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Original Investigation |

Analyzing the Role of MicroRNAs in Schizophrenia in the Context of Common Genetic Risk Variants

Mads Engel Hauberg, MD1,2,3; Panos Roussos, MD, PhD4,5,6,7,8; Jakob Grove, PhD1,2,3,9; Anders Dupont Børglum, MD, PhD1,2,3,10,11; Manuel Mattheisen, MD1,2,3 ; for the Schizophrenia Working Group of the Psychiatric Genomics Consortium
[+] Author Affiliations
1Department of Biomedicine, Aarhus University, Aarhus, Denmark
2Lundbeck Foundation Initiative of Integrative Psychiatric Research, Lundbeck, Denmark
3Centre for Integrative Sequencing, Aarhus University, Aarhus, Denmark
4Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, New York
5Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York
6Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, New York
7Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, New York
8James J. Peters VA Medical Center, Mental Illness Research Education and Clinical Center, Bronx, New York
9Bioinformatics Research Centre, Aarhus University, Aarhus, Denmark
10Research Department P, Aarhus University Hospital, Risskov, Denmark
11Translational Neuropsychiatry Unit, Department of Clinical Medicine, Aarhus University, Denmark
JAMA Psychiatry. 2016;73(4):369-377. doi:10.1001/jamapsychiatry.2015.3018.
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Importance  The recent implication of 108 genomic loci in schizophrenia marked a great advancement in our understanding of the disease. Against the background of its polygenic nature there is a necessity to identify how schizophrenia risk genes interplay. As regulators of gene expression, microRNAs (miRNAs) have repeatedly been implicated in schizophrenia etiology. It is therefore of interest to establish their role in the regulation of schizophrenia risk genes in disease-relevant biological processes.

Objective  To examine the role of miRNAs in schizophrenia in the context of disease-associated genetic variation.

Design, Setting, and Participants  The basis of this study was summary statistics from the largest schizophrenia genome-wide association study meta-analysis to date (83 550 individuals in a meta-analysis of 52 genome-wide association studies) completed in 2014 along with publicly available data for predicted miRNA targets. We examined whether schizophrenia risk genes were more likely to be regulated by miRNA. Further, we used gene set analyses to identify miRNAs that are regulators of schizophrenia risk genes.

Main Outcomes and Measures  Results from association tests for miRNA targetomes and related analyses.

Results  In line with previous studies, we found that similar to other complex traits, schizophrenia risk genes were more likely to be regulated by miRNAs (P < 2 × 10−16). Further, the gene set analyses revealed several miRNAs regulating schizophrenia risk genes, with the strongest enrichment for targets of miR-9-5p (P = .0056 for enrichment among the top 1% most-associated single-nucleotide polymorphisms, corrected for multiple testing). It is further of note that MIR9-2 is located in a genomic region showing strong evidence for association with schizophrenia (P = 7.1 × 10−8). The second and third strongest gene set signals were seen for the targets of miR-485-5p and miR-137, respectively.

Conclusions and Relevance  This study provides evidence for a role of miR-9-5p in the etiology of schizophrenia. Its implication is of particular interest as the functions of this neurodevelopmental miRNA tie in with established disease biology: it has a regulatory loop with the fragile X mental retardation homologue FXR1 and regulates dopamine D2 receptor density.

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Figure 1.
Flowcharts of the Analyses

A, Flowchart of the linear model for assessing whether schizophrenia risk genes are more likely to be targeted by microRNAs (miRNAs). GWAS indicates genome-wide association study; UTR, untranslated region. B, Flowchart for gene set analyses of all conserved miRNAs and the targeted gene set analyses. CLIP-Seq indicates cross-linking immunoprecipitation followed by sequencing; CNV, copy number variation; MAF, minor allele frequency; and Mb, megabase. More information on the analytical strategies can be found in the text.

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Figure 2.
Circos Plot of the Top 10 Schizophrenia MicroRNA (miRNA) Gene Sets

The innermost 10 tracks illustrate the targets of each miRNA. The targets are color coded based on their gene P values. The miRNAs were ordered by their correlational clustering. Peripherally to this, a Manhattan plot is shown (only single-nucleotide polymorphisms [SNPs] with P < .02 located in protein-coding genes are included). At the edge, the genome-wide–significant genes targeted by the top 10 miRNAs are shown. They are color coded based on the number of miRNAs in the top 10 list that target them. The major histocompatibility complex (MHC) region is included here for illustrative purposes but was not part of the gene set tests, and P values from the most recent schizophrenia genome-wide association study meta-analysis conducted by the Schizophrenia Working Group of the Psychiatric Genomics Consortium (PGC2)12 are without replication. In eFigure 1 in the Supplement, a zoomed-in view of this region is presented.

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Figure 3.
Clustering of MicroRNAs (miRNAs) Based on the Jaccard Distance Between the Targets of Each miRNA

Height indicates the dissimilarity measure in the clustering; SNPs, single-nucleotide polymorphisms. In eFigure 2 in the Supplement, the clustering is repeated considering only the targets showing increasing degrees of association with schizophrenia.

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