IRB Protocol 80-M-0083 NCT00001174 The long-term objective of this study is to identify genes involved in the risk of bipolar disorder Starting in 1993, in collaboration with 10 academic centers across the United States, we recruited a large sample of over 3,000 individuals with BD or related mood disorders. This is the largest sample ever to participate in a genetic study of BD. All participants did a diagnostic interview and provided a blood sample for DNA analysis. DNA and clinical data are available through the NIMH Center for Genetics. Genetic linkage studies suggested several chromosomal regions may contain genes that contribute to BD in this sample. To identify individual causal genes, we conducted the first genome-wide association study of BD in 2007. The results implicated several genes, each of small effect, suggesting that bipolar disorder is a polygenic disease. Our 2011 meta-analysis of independent case-control studies of bipolar disorder supported association with a cluster of genes on chromosome 3p21, markers near TRANK1, LMAN2L, and PTGFR, and 2 independent regions of ANK3 implicated in previous studies. Many of these findings have now been replicated in independent samples. We have shown that models based on large numbers of markers can distinguish between cases and controls in independent datasets with high significance, but only modest predictive value. These studies also suggest that common alleles predisposing to BD also predispose to major depression and schizophrenia, but not to neurological diseases such as Parkinson Disease. To identify genetic variants that may have a larger impact on individual risk, we have undertaken genome sequencing studies in selected populations with reduced genetic diversity and large families. Large families increase the opportunities for ascertaining distant relatives with bipolar disorder, and individuals belonging to different nuclear families are often related, forming an extended kindred ideal for genome sequencing studies. So far we have collected 200 distantly-related individuals (125 with bipolar disorder) from Amish and Mennonite communities whose unique genetic history makes them especially good candidates for this kind of study. All blood samples are processed by the Rutgers Cell and DNA Repository who also establish lymphoblastoid cell lines and distribute DNA as a resource for the general scientific community. We have completed about 60 exome sequences so far. In the coming year, in collaboration with colleagues at the Institute of Systems Biology (ISB), we plan to complete and analyze whole-genome sequencing on this sample. This experiment may reveal genetic variations that influence BD risk by virtue of their impact on gene regulation. We will also continue to collect families as they are identified. Our goal is to collect at least 100 additional cases, along with their parents and offspring. Also in collaboration with ISB, and the Bipolar Disorder Genome Study (BiGS), we have begun to analyze whole-genome sequences performed on members of multiplex families with bipolar disorder selected on the basis of risk allele burden or linkage evidence. The goal is to identify rare coding and regulatory variants that contribute to bipolar disorder in these families and may point to genes or pathways more generally involved in the illness. Promising variants identified by sequencing have been submitted to the Bipolar Sequencing Consortium (BSC) where they will become part of a large meta-analysis of sequenced cases, controls, and families. This analysis will be performed by members of the BSC in the coming year. Finally, we are also searching for genetic markers that might help predict an individual's response to lithium, one of the most effective known treatments for bipolar disorder. To this end, we organized a large international collaboration, known as the Consortium on Lithium Genetics (ConLiGen), which aims to characterize lithium response in a large group of patients using reliable instruments, then perform a genome-wide association study. So far we have perforemd a GWAS on over 1000 cases. In the coming year, we will analyze a second sample of over 1000 cases characterized for lithium response over several years and perform a meta-analysis. Findings will be followed up by additional replication and functional genomics studies.

Project Start
Project End
Budget Start
Budget End
Support Year
Fiscal Year
Total Cost
Indirect Cost
U.S. National Institute of Mental Health
Zip Code
Hibar, Derrek P (see original citation for additional authors) (2017) Novel genetic loci associated with hippocampal volume. Nat Commun 8:13624
Hou, Liping; Heilbronner, Urs; Degenhardt, Franziska et al. (2016) Genetic variants associated with response to lithium treatment in bipolar disorder: a genome-wide association study. Lancet 387:1085-1093
McMahon, Francis J (2016) Genetic association studies in psychiatry: time for pay-off. Lancet Psychiatry 3:309-10
Akula, Nirmala; Wendland, Jens R; Choi, Kwang H et al. (2016) An Integrative Genomic Study Implicates the Postsynaptic Density in the Pathogenesis of Bipolar Disorder. Neuropsychopharmacology 41:886-95
Adams, Hieab H H (see original citation for additional authors) (2016) Novel genetic loci underlying human intracranial volume identified through genome-wide association. Nat Neurosci 19:1569-1582
Franke, Barbara; Stein, Jason L; Ripke, Stephan et al. (2016) Genetic influences on schizophrenia and subcortical brain volumes: large-scale proof of concept. Nat Neurosci 19:420-431
Hou, Liping; Bergen, Sarah E; Akula, Nirmala et al. (2016) Genome-wide association study of 40,000 individuals identifies two novel loci associated with bipolar disorder. Hum Mol Genet 25:3383-3394
Geoffroy, P A; Etain, B; Lajnef, M et al. (2016) Circadian genes and lithium response in bipolar disorders: associations with PPARGC1A (PGC-1?) and RORA. Genes Brain Behav 15:660-8
Ament, Seth A; Szelinger, Szabolcs; Glusman, Gustavo et al. (2015) Rare variants in neuronal excitability genes influence risk for bipolar disorder. Proc Natl Acad Sci U S A 112:3576-81
Hibar, Derrek P (see original citation for additional authors) (2015) Common genetic variants influence human subcortical brain structures. Nature 520:224-9

Showing the most recent 10 out of 46 publications