Interrogation of rare functional variation within bipolar disorder and suicidal behavior cohorts

• Main Author: Monson, Eric Thayne
• Other Authors: Willour, Virginia L.
• Format: Others
• Language: English
• Published: University of Iowa 2018
• Subjects: Bipolar disorder; Exome; Pathway analysis; Sequencing; Suicidal behavior; Genetics
• Online Access: https://ir.uiowa.edu/etd/6219; https://ir.uiowa.edu/cgi/viewcontent.cgi?article=7551&context=etd

Suicidal behavior represents the most severe, yet inherently preventable, outcome of psychiatric disease. Despite tremendous efforts to improve the awareness and treatment of psychiatric illness, suicidal behavior rates have been on the rise. The greatest challenge to confronting this crisis is the effective identification and treatment of those at risk for suicide. This challenge has been difficult to address due, in part, to the lack of a clear biological basis for suicidal behavior. Toward addressing this knowledge gap, evidence has been identified of a significant heritable component to suicidal behavior. Subsequent genetic research efforts have focused on the examination of common sites of genetic variation within candidate genes and throughout the genome. These efforts have identified many potentially important risk loci, but the majority of the risk expected to arise from genetic variation remains unexplained by current data. The primary objective of this dissertation was to examine the contribution of largely unexplored rare and potentially damaging genetic variation within suicidal behavior. To do this, targeted next-generation sequencing approaches were employed within a cohort of individuals diagnosed with bipolar disorder, a group particularly enriched for suicidal behavior. Sequence data was generated that examined essentially all protein-coding regions of the human genome (“exome”), with expanded sequencing around and within candidate genes hypothesized to play a role in suicidal behavior risk. The secondary objective of this dissertation focused on the assessment of rare variation within bipolar disorder through sequenced pedigrees and followup in a large collaborative bipolar disorder versus normal control sequencing dataset. These objectives were addressed through the thoughtful application of diverse and complimentary methods. These methods were selected to investigate individual variants, genes, and biological pathways. This approach offered examinations of the potential impact of rare genetic variation within focused regions and across complex biological process pathways that could be disrupted through damaging variation in many different genes. The presented efforts represent the largest examinations of rare functional variation with suicidal behavior and bipolar disorder performed, to date. No individual variant or gene survived correction for multiple testing for either phenotype. These results are consistent with other initial sequencing efforts in complex psychiatric phenotypes, offering conclusions that larger samples will likely be required to identify significant associations for single variants and genes. Within pathway analyses, however, we identified a significant enrichment of rare damaging variation that segregated within bipolar disorder pedigrees in genes that have been implicated in de novo studies of autism. This finding was further replicated within three large case/control sequencing samples, providing support to emerging evidence of a potential overlap of risk loci for autism and bipolar disorder. Many additional results approached significance that bear further consideration. These results offer potential candidate genes and pathways that could be utilized in future sequencing efforts for suicidal behavior and bipolar disorder. In addition, highly valuable resources in the form of datasets strongly enriched for novel rare loci were produced that can significantly contribute to ongoing efforts to investigate bipolar disorder and suicidal behavior. These data can be used in combination with other emerging datasets to generate more powerful meta- and mega-analyses to confidently identify risk loci for both phenotypes.