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      Incorporating Information of microRNAs into Pathway Analysis in a Genome-Wide Association Study of Bipolar Disorder

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          Abstract

          MicroRNAs (miRNAs) are known to be important post-transcriptional regulators that are involved in the etiology of complex psychiatric traits. The present study aimed to incorporate miRNAs information into pathway analysis using a genome-wide association dataset to identify relevant biological pathways for bipolar disorder (BPD). We selected psychiatric- and neurological-associated miRNAs ( N = 157) from PhenomiR database. The miRNA target genes (miTG) predictions were obtained from microRNA.org. Canonical pathways ( N = 4,051) were downloaded from the Molecule Signature Database. We employed a novel weighting scheme for miTGs in pathway analysis using methods of gene set enrichment analysis and sum-statistic. Under four statistical scenarios, 38 significantly enriched pathways ( P-value < 0.01 after multiple testing correction) were identified for the risk of developing BPD, including pathways of ion channels associated (e.g., gated channel activity, ion transmembrane transporter activity, and ion channel activity) and nervous related biological processes (e.g., nervous system development, cytoskeleton, and neuroactive ligand receptor interaction). Among them, 19 were identified only when the weighting scheme was applied. Many miRNA-targeted genes were functionally related to ion channels, collagen, and axonal growth and guidance that have been suggested to be associated with BPD previously. Some of these genes are linked to the regulation of miRNA machinery in the literature. Our findings provide support for the potential involvement of miRNAs in the psychopathology of BPD. Further investigations to elucidate the functions and mechanisms of identified candidate pathways are needed.

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          Most cited references48

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          Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles

          Although genomewide RNA expression analysis has become a routine tool in biomedical research, extracting biological insight from such information remains a major challenge. Here, we describe a powerful analytical method called Gene Set Enrichment Analysis (GSEA) for interpreting gene expression data. The method derives its power by focusing on gene sets, that is, groups of genes that share common biological function, chromosomal location, or regulation. We demonstrate how GSEA yields insights into several cancer-related data sets, including leukemia and lung cancer. Notably, where single-gene analysis finds little similarity between two independent studies of patient survival in lung cancer, GSEA reveals many biological pathways in common. The GSEA method is embodied in a freely available software package, together with an initial database of 1,325 biologically defined gene sets.
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            Microarray analysis shows that some microRNAs downregulate large numbers of target mRNAs.

            MicroRNAs (miRNAs) are a class of noncoding RNAs that post-transcriptionally regulate gene expression in plants and animals. To investigate the influence of miRNAs on transcript levels, we transfected miRNAs into human cells and used microarrays to examine changes in the messenger RNA profile. Here we show that delivering miR-124 causes the expression profile to shift towards that of brain, the organ in which miR-124 is preferentially expressed, whereas delivering miR-1 shifts the profile towards that of muscle, where miR-1 is preferentially expressed. In each case, about 100 messages were downregulated after 12 h. The 3' untranslated regions of these messages had a significant propensity to pair to the 5' region of the miRNA, as expected if many of these messages are the direct targets of the miRNAs. Our results suggest that metazoan miRNAs can reduce the levels of many of their target transcripts, not just the amount of protein deriving from these transcripts. Moreover, miR-1 and miR-124, and presumably other tissue-specific miRNAs, seem to downregulate a far greater number of targets than previously appreciated, thereby helping to define tissue-specific gene expression in humans.
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              Comprehensive modeling of microRNA targets predicts functional non-conserved and non-canonical sites

              mirSVR is a new machine learning method for ranking microRNA target sites by a down-regulation score. The algorithm trains a regression model on sequence and contextual features extracted from miRanda-predicted target sites. In a large-scale evaluation, miRanda-mirSVR is competitive with other target prediction methods in identifying target genes and predicting the extent of their downregulation at the mRNA or protein levels. Importantly, the method identifies a significant number of experimentally determined non-canonical and non-conserved sites.
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                Author and article information

                Journal
                Front Genet
                Front Genet
                Front. Gene.
                Frontiers in Genetics
                Frontiers Media S.A.
                1664-8021
                03 October 2012
                18 December 2012
                2012
                : 3
                : 293
                Affiliations
                [1] 1Department of Public Health and Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University Taipei, Taiwan
                [2] 2Infectious Diseases Research and Education Center, Department of Health – Executive Yuan and National Taiwan University Taipei, Taiwan
                [3] 3Research Center for Genes, Environment and Human Health, National Taiwan University Taipei, Taiwan
                [4] 4Department of Nursing, Cardinal Tien College of Healthcare and Management Yilan, Taiwan
                Author notes

                Edited by: Peng Jin, Emory University School of Medicine, USA

                Reviewed by: Hongyan Xu, Georgia Health Sciences University, USA; Michiel J. De Hoon, RIKEN, Japan

                *Correspondence: Po-Hsiu Kuo, Department of Public Health and Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Room 521, No. 17, Xuzhou Road, Taipei 10055, Taiwan. e-mail: phkuo@ 123456ntu.edu.tw

                This article was submitted to Frontiers in Non-Coding RNA, a specialty of Frontiers in Genetics.

                Article
                10.3389/fgene.2012.00293
                3524550
                23264780
                57d16311-3d19-4839-ae97-356fc2cdeba5
                Copyright © 2012 Shih, Kao, Chuang and Kuo.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in other forums, provided the original authors and source are credited and subject to any copyright notices concerning any third-party graphics etc.

                History
                : 31 August 2012
                : 27 November 2012
                Page count
                Figures: 1, Tables: 7, Equations: 0, References: 64, Pages: 15, Words: 9204
                Categories
                Genetics
                Original Research

                Genetics
                microrna,bipolar disorder,pathway analysis,genome-wide association,ion channel
                Genetics
                microrna, bipolar disorder, pathway analysis, genome-wide association, ion channel

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