the Psychiatric Genomics Consortium Phase 1 (PGC-1) schizophrenia study represent good target loci to test for the impact of rare SNVs in non-coding constrained regions. We developed custom reagents to capture mammalian constrained non-coding regions, exons, and 5’- and 3’-untranslated regions (UTRs) in the 12 PGC-1 loci for pooled sequencing in 912 cases and 936 controls. Compared to our coding targets, our noncoding targets contain substantially more highly conserved bases (46,412 vs. 31,609) and variants (390 vs. 193). Using C-alpha to detect excess variance due to aggregate risk increasing or decreasing rare SNV effects, we identified signals attributable to alleles with MAF < 0.1% in both coding sequences and in functional non-coding sequences, including variants within ENCODE transcription factor binding sites, DNase hypersensitive regions, and histone modification sites in neuronal cell lines. We also observed significant excess risk-altering variation in the CUB domain of CSMD1, a gene expressed in the developing central nervous system. These results support the hypothesis that common and rare variants in the same loci contribute to schizophrenia risk, but highlight the need to expand capture strategies in order to detect trait-relevant sequence variation in a broader set of functional sequences.

Rights

© The Author

Is Part Of

VCU University Archives

Is Part Of

VCU Theses and Dissertations

Date of Submission

8-18-2014

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