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Evidence for single nucleotide polymorphisms and their association with bipolar disorder
Authors Szczepankiewicz A
Received 24 June 2013
Accepted for publication 2 September 2013
Published 11 October 2013 Volume 2013:9 Pages 1573—1582
Checked for plagiarism Yes
Review by Single anonymous peer review
Peer reviewer comments 5
1Laboratory of Molecular and Cell Biology, 2Department of Psychiatric Genetics, Poznan University of Medical Sciences, Poznan, Poland
Abstract: Bipolar disorder (BD) is a complex disorder with a number of susceptibility genes and environmental risk factors involved in its pathogenesis. In recent years, huge progress has been made in molecular techniques for genetic studies, which have enabled identification of numerous genomic regions and genetic variants implicated in BD across populations. Despite the abundance of genetic findings, the results have often been inconsistent and not replicated for many candidate genes/single nucleotide polymorphisms (SNPs). Therefore, the aim of the review presented here is to summarize the most important data reported so far in candidate gene and genome-wide association studies. Taking into account the abundance of association data, this review focuses on the most extensively studied genes and polymorphisms reported so far for BD to present the most promising genomic regions/SNPs involved in BD. The review of association data reveals evidence for several genes (SLC6A4/5-HTT [serotonin transporter gene], BDNF [brain-derived neurotrophic factor], DAOA [D-amino acid oxidase activator], DTNBP1 [dysbindin], NRG1 [neuregulin 1], DISC1 [disrupted in schizophrenia 1]) to be crucial candidates in BD, whereas numerous genome-wide association studies conducted in BD indicate polymorphisms in two genes (CACNA1C [calcium channel, voltage-dependent, L type, alpha 1C subunit], ANK3 [ankyrin 3]) replicated for association with BD in most of these studies. Nevertheless, further studies focusing on interactions between multiple candidate genes/SNPs, as well as systems biology and pathway analyses are necessary to integrate and improve the way we analyze the currently available association data.
Keywords: candidate gene, genome-wide association study, SLC6A4, BDNF, DAOA, DTNBP1, NRG1, DISC1
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