This is a proposal to take a novel approach to the genetics of bipolar disorder (BP) through sequencing of all known synaptic genes (the synaptome). The project will take advantage of the talents of a next-generation sequencing leader, a BP genetics expert, and a synapse neurobiology specialist. Together we hope to discover rare BP susceptibility variants. BP, the sixth-leading cause of disability worldwide, is highly heritable. Molecular genetics work in BP is currently focused on uncovering common disease variants. The first four genome-wide association (GWA) scans have, however, been disappointing yielding no genome-wide significant signals, although one signal surpassed that threshold in a combined analysis. Interestingly, the two strongest genes in that analysis encode synaptic proteins, and in a pathway analysis of two of these GWA studies, the most significantly enriched gene set was for synaptic transmission. We propose to determine the genetic variation in genes encoding components of the synapse including neurotransmitters and their receptors, adhesion/cytoskeletal proteins and scaffold proteins. Advances in sequencing technology and the ability to target specific genomic areas will allow us, in Aim 1, to resequence exons and promoters of 1,500 synaptome genes in 800 BP probands and 400 controls, and to similarly screen the whole exome in 80 probands from our largest BP families and from 40 controls.
In Aim 2 we will bioinformatically assess the likely functional impact of variants, and compare variation in cases to variation in 800 controls (our sequenced controls plus 400 sequenced by the 1000 Genomes Project) to determine whether genes and/or clusters are enriched for rare deleterious variants. We will similarly compare whole-exome variation in 80 cases and 80 controls.
In Aim 3 we will genotype extended families of Aim 1 probands carrying likely susceptibility variants to assess for linkage, and genotype 1,600 cases and 1,600 controls to replicate gene and cluster enrichment of functional variants in BP. We will also resequence in a subset of genes to replicate enrichment in BP of functional variants. Our study holds out the possibility of finding, not merely variants in linkage disequilibrium with BP susceptibility variants, but the functional disease variants themselves. Further, it is important to emphasize that the great majority of psychiatric drugs modulate synaptic mechanisms. We therefore consider that discovery of BP genes encoding synaptic proteins has very high translational potential as these potentially represent the most """"""""druggable"""""""" targets in BP.
This is a proposal to take a novel approach to the genetics of bipolar disorder through sequencing of all known genes that code for proteins in the brain's synapses. Our study holds out the possibility of finding, not merely gene variants that lie near those that confer susceptibility to bipolar disorder, but the actual disease variants themselves. Further, it is important to emphasize that the great majority of psychiatric drugs modulate brain synapse mechanisms. We therefore consider that discovery of bipolar disorder genes encoding synaptic proteins has very high translational potential as these potentially represent the most druggable targets in bipolar disorder. )
|Martin, P-M; Stanley, R E; Ross, A P et al. (2018) DIXDC1 contributes to psychiatric susceptibility by regulating dendritic spine and glutamatergic synapse density via GSK3 and Wnt/?-catenin signaling. Mol Psychiatry 23:467-475|
|Monson, Eric T; Pirooznia, Mehdi; Parla, Jennifer et al. (2017) Assessment of Whole-Exome Sequence Data in Attempted Suicide within a Bipolar Disorder Cohort. Mol Neuropsychiatry 3:1-11|
|Goes, Fernando S; Pirooznia, Mehdi; Parla, Jennifer S et al. (2016) Exome Sequencing of Familial Bipolar Disorder. JAMA Psychiatry 73:590-7|
|Pirooznia, Mehdi; Kramer, Melissa; Parla, Jennifer et al. (2014) Validation and assessment of variant calling pipelines for next-generation sequencing. Hum Genomics 8:14|
|Chen, Yun-Ching; Carter, Hannah; Parla, Jennifer et al. (2013) A hybrid likelihood model for sequence-based disease association studies. PLoS Genet 9:e1003224|