Psychotic symptoms in bipolar disorder (BP) are common, correlate with greater severity of illness, and represent a familial subtype of BP with possible etiological ties to schizophrenia. The long term goal of this K99/R00 application is to uncover susceptibility genes for this serious form of BP. The candidate is a psychiatrist with post-doctoral research experience in mood disorders and genetic epidemiology, who seeks to develop expertise in next-generation DNA sequencing, high-throughput bioinformatics and statistical methods to help uncover the genetic underpinnings of psychotic BP. The primary hypothesis to be tested is that susceptibility genes for psychotic BP will harbor both common and rare causal variants. To test this hypothesis, we propose the following specific aims: (1) To perform a secondary analysis of a BP genome-wide association study (GWAS) using 1200 psychotic BP cases, 900 non-psychotic BP cases and 1500 controls, and to replicate our best findings in a similarly powered independent replication sample. (2) To select genes that meet genome-wide significance in the combined discovery and replication sample and sequence all exons, UTRs, promoters, and highly conserved sequences in 500 cases with psychotic BP and 500 controls using a novel microarray enrichment technique and second generation high-throughput sequencing technology. (3) To validate our findings by performing: a) case-control replication analysis of 1000 additional cases with psychotic BP and 1000 controls;b) selected sequencing of multiply affected families to find evidence of co-segregation between a putative causal variant and disease status. The training and research proposal will enable the candidate to develop into an independent investigator in psychiatric genetics, and has the potential to identify novel susceptibility variants for psychotic BP. Primary mentorship will be provided by Dr. James Potash, director of research for mood disorders at Johns Hopkins, with co-mentorship from Dr. Richard McCombie, co-director of the CSHL Genome Center and an expert in high throughput sequencing and Dr. Peter Zandi, a genetic epidemiologist with expertise in bioinformatics .

Public Health Relevance

Bipolar disorder type I affects 1% of the U.S. population and is one of the top ten worldwide causes of disability. Psychotic symptoms occur in approximately half of all patients with bipolar disorder and correlate with increased illness severity and, possibly, increased risk of suicide. Although psychotic bipolar disorder is heritable and clusters within families, little is known about its underlying genetic etiology. This work proposed to uncover gene(s) associated with susceptibility to psychotic Bipolar Disorder, which should provide insights into the pathophysiology of the disorder, and may point to targets appropriate for rational drug development.

Agency
National Institute of Health (NIH)
Institute
National Institute of Mental Health (NIMH)
Type
Research Transition Award (R00)
Project #
5R00MH086049-05
Application #
8661788
Study Section
Special Emphasis Panel (NSS)
Program Officer
Addington, Anjene M
Project Start
2010-02-02
Project End
2015-05-31
Budget Start
2014-06-01
Budget End
2015-05-31
Support Year
5
Fiscal Year
2014
Total Cost
$244,626
Indirect Cost
$93,622
Name
Johns Hopkins University
Department
Psychiatry
Type
Schools of Medicine
DUNS #
001910777
City
Baltimore
State
MD
Country
United States
Zip Code
21218
Goes, Fernando S; Pirooznia, Mehdi; Parla, Jennifer S et al. (2016) Exome Sequencing of Familial Bipolar Disorder. JAMA Psychiatry 73:590-7
Liebers, David T; Pirooznia, Mehdi; Seiffudin, Fayaz et al. (2016) Polygenic Risk of Schizophrenia and Cognition in a Population-Based Survey of Older Adults. Schizophr Bull 42:984-91
Goes, Fernando S (2016) Genetics of Bipolar Disorder: Recent Update and Future Directions. Psychiatr Clin North Am 39:139-55
Goes, Fernando S; McGrath, John; Avramopoulos, Dimitrios et al. (2015) Genome-wide association study of schizophrenia in Ashkenazi Jews. Am J Med Genet B Neuropsychiatr Genet 168:649-59
Chen, Yun-Ching; Carter, Hannah; Parla, Jennifer et al. (2013) A hybrid likelihood model for sequence-based disease association studies. PLoS Genet 9:e1003224
Goes, F S; Hamshere, M L; Seifuddin, F et al. (2012) Genome-wide association of mood-incongruent psychotic bipolar disorder. Transl Psychiatry 2:e180