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JAMA Psychiatry. 2014 Jun;71(6):657-64. doi: 10.1001/jamapsychiatry.2014.176.

Identification of pathways for bipolar disorder: a meta-analysis.

Collaborators (171)

Sklar P, Ripke S, Scott LJ, Andreassen A, Cichon S, Craddock N, Edenberg HJ, Nurnberger JI Jr, Rietschel M, Blackwood D, Corvin A, Flickinger M, Guan W, Mattingsdal M, McQuillin A, Wienker TF, Daly M, Dudbridge F, Holmans PA, Lin D, Burmeister M, Greenwood TA, Hamshere ML, Muglia P, Smith EN, Zandi PP, Nievergelt CM, McKinney R, Shilling PD, Schork NJ, Bloss CS, Foroud T, Koller DL, Gershon ES, Liu C, Badner JA, Scheftner WA, Lawson WB, Nwulia EA, Hipolito M, Coryell W, Rice J, Byerley W, McMahon FJ, Schulze TG, Berrettini W, Lohoff FW, Potash JB, Mahon PB, McInnis MG, Zöllner S, Zhang P, Craig DW, Szelinger S, Barrett TB, Breuer R, Meier S, Strohmaier J, Witt SH, Tozzi F, Farmer A, McGuffin P, Strauss J, Xu W, Kennedy JL, Vincent JB, Matthews K, Day R, Ferreira MA, O'Dushlaine C, Perlis R, Raychaudhuri S, Ruderfer D, Lee PH, Smoller W, Li J, Absher D, Bunney WE, Barchas JD, Schatzberg AF, Jones EG, Meng F, Thompson RC, Watson SJ, Myers RM, Akil H, Boehnke M, Chambert K, Moran J, Scolnick EM, Djurovic S, Melle I, Morken G, Gill M, Morris D, Quinn E, Mühleisen TW, Degenhardt FA, Mattheisen M, Schumacher J, Maier W, Steffens M, Propping P, Nöthen MM, Anjorin A, Bass N, Gurling H, Kandaswamy R, Lawrence J, McGhee K, McIntosh AM, McLean AW, Muir WJ, Pickard BS, Breen G, Clair DS, Caesar S, Gordon-Smith K, Jones L, Fraser C, Green EK, Grozeva D, Jones IR, Kirov G, Moskvina V, Nikolov I, O'Donovan MC, Owen MJ, Collier DA, Elkin A, Williamson R, Young AH, Ferrier I, Stefansson K, Stefansson H, Þorgeirsson Þ, Steinberg S, Gustafsson Ó, Bergen SE, Nimgaonkar V, Hultman C, Landén M, Lichtenstein P, Sullivan PF, Schalling M, Osby U, Backlund L, Frisén L, Langstrom N, Jamain S, Leboyer M, Etain B, Bellivier F, Petursson H, Sigurðsson E, Müller-Mysok B, Lucae S, Schwarz M, Fullerton JM, Schofield PR, Martin N, Montgomery GW, Lathrop M, Óskarsson H, Bauer M, Wright A, Mitchell PB, Hautzinger M, Reif A, Kelsoe JR, Purcell SM.

Author information

1
Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis2Institute of Psychiatric Research, Department of Psychiatry, Indiana University School of Medicine, Indianapolis.
2
Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis.
3
Laboratory of Neurogenetics, National Institute on Alcohol Abuse and Alcoholism Intramural Research Program, Bethesda, Maryland.
4
Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis4Department of Biochemistry and Molecular Biology, Indiana University School of Medicine, Indianapolis.
5
Functional Genomics Laboratory, Department of Psychiatry and Human Behavior, School of Medicine, University of California, Irvine.
6
Department of Psychiatry, School of Medicine, University of California, San Diego, La Jolla7Department of Psychiatry, Special Treatment and Evaluation Program, Veterans Affairs San Diego Healthcare System, San Diego, California.

Abstract

IMPORTANCE:

Genome-wide investigations provide systematic information regarding the neurobiology of psychiatric disorders.

OBJECTIVE:

To identify biological pathways that contribute to risk for bipolar disorder (BP) using genes with consistent evidence for association in multiple genome-wide association studies (GWAS).

DATA SOURCES:

Four independent data sets with individual genome-wide data available in July 2011 along with all data sets contributed to the Psychiatric Genomics Consortium Bipolar Group by May 2012. A prior meta-analysis was used as a source for brain gene expression data.

STUDY SELECTION:

The 4 published GWAS were included in the initial sample. All independent BP data sets providing genome-wide data in the Psychiatric Genomics Consortium were included as a replication sample.

DATA EXTRACTION AND SYNTHESIS:

We identified 966 genes that contained 2 or more variants associated with BP at P < .05 in 3 of 4 GWAS data sets (n = 12,127 [5253 cases, 6874 controls]). Simulations using 10,000 replicates of these data sets corrected for gene size and allowed the calculation of an empirical P value for each gene; empirically significant genes were entered into a pathway analysis. Each of these pathways was then tested in the replication sample (n = 8396 [3507 cases, 4889 controls]) using gene set enrichment analysis for single-nucleotide polymorphisms. The 226 genes were also compared with results from a meta-analysis of gene expression in the dorsolateral prefrontal cortex.

MAIN OUTCOMES AND MEASURES:

Empirically significant genes and biological pathways. RESULTS Among 966 genes, 226 were empirically significant (P < .05). Seventeen pathways were overrepresented in analyses of the initial data set. Six of the 17 pathways were associated with BP in both the initial and replication samples: corticotropin-releasing hormone signaling, cardiac β-adrenergic signaling, phospholipase C signaling, glutamate receptor signaling, endothelin 1 signaling, and cardiac hypertrophy signaling. Among the 226 genes, 9 differed in expression in the dorsolateral prefrontal cortex in patients with BP: CACNA1C, DTNA, FOXP1, GNG2, ITPR2, LSAMP, NPAS3, NCOA2, and NTRK3.

CONCLUSIONS AND RELEVANCE:

Pathways involved in the genetic predisposition to BP include hormonal regulation, calcium channels, second messenger systems, and glutamate signaling. Gene expression studies implicate neuronal development pathways as well. These results tend to reinforce specific hypotheses regarding BP neurobiology and may provide clues for new approaches to treatment and prevention.

PMID:
24718920
PMCID:
PMC4523227
DOI:
10.1001/jamapsychiatry.2014.176
[Indexed for MEDLINE]
Free PMC Article

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