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The Centre for Applied Genomics and Program in Genetics and Genome Biology, The Hospital for Sick Children, Ontario, CanadaDepartment of Molecular Genetics and McLaughlin Centre, Ontario, Canada
The Centre for Applied Genomics and Program in Genetics and Genome Biology, The Hospital for Sick Children, Ontario, CanadaDepartment of Molecular Genetics and McLaughlin Centre, Ontario, Canada
The Centre for Applied Genomics and Program in Genetics and Genome Biology, The Hospital for Sick Children, Ontario, CanadaDepartment of Molecular Genetics and McLaughlin Centre, Ontario, Canada
Address correspondence to Anne S. Bassett, M.D., Centre for Addiction and Mental Health, 33 Russell Street, Room 1100, Toronto, Ontario M5S 2S1, Canada
Clinical Genetics Research Program, Centre for Addiction and Mental Health, Ontario, CanadaDepartment of Psychiatry, University of Toronto, Ontario, CanadaDepartment of Psychiatry, University Health Network, Toronto, Ontario, CanadaDivision of Cardiology, Department of Medicine, University Health Network, Toronto, Ontario, Canada
MicroRNAs (miRNAs) are key regulators of gene expression in the human genome and may contribute to risk for neuropsychiatric disorders. miRNAs play an acknowledged role in the strongest of genetic risk factors for schizophrenia, 22q11.2 deletions. We hypothesized that in schizophrenia there would be an enrichment of other rare copy number variants (CNVs) that overlap miRNAs.
Methods
Using high-resolution genome-wide microarrays and rigorous methods, we compared the miRNA content of rare CNVs in well-characterized cohorts of schizophrenia cases (n = 420) and comparison subjects, excluding 22q11.2 CNVs. We also performed a gene-set enrichment analysis of the predicted miRNA target genes.
Results
The schizophrenia group was enriched for the proportion of individuals with a rare CNV overlapping a miRNA (3.29-fold increase over comparison subjects, p < .0001). The presence of a rare CNV overlapping a miRNA remained a significant predictor of schizophrenia case status (p = .0072) in a multivariate logistic regression model correcting for total CNV size. In contrast, comparable analyses correcting for CNV size showed no enrichment of rare CNVs overlapping protein-coding genes. A gene-set enrichment analysis indicated that predicted target genes of recurrent CNV-overlapped miRNAs in schizophrenia may be functionally enriched for neurodevelopmental processes, including axonogenesis and neuron projection development. Predicted gene targets driving these results included CAPRIN1, NEDD4, NTRK2, PAK2, RHOA, and SYNGAP1.
Conclusions
These data are the first to demonstrate a genome-wide role for CNVs overlapping miRNAs in the genetic risk for schizophrenia. The results provide support for an expanded multihit model of causation, with potential implications for miRNA-based therapeutics.
Rare structural genetic changes (copy number variants [CNVs]) contribute to genetic risk for neurodevelopmental/neuropsychiatric disorders such as schizophrenia (
). Large CNVs are more likely to disrupt multiple genes simultaneously, supporting a “multiple-hit” hypothesis in schizophrenia; further evidence has shown significant enrichment in schizophrenia of individuals with two or more rare CNVs of any size that overlap coding sequences (
). Another potential mechanism for disruption of multiple gene targets involves changes in expression of regulatory elements such as microRNAs (miRNAs) (
). Through multiple mechanisms affecting transcription and translation, miRNAs can regulate the expression of suites of genes important for development and lifelong cellular functioning (
Several lines of evidence support a role for miRNAs in the etiology of schizophrenia, including the elevated (~25-fold) risk imparted by the most common of the pathogenic recurrent CNVs, a 22q11.2 deletion (
). Reduced dosage of this gene causes global alterations in the miRNA complement, a potential mechanism for the high risk of schizophrenia with 22q11.2 deletions that is supported by mouse models (
) studies provide further support implicating miRNAs in the pathogenesis of schizophrenia.
In the present study, we directly investigate for the first time the genome-wide miRNA content of rare CNVs in schizophrenia. We hypothesized that, excluding the known effect of rare 22q11.2 CNVs, individuals with schizophrenia would be enriched for rare CNVs that overlap miRNAs, even when accounting for CNV size. We also examined whether the target genes of the miRNAs overlapped by rare CNVs in schizophrenia are more likely to be involved in neurodevelopmental processes.
Methods and Materials
Schizophrenia Cases and Ontario Population Genomics Platform Comparison Samples
), we have studied structural variants in a prospectively recruited cohort of unrelated Canadian patients of European ancestry meeting DSM-IV criteria for chronic schizophrenia or schizoaffective disorder, herein termed schizophrenia cases. The study was approved by local research ethics boards, and written informed consent was obtained for each participant. Case subjects with 22q11.2 deletions were a priori excluded to prevent findings from being driven by these established risk variants (
). The comparison sample comprised unrelated adults of European ancestry who are members of the Ontario Population Genomics Platform (OPGP) genetic epidemiological project (Supplemental Methods in Supplement 1) (
) and independent of the 2357 controls used for adjudication of CNV rarity (see below). In the present study of miRNA content of rare CNVs, we considered only schizophrenia case and OPGP comparison subjects with at least one rare CNV and excluded one comparison subject with a 22q11.2 duplication that was reciprocal to the 22q11.2 deletions (
) and in Supplemental Methods in Supplement 1. Briefly, genotyping and CNV analyses for all schizophrenia case and OPGP comparison samples were performed at the same laboratory using identical protocols. The Centre for Applied Genomics in Toronto, Canada, genotyped high-quality genomic DNA using the Affymetrix Genome-Wide Human SNP Array 6.0 (Affymetrix, Inc, Santa Clara, California). Only arrays that met the Affymetrix recommended quality control guideline of contrast quality control >.4 were used for further analysis. In this study, we consider only “stringent” CNVs: those detected by at least two of three CNV calling algorithms [Birdsuite (
). To adjudicate the rarity of CNVs identified in schizophrenia case and OPGP comparison subjects, we used the CNVs identified using the same platform in independent population-based control cohorts comprising 2357 individuals of European ancestry (
). “Rare” and “very rare” CNVs were defined as those present in <.1% and 0% of these population-based controls, respectively, using 50% reciprocal overlap criteria (
): of 58 rare CNVs in this data set tested using another method, all 58 (100%) were validated, and these spanned various sizes, including 17 (30%) <50 kb. There were 376 (of 420) case and 371 (of 415) comparison subjects with at least one rare CNV (
), each large (>500 kb) rare CNV in a schizophrenia case or OPGP comparison subject was assessed independently by two experienced clinical cytogenetic laboratory directors, blind to schizophrenia case status. The CNVs deemed to be clinically “Pathogenic” or of “Uncertain clinical significance; likely pathogenic” per established American College of Medical Genetics criteria (
Working Group of the American College of Medical Genetics Laboratory Quality Assurance Committee American College of Medical Genetics standards and guidelines for interpretation and reporting of postnatal constitutional copy number variants.
) were collectively termed “pathogenic,” and CNVs of “Uncertain clinical significance (no subclassification)” were termed “variants of unknown significance”; the remainder were termed “benign.”
miRNA Annotation and Burden Analyses
We previously published all rare CNVs in both the case and the comparison cohorts used (
To investigate the relative miRNA content of rare CNVs in case and comparison subjects, we used two complementary CNV burden analysis methods, as before restricting to autosomal CNVs (
). First, we compared the proportions of subjects with one or more rare CNVs that overlapped at least one miRNA. Second, we compared the proportions of all rare CNVs in case (n = 1052; 56.2% losses; 57.0% genic) and comparison (n = 954; 58.3% losses; 54.7% genic) subjects that overlapped at least one miRNA. Pearson χ2 tests, odds ratios (ORs), and 95% confidence intervals (CIs) were calculated using standard methods. We also constructed multivariate logistic regression models (Supplemental Methods in Supplement 1) to investigate potential confounding variables in these two burden analyses. The dependent variable was schizophrenia case status. In the first model, predictor variables were the presence of a rare CNV overlapping a miRNA in the individual (binary indicator variable) and either the total number of rare CNVs or the total size of the rare CNVs (in base pairs) in the individual. In the second model, predictor variables were the presence of a miRNA within the genomic extent of a rare CNV (binary indicator variable) and size of the rare CNV. All tests were two-sided, with statistical significance defined as p < .05. All statistical analyses were performed using R 3.0.1 software.
miRNA Target Prediction and Overrepresentation Analyses
We used a conservative strategy to examine predicted target genes of miRNAs overlapped by rare CNVs in case and comparison subjects, similar to the strategy used in other studies (
). These prediction tools employ varying score thresholds to produce target gene lists; we chose a stringent 97th percentile score for our analyses. We then investigated whether the predicted targets of miRNAs overlapped by rare CNVs in schizophrenia cases were enriched for genes involved in neurodevelopmental pathways, relative to the predicted target genes of miRNAs overlapped by rare CNVs in comparison subjects. For these analyses, we considered only targets of miRNAs that were overlapped by a rare CNV in two or more unrelated subjects with schizophrenia (“recurrent” case miRNAs). In OPGP comparison subjects, there were no such recurrent miRNAs, and we used the gene targets of all miRNAs overlapped by rare CNVs in these comparison subjects as the comparison group. We derived all functional “gene sets” from Gene Ontology (
), and BioCarta (http://www.biocarta.com). We compared the enrichment of all the functional gene sets in the predicted target genes for case and comparison subjects using Fisher exact tests, with statistical significance defined as p < .05. Because this analysis compares functional enrichment between schizophrenia case and OPGP comparison subject miRNA target genes, it is robust to functional biases in miRNA targets overall. To control the false discovery rate, we used the Benjamini-Hochberg correction.
Results
Increased Burden of CNVs Overlapping miRNAs in Schizophrenia Cases
In subjects with at least one rare autosomal CNV, the proportion with a rare CNV that overlapped a miRNA was significantly greater in schizophrenia cases than in comparison subjects (62 of 376 vs. 21 of 371; p < .0001; OR, 3.29 [95% CI, 1.96–5.52]) (Table 1). In a multivariate logistic regression model that included the total number of rare CNVs per subject as an additional predictor variable, the presence of a rare CNV overlapping a miRNA was the sole significant predictor of schizophrenia case status (p < .0001; OR, 3.15 [95% CI, 1.90–5.42]). In a logistic regression model including total size, rather than total number, of rare CNVs per subject, the presence of a rare CNV overlapping a miRNA remained a significant predictor (p = .0072; OR, 2.18 [95% CI, 1.25–3.90]) (Table 1), although total size of rare CNVs was also significant. In contrast, comparisons using the proportion of individuals with a rare CNV that overlapped a protein-coding gene showed no significant difference after correcting for total number or total size of rare CNVs per subject (p = .452 and p = .925, respectively). As previously reported (
Corrected for total CNV size through a logistic regression model including total size of rare CNVs per subject and presence of a rare CNV overlapping a miRNA as predictor variables.
Rare CNVs with <.1% prevalence in 2357 population controls of European ancestry (used to adjudicate CNVs in both schizophrenia cases and OPGP comparison subjects). Only CNVs >10 kb were considered (see Methods and Materials).
Rare CNVs with <.1% prevalence in 2357 population controls of European ancestry (used to adjudicate CNVs in both schizophrenia cases and OPGP comparison subjects). Only CNVs >10 kb were considered (see Methods and Materials).
Trend only result; all other results shown in this table are significant (p < .05).
(.98)
(3.91)
CI, 95% confidence interval of odds ratio; CNV, copy number variant; Corrected p, two-sided Wald p value; miRNA, microRNA; OPGP, Ontario Population Genomics Platform; No. miRNA, number of individuals with at least one rare autosomal CNV overlapping an miRNA; No. total, total number of individuals with one or more rare autosomal CNVs; OR, odds ratio; Uncorrected p, two-sided Pearson χ2p value.
a Corrected for total CNV size through a logistic regression model including total size of rare CNVs per subject and presence of a rare CNV overlapping a miRNA as predictor variables.
b Rare CNVs with <.1% prevalence in 2357 population controls of European ancestry (used to adjudicate CNVs in both schizophrenia cases and OPGP comparison subjects). Only CNVs >10 kb were considered (see Methods and Materials).
c Very rare CNVs with 0% prevalence in 2357 population controls.
d Trend only result; all other results shown in this table are significant (p < .05).
Results were similar using a different definition of the miRNA burden of rare CNVs. The proportion of rare CNVs that overlapped a miRNA was significantly greater in schizophrenia cases than in comparison subjects (67 of 1052 vs. 25 of 954; p < .0001; OR, 2.53 [95% CI, 1.58–4.04]) (Table S1 in Supplement 1). The presence of a miRNA within the genomic extent of a rare CNV remained a significant predictor of schizophrenia case status (p = .0189; OR, 1.83 [95% CI, 1.12–3.07]) (Table S1 in Supplement 1) in a multivariate logistic regression model that included rare CNV size as a significant predictor variable. Restricting to rare CNVs <500 kb in size, there was a nonsignificant trend toward an increased miRNA burden in cases after correcting for CNV size (p = .052) (Table S1 in Supplement 1). The proportion of rare CNVs that overlapped a protein-coding gene was not significantly different between case and comparison subjects after correcting for rare CNV size (p = .6156). These results were similar after restricting to rare CNVs <500 kb in size (data not shown).
The 67 rare CNVs in 62 schizophrenia cases overlapped 122 distinct miRNAs, including 25 (20.5%) miRNAs that were implicated by rare CNVs in two or more unrelated subjects (Table 2, Figure 1, and Table S2 in Supplement 1). These 25 miRNAs, termed “recurrent” miRNAs, involved CNVs at eight loci: 1q21.1, 2q13, 12q21.31, 14q32.33, 15q11-15q13, 16p11.2, 16p13.11, and 19q13.42. To our knowledge, this is the first report implicating these 25 miRNAs in neuropsychiatric disease. Of the 67 rare CNVs, 20 were large (>500 kb) and were previously clinically classified (
); 7 (35%) as clinically pathogenic; 12 (60%) as variants of unknown significance; and 1 (5%), at 14q32.33, as benign. The number of protein-coding genes overlapped by these 20 large rare CNVs varied greatly (median, 5.5; range, 0–40). In contrast, in the OPGP comparison subjects, only 35 distinct miRNAs were overlapped by rare CNVs, none of which were recurrent (Table S2 in Supplement 1). The proportion of recurrent rare CNV-overlapped miRNAs was significantly greater in schizophrenia cases than in comparison subjects (25 of 122 vs. 0 of 35; p = .0013). None of the miRNAs implicated by rare CNVs in schizophrenia cases was the same as miRNAs in comparison subjects.
Table 2Recurrent miRNAs (n = 25) Overlapped by Rare CNVs
See Methods and Materials for details. Our stringent criteria (97th percentile target prediction score and with genes predicted by two of three established tools) and the lack of available data for many miRNAs (e.g., miRNAs numbered above 5000) resulted in many miRNAs not having a reliable list of target genes.
As detailed in miRBase, hsa-miR-4771 is a mature miRNA located at two different cytobands: 2p11.2 and 2q13.
2p11.2 and 2q13
●
hsa-miR-5087
1q21.1
hsa-miR-6506-3p, hsa-miR-6506-5p
16p13.11
hsa-miR-6511a-3p, hsa-miR-6511a-5p
16p13.11
CNV, copy number variant; miRNA, microRNA.
a See Table S2 in Supplement 1 for additional details, including relevant CNV coordinates.
b All miRNAs overlapped by CNVs in n = 2 unrelated probands except for hsa-miR-3680-3p, hsa-miR-3680-5p (n = 4); hsa-miR-4435 (n = 3); hsa-miR-4508 (n = 3); hsa-miR-4715-3p, hsa-miR-4715-5p (n = 3); and hsa-miR-4771 (n = 4).
c See Methods and Materials for details. Our stringent criteria (97th percentile target prediction score and with genes predicted by two of three established tools) and the lack of available data for many miRNAs (e.g., miRNAs numbered above 5000) resulted in many miRNAs not having a reliable list of target genes.
d As detailed in miRBase, hsa-miR-3180, hsa-miR-3180-3p, and hsa-miR-3180-5p are distinct mature miRNAs.
e As detailed in miRBase, hsa-miR-4771 is a mature miRNA located at two different cytobands: 2p11.2 and 2q13.
Figure 1Genomic location of miRNAs overlapped by CNVs in schizophrenia cases. The genomic locations of the 122 schizophrenia case miRNAs that are overlapped by rare autosomal CNVs are shown. Red labels indicate the 97 nonrecurrent and blue labels the 25 recurrent miRNAs overlapped by rare CNVs in schizophrenia cases. The miRNA numbers correspond to the miRNA identification numbers in Table S2 in Supplement 1. As detailed in miRBase, hsa-miR-4771 (no. 88) is a mature miRNA located at two different cytobands: 2p11.2 and 2q13. CNVs, copy number variants; miRNA, microRNA.
Predicted Targets of miRNAs Implicated in Schizophrenia Cases Are Enriched for Neurodevelopmental Genes
There were 145 predicted target genes of the 25 recurrent rare CNV-overlapped miRNAs (Table S3 in Supplement 1) in schizophrenia cases and 387 predicted target genes of the 35 comparison subject miRNAs, including 9 genes common to both case and comparison subjects. Of the 145 case miRNA targets, three genes (SYNGAP1, GLT8D2, and GOLGA6L6) were predicted targets of ≥2 of the 25 miRNAs involved, excluding the overlap of target genes of hsa-miR-3180 and hsa-miR-3180-3p (Table S3 in Supplement 1). With our stringent cutoff and data currently available, there were no predicted target genes for the miRNAs overlapped by two recurrent pathogenic CNVs, 16p11.2 duplications and 1q21.1 duplications (Table 2).
The gene-set overrepresentation analysis comparing case with comparison subjects showed that 16 (80%) of the top 20 gene sets (each with nominally significant enrichment in schizophrenia) could be considered related to neurodevelopmental functions, including axonogenesis, neuron projection development, and neuron death (Figure 2; Table S4 in Supplement 1). Predicted gene targets driving these results included CAPRIN1 (
Myosin X and its motorless isoform differentially modulate dendritic spine development by regulating trafficking and retention of vasodilator-stimulated phosphoprotein.
). After correcting for multiple testing, none of the overrepresentation analyses had significant p values.
Figure 2Top 20 functional gene sets enriched in predicted targets of recurrent miRNAs overlapped by rare CNVs in schizophrenia. A functional map is shown of recurrent schizophrenia miRNAs overlapped by rare CNVs using results of the gene-set association analysis of predicted miRNA gene targets, displayed as a network of 20 gene sets (circles) related by mutual overlap (lines). The map was created using the Cytoscape plugin Enrichment Map (version 1.2)
, the gene-set enrichment table was loaded using the generic format interface, and gene-set overlaps were quantified using the Jaccard+Overlap combined coefficient with the filtering threshold set at .3. Circle color is proportional to the total number of “support” genes in each gene set based on the functional enrichment p value (inset), and line thickness represents the number of genes in common between two gene sets. Each support gene is a predicted target of a recurrent miRNA overlapped by more than one CNV in unrelated schizophrenia cases and not found as a miRNA target in Ontario Population Genomics Platform comparison subjects. Groups of functionally related gene sets are highlighted by large background circles and respective labels; three major neurodevelopmentally relevant functional groups are further highlighted by yellow filled circles. Support genes for schizophrenia are shown. CNVs, copy number variants; ER, endoplasmic reticulum; FET, the Fisher exact test; JAK-STAT, Janus kinase–signal transducer and activator of transcription; miRNA, microRNA.
To our knowledge, this is the first genome-wide study investigating CNVs that overlap miRNAs in schizophrenia. Consistent with preliminary findings in other diseases (
), the results showed both quantitative and qualitative CNV-related miRNA differences between schizophrenia cases and comparison subjects. There was a significant enrichment in schizophrenia cases of rare CNVs that overlap miRNAs. Also, the predicted target genes of the 25 CNV-overlapped miRNAs that were recurrent in unrelated subjects with schizophrenia tended to be involved in neurodevelopmental processes. These data provide further support for a model of causation in which simultaneous disruption of multiple genetic pathways may be necessary for expression of schizophrenia within an individual.
Delineating a miRNA Target Gene Network in Schizophrenia
Our results indicate that rare CNVs that disrupt DNA encoding a miRNA may contribute to causing schizophrenia in a substantial minority of individuals. Such disruption is predicted to affect the functioning of the miRNAs with respect to their target genes (
). Several of the top 20 miRNA target gene sets (Figure 2) show an overlap with the most relevant functional gene-set clusters identified previously for this cohort using the protein-coding genetic content of rare deletions (
). These gene sets include cell projection, axonogenesis, and neuron development. A gene represented in 10 of the top 20 recurrent CNV-overlapped miRNA target gene sets, CAPRIN1, is relevant to autism (
). Of the 26 predicted target genes of recurrent schizophrenia-related miRNAs identified in this study that were overrepresented in the top 20 gene sets (Figure 2), 8—CAPRIN1, KAL1, MAP2K7, MYO10, NTRK2, PRPF40A, SPRY3, and TSGA10—are differentially expressed in human dorsolateral prefrontal cortex (
). The laminin gene LAMB3, a target of the miRNA hsa-miR-484, is another promising candidate for schizophrenia, with other laminin genes previously implicated by rare CNVs and point mutations in schizophrenia (
). The increased burden of rare CNVs that overlap miRNAs in schizophrenia provides an alternative or enhanced explanation for the genetic disruption associated with rare CNVs because individual miRNAs can target diverse networks of genes. The fact that these genes may be anywhere in the rest of the genome and each subject to common and rare variation could help to explain the variability of expression of recurrent CNVs [e.g., 16p13.11 duplications (
)]. Also, in the schizophrenia cases, there were rare CNVs that overlapped miRNAs but no protein-coding genes located at eight loci: 3p11.2, 3q26.1, 9p21.1, 10p15.1, 10q26.3, 13q31.3, 14q32.33, and 18q12.2-18q12.3 (Table S2 in Supplement 1). Although such miRNA-related CNVs may confer risk for schizophrenia, they would not be reliably identified in exome sequencing studies.
), in the present study, we applied identical molecular and conservative analytic methods to unrelated cases and to a similar-sized Canadian sample of epidemiologic comparison subjects. Advantages derived from our sampling strategy are detailed elsewhere (
). With respect to the CNVs themselves, our strategy emphasizes CNVs likely to have an enhanced effect size because we use a stringent level to define rarity (<.1%). Although this strategy could mean missing more common variants with a lower effect size, using an independent population-based control set to adjudicate rarity ensured equal effects for both cases and OPGP comparison subjects and similar expected rates of type 1 and 2 errors. Our methods show high validation rates for rare CNVs (
), including 100% (58 of 58) in this data set. Most case and comparison subject rare CNVs that overlapped miRNAs (79.1% of case subjects and 84.0% of comparison subjects) were >100 kb in size, further increasing confidence in the CNV results. We ensured that the miRNA-related results were not driven by effects of 22q11.2 deletions by excluding a priori subjects with these CNVs (
). Although outside the scope of this initial genome-wide case-control study, it would be important to investigate specifically the potential role of miRNAs in other established large rare CNVs associated with schizophrenia. However, the a priori exclusion of all such CNVs would reduce the power of the analyses undertaken and be premature at this point given the lack of data on effects of these CNVs related to their miRNA content. A related issue is that there may be different risks for neuropsychiatric disorders when considering losses and gains (deletions and duplications) at the same genomic locus separately (
). However, predicting the related effects of loss or gain CNVs on expression of protein-coding genes is not straightforward (e.g., a deletion does not always mean reduced expression of overlapped genes) (
A limitation faced by all current miRNA studies is that one must rely on target gene prediction tools, given that there are limited validated gene target data available, and even then the miRNA expression studies that are the gold standard may be imperfect (
). Also, miRNAs are being identified daily, and the prediction tools are limited by the data available on these miRNAs and their gene targets. To minimize false positive targets, we adopted a high level of stringency and considered only gene targets predicted by at least two of three established prediction tools, treating case and comparison cohorts equally. Although imperfect, the integration of more than one prediction method tends to balance out the precision and recall, resulting in better accuracy and coverage of predictions (
). As a result of these factors, true target genes of the miRNAs observed were likely missed. No predicted target genes were found for 34.4% of case and 28.6% of comparison subject miRNAs, including none for more recently discovered miRNAs numbered above 5000 (Table S2 in Supplement 1). In time, improved annotation of experimentally validated miRNA target genes may facilitate the reanalysis of these CNV data and could minimize both false-positive and false-negative targets and potentially assess protein translation effects. Although the sample size of this study had sufficient power to show significant results for the main burden analyses, a larger cohort is required to refine further the set of candidate miRNAs recurrent in schizophrenia but not comparison subjects for gene-set enrichment analyses. Nevertheless, the gene-set enrichment results align with previous reports for protein-coding genes (
In conclusion, these findings provide further support for a model of the genetic causation of schizophrenia that extends beyond protein-coding genes. The apparent significance of the miRNA content of rare CNVs has implications for the interpretation of rare structural variants in schizophrenia and the current and potential future yield of clinical microarray testing (
). Rare single nucleotide variants in miRNAs and their target genes may similarly play an important role in schizophrenia. Whole-genome sequencing in this cohort is a logical future consideration. Lastly, in light of developments regarding miRNA-based therapeutics in other diseases such as cancer (
), these results suggest that a similar approach to novel treatment design could hold promise in schizophrenia.
Acknowledgments and Disclosures
This work was supported by the Canadian Institutes of Health Research Grant Nos. MOP-89066 and MOP-111238 (to ASB) as well as grants from the University of Toronto McLaughlin Centre, NeuroDevNet, Genome Canada and the Ontario Genomics Institute, the Canadian Institutes of Health Research, the Canadian Institute for Advanced Research, the Canada Foundation for Innovation, the government of Ontario, Autism Speaks, and The Hospital for Sick Children Foundation (to SWS). SWS holds the GlaxoSmithKline–Canadian Institutes of Health Research Chair in Genome Sciences at the University of Toronto and The Hospital for Sick Children. ASB holds the Canada Research Chair in Schizophrenia Genetics and Genomic Disorders and the Dalglish Chair in 22q11.2 Deletion Syndrome.
We thank the patients and their families for their participation; colleagues for referring patients; research assistants; staff at Saint John Community Mental Health Services, Saint John Regional Hospital, Hillsborough Hospital, and Hamilton clinic; staff at The Centre for Applied Genomics; and fellows and students who assisted in the collection and analysis of data for the study. We thank A. Fiebig, A. Franke, and S. Schreiber at PopGen (University of Kiel, Germany) and A. Stewart, R. McPherson, and R. Roberts of the University of Ottawa Heart Institute (University of Ottawa, Canada) for providing comparison subject data. We thank S. Bekeschus for figure design and formatting and A. Lionel (The Centre for Applied Genomics) for critical discussion.
SWS is on the Scientific Advisory Board of Population Diagnostics, Inc., and is a cofounder of YouNique Genomics. All other authors report no biomedical financial interests or potential conflicts of interest.
Myosin X and its motorless isoform differentially modulate dendritic spine development by regulating trafficking and retention of vasodilator-stimulated phosphoprotein.
The study by Warnica et al. (1) investigates a clever and straightforward idea. Of course, there is substantial evidence to support the role of rare genomic copy number variants (CNVs) in schizophrenia and other neuropsychiatric disease (2). A role for specific microRNA pathways (miRNAs) in schizophrenia has also been convincingly demonstrated in a variety of ways (3,4). For example, the well-known schizophrenia-associated deletion at 22q11.2 includes both miR-185 as well as the gene DGCR8, a component of the microprocessor complex that is involved in the initial step of miRNA biogenesis.
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