0
We're unable to sign you in at this time. Please try again in a few minutes.
Retry
We were able to sign you in, but your subscription(s) could not be found. Please try again in a few minutes.
Retry
There may be a problem with your account. Please contact the AMA Service Center to resolve this issue.
Contact the AMA Service Center:
Telephone: 1 (800) 262-2350 or 1 (312) 670-7827  *   Email: subscriptions@jamanetwork.com
Error Message ......
Original Investigation |

Methylome-Wide Association Study of Schizophrenia:  Identifying Blood Biomarker Signatures of Environmental Insults FREE

Karolina A. Aberg, PhD1; Joseph L. McClay, PhD1; Srilaxmi Nerella, MS1; Shaunna Clark, PhD1; Gaurav Kumar, PhD1; Wenan Chen, PhD2; Amit N. Khachane, PhD1; Linying Xie, MS1; Alexandra Hudson, BS1; Guimin Gao, PhD2; Aki Harada, PhD1; Christina M. Hultman, MD3; Patrick F. Sullivan, MD3,4; Patrik K. E. Magnusson, PhD3; Edwin J. C. G. van den Oord, PhD1
[+] Author Affiliations
1Center for Biomarker Research and Personalized Medicine, Virginia Commonwealth University, Richmond
2Department of Biostatistics, Virginia Commonwealth University, Richmond
3Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
4Departments of Genetics and Psychiatry, University of North Carolina at Chapel Hill
JAMA Psychiatry. 2014;71(3):255-264. doi:10.1001/jamapsychiatry.2013.3730.
Text Size: A A A
Published online

Importance  Epigenetic studies present unique opportunities to advance schizophrenia research because they can potentially account for many of its clinical features and suggest novel strategies to improve disease management.

Objective  To identify schizophrenia DNA methylation biomarkers in blood.

Design, Setting, and Participants  The sample consisted of 759 schizophrenia cases and 738 controls (N = 1497) collected in Sweden. We used methyl-CpG–binding domain protein-enriched genome sequencing of the methylated genomic fraction, followed by next-generation DNA sequencing. We obtained a mean (SD) number of 68 (26.8) million reads per sample. This massive data set was processed using a specifically designed data analysis pipeline. Critical top findings from our methylome-wide association study (MWAS) were replicated in independent case-control participants using targeted pyrosequencing of bisulfite-converted DNA.

Main Outcomes and Measures  Status of schizophrenia cases and controls.

Results  Our MWAS suggested a considerable number of effects, with 25 sites passing the highly conservative Bonferroni correction and 139 sites significant at a false discovery rate of 0.01. Our top MWAS finding, which was located in FAM63B, replicated with P = 2.3 × 10−10. It was part of the networks regulated by microRNA that can be linked to neuronal differentiation and dopaminergic gene expression. Many other top MWAS results could be linked to hypoxia and, to a lesser extent, infection, suggesting that a record of pathogenic events may be preserved in the methylome. Our findings also implicated a site in RELN, one of the most frequently studied candidates in methylation studies of schizophrenia.

Conclusions and Relevance  To our knowledge, the present study is one of the first MWASs of disease with a large sample size using a technology that provides good coverage of methylation sites across the genome. Our results demonstrated one of the unique features of methylation studies that can capture signatures of environmental insults in peripheral tissues. Our MWAS suggested testable hypotheses about disease mechanisms and yielded biomarkers that can potentially be used to improve disease management.

Figures in this Article

The methylation of DNA cytosine residues at the carbon 5 position is a common epigenetic modification that is often found in the sequence context CpG. Investigations of these markings provide a promising complement to schizophrenia studies of DNA sequence variation. First, methylation can directly affect gene expression, so it may capture additional variation in disease susceptibility. Indeed, specific epimutations have already been associated with human diseases, including psychiatric disorders.1 Second, methylation studies may advance our understanding of schizophrenia. For example, they can potentially account for a variety of features, such as its episodic nature.2,3 Third, the translational potential is considerable. For example, epigenetic markings are modifiable by pharmaceutical interventions, making them possible new drug targets.4

The pathogenic processes for psychiatric disorders likely involve the brain. However, brain tissue is not readily accessible in living patients, so blood is typically used in biomarker studies. There are 2 models explaining how methylation studies in blood can advance schizophrenia research.5 Neither model assumes that methylation in blood directly affects disease susceptibility, although this is possible, in principle, because blood provides a biological environment for other tissues, including the brain. In the “signature” model, associations occur between schizophrenia and methylation markings because the factors that increase disease susceptibility leave a biomarker signature in blood. Thus, the methylation markings in blood implicate a cause of the disease, which may affect schizophrenia through processes that are unrelated to methylation in the brain. In contrast, the “functional mirror site” model assumes a causal role of methylation sites in the brain. When the methylation status of these sites in the brain is mirrored by the corresponding sites in the blood, we will observe associations between schizophrenia and methylation markings at the same loci in blood. Compared with tissue-specific differentially methylated regions,6 correlated methylation profiles across tissues are common. Mirror sites occur because peripheral tissues may reveal methylation markings predating or resulting from the epigenetic reprogramming events affecting the germ line and embryogenesis,7 and environmental factors and genetic polymorphisms can affect methylation levels in multiple tissues.8,9 To study the 2 models,5 we administered haloperidol decanoate to inbred mice and then performed whole-methylome profiling in the blood, cortex, and hippocampus. More than 65% of the sites showed correlated changes where the concordance rates were similar between blood and brain vs between the 2 brain tissues. This showed that factors affecting brain processes (eg, haloperidol) can leave biomarker signatures in blood and that the methylation status of many sites in the brain is mirrored in the blood.

Current knowledge about the role of DNA methylation in schizophrenia is mainly acquired from relatively small studies of peripheral blood1017 and postmortem brain tissue.1826 Most studies focused on specific genes, such as RELN,19,22HTR2A,20COMT,13,18SOX10,23 and FOXP2.26 Two studies1,12 investigated a broader set of sites. One investigated approximately 12 000 regulatory regions in postmortem brain tissue samples from 35 patients with schizophrenia and 35 controls.1 It reported differences in the vicinity of loci that can be functionally linked to disease etiology. The second study12 investigated approximately 27 000 CpG sites in peripheral blood from 11 pairs of monozygotic twins discordant for schizophrenia. Dempster et al12 observed significant epigenetic disruptions in biological networks relevant to psychiatric disease and neurodevelopment.

The goal of the present study is to identify schizophrenia methylation biomarkers in blood through a methylome-wide association study (MWAS). The most comprehensive method involves the use of next-generation sequencing after bisulfite conversion of unmethylated cytosines. Currently, however, this is not economically feasible considering the sample sizes required for an MWAS.27 As a cost-effective alternative, we first captured the methylated DNA fragments and then sequenced this methylation-enriched portion of the genome28 (see Aberg et al29 for a discussion of the merits of methyl-CpG binding domain [MBD] protein-enriched genome sequencing [MBD-seq]). Our “discovery” MWAS sample consisted of almost 1500 schizophrenia cases and controls. Critical findings were replicated in an independent group of participants using targeted bisulfite pyrosequencing.

Detailed descriptions of the method can be found elsewhere.2931 Our study was approved by the institutional review board at Karolinska Institutet, Stockholm, Sweden, and written informed consent was obtained from all participants.

Sample

Table 1 describes the “discovery” MWAS and replication samples. All participants were selected from national population registers in Sweden and are part of a larger study.32 Because 3 participants withdrew their consent during the study, we report results for 1497 participants. Key findings were replicated in an independent group of 1144 participants and at other sites in an independent group of 360 participants. For all participants, DNA was extracted from the buffy coat of whole blood.

Table Graphic Jump LocationTable 1.  Key Findings of Methylome-Wide Association Study of Schizophreniaa
Whole-Methylome Profiling

We used MethylMiner (Invitrogen), which employs MBD protein-based enrichment of the methylated DNA fraction, followed by single-end sequencing (50 base-pair reads) on the SOLiD platform (Life Technologies). We eluted the captured methylated fraction with 0.5M sodium chloride to increase the relative number of fragments from CpG-poor regions,29 which otherwise would not be as well covered.33 To avoid batch effects, samples were processed in random order.

eTable 1 in the Supplement gives descriptive statistics for a variety of sequencing parameters. In summary, after deleting reads with more than 2 missing calls, we obtained a mean (SD) number of 68 (26.8) million reads per sample. Reads were aligned (build hg19/GRCh37) using BioScope 1.2 (Life Technologies). We deleted all samples with less than 40% alignment. For the remaining samples, the mean (SD) percentage of mapped reads was 69.2% (6.2%). We eliminated 32.1% of the mapped reads because they were low-quality multireads (reads aligning to multiple locations) or duplicate reads (reads with identical start positions). We excluded 38 participants because less than 15 million reads remained after quality control. This left 1459 participants with a mean (SD) number of 32.4 (13.7) million quality-control reads. Using data from 73 technical replicates, we observed a mean/median correlation of 0.90/0.92 between the methylation profiles from the replicates.29 This supported the reproducibility of our assay.

The MBD protein only binds to methylated CpG sites, so we only consider the 26 752 702 autosomal CpG sites in the reference genome for our analysis. The 10.5 million CpG sites (36%) located in regions showing alignment problems were eliminated.29 Most (71.8%) of these were in regions flagged as repetitive elements by RepeatMasker (http://www.repeatmasker.org/). Methylation measurements were obtained by estimating how many fragments covered each CpG site.31 Highly intercorrelated coverage estimates at adjacent CpG sites were combined to obtain more reliable measurements.34 Rather than using a sliding window of an arbitrary fixed length, we combined sites adaptively based on their observed intercorrelations.30 Using the 99th percentile of the coverage estimates at non-CpG sites29 as the threshold for background noise, we excluded 730 522 blocks with low coverage (likely unmethylated) from further analysis (eFigure 1 in the Supplement). This left 4 344 016 blocks for association testing.

Association Testing, Confounders, and Tissue Heterogeneity

A variety of efforts were made to control for confounders. First, we regressed out possible assay-related technical artifacts such as the quantity of genomic DNA starting material, the quantity of methylation-enriched DNA captured, and the sample batch. In addition, we controlled for age and sex.

Second, after regressing out the measured confounders, we performed principal component analysis to capture the major remaining unmeasured confounders. Because existing software cannot handle the ultrahigh-dimensional MWAS data, we used our own software30 that allows for parallel processing, that uses C++ for CPU-intensive and input/output-intensive calculations, and that follows Gower35 by performing the eigen-decomposition of a much smaller transposed variant of the data matrix. Based on a scree test (eFigure 2 in the Supplement), the first 7 principal components were regressed out of the association analysis.

Third, we correlated principal component scores with a variety of variables to check whether additional covariates were required (see Table 2 in Aberg et al29). For example, these analyses showed that, in this fairly homogeneous sample, ancestry did not contribute substantially to variation in the methylome, and it was therefore not included as a covariate.

Blood consists of a variety of cell types. By using whole-blood samples, we are studying an “average” methylation pattern that will be dominated by the common types. This can produce false positives only if both (1) the relative abundance of common cell types differs across cases and controls, and (2) methylation patterns of common cell types differ. Ideally, we would have case-control MBD-seq data obtained from separated white blood cells36 to identify sites that are at risk for creating false positives. The principal component analysis provides an alternative in situations where cell-type heterogeneity affects many methylation sites.3638 Participants with a similar cell-type composition will have more similar multilocus methylation patterns, and these patterns will be captured by the principal components. However, situations where few methylation sites are involved will remain uncorrected. We note, however, that most tissue samples will be heterogeneous, so similar risks are present when studying other tissues too.

Network Analyses

We used ConsensusPathDB3941 to generate protein-protein interaction (PPI) networks and perform pathway analyses based on the Reactome,42 Kyoto Encyclopedia of Genes and Genomes,43 and BioCarta databases. To create microRNA (miRNA) networks, we used the University of California, Santa Cruz, genome track TS miRNA site for GRCh37/hg19, which is based on TargetScan 5.1 (Bioinformatics and Research Computing). All blocks with q < 0.01 in the MWAS were matched to the closest gene ±20 kilobases. For each of the 4601 reference pathways present in ConsensusPathDB, incorporating 9859 known genes, a hypergeometric test was performed to study whether the overlap between the top MWAS genes and those present in each reference pathway was higher than expected by chance.

Replication

For the replication, we used targeted bisulfite pyrosequencing.44,45 We replicated the top 5 MWAS findings and 10 sites selected from the network analyses. Controlling the familywise error rate at the α level of .05 through a Bonferroni correction therefore gives a threshold of .05/15 = 3.3 × 10−3. We conservatively used the highest (least significant) P value if there were multiple (correlated) CpG sites in the same assay. Finally, we added a negative control by assaying a site with a high nonsignificant MWAS P value, and to assess the efficacy of the principal component analysis, we selected the 2 most significant findings obtained after performing the MWAS without principal components.

For network/pathways findings, a second “replication” opportunity existed by testing whether, after excluding the (top) findings used to identify the networks, the remaining genes from that network are also associated with case-control status in the MWAS (for miRNA networks, these tests are not suitable because miRNA likely regulates genes that may have different functions). For this purpose, we performed permutation tests.

Our Figure shows the MWAS Manhattan plot with 139 tests with q < 0.01, meaning that less than 1% of the 139 findings are expected to be false discoveries (eFigure 3 in the Supplement).46,47 The P values for these sites ranged from 10−7 to 10−11, with 25 sites reaching significance after we used the highly conservative Bonferroni correction (threshold P = 1.15 × 10−8). Our test statistic inflation parameter λ of 1.12 was higher compared with what is commonly observed in genome-wide association studies. This λ value is unlikely an artifact. After we performed a square root transformation to normalize the data and mitigate the effects of possible outliers, λ did not change. Furthermore, increasing the stringency of the quality control resulted in higher rather than lower λ values (eFigure 4 in the Supplement). Instead, this λ value reflects that methylation studies are more akin to gene expression studies that typically show many correlated effects with relatively large effect sizes.

Place holder to copy figure label and caption
Figure.
Methylome-Wide Association Study Manhattan Plot

The 22 autosomes are displayed along the x-axis, with the negative logarithm of the association P value for each block displayed on the y-axis. All P values above the upper (red) line have q values of less than 0.01, and those above the lower (blue) line have q values of less than 0.1.

Graphic Jump Location

Of the 139 MWAS findings, 112 overlapped with genes. Table 2 shows that regardless of whether we used PPI networks, pathway databases, or miRNA target networks, hypoxia was the dominant theme. For example, the PPI network centered on EPAS1 (previously known as hypoxia-inducible factor 2) includes 2 genes, both of which were detected in our MWAS. EPAS1 encodes a transcription factor induced as oxygen levels fall and is known to specifically interact with ETS1, another center for a PPI network among our findings, which is involved in the regulation of vascular development in the neonatal mouse brain.48 Furthermore, transcription coactivator EP300 is necessary for hypoxia-induced transcriptional activation and is upregulated in low-oxygen conditions.49 Using reference biological pathways, we detected the hypoxia-inducible factor 1 alpha (HIF1A) transcription factor network. HIF1A, together with ARNT, forms hypoxia-inducible factor (HIF), which regulates hypoxia-inducible genes.50 In addition, AKT signaling is an important modulator of HIF activity,51 and signaling by Rho GTPases has been linked to hypoxia response, particularly in the vascular system.52 Finally, miRNA miR-217 regulates heme oxygenase 1, an enzyme responsive to hypoxic conditions.53

Table Graphic Jump LocationTable 2.  Gene Network Analyses of 139 Findings From MWAS of Schizophrenia

Other findings shown in Table 2 converge on immune system themes. A prominent example is IgA1 (encoded by IGHA1), which is highlighted by our PPI network analyses. Although several genes associated with this network (RUNX3, CREB1, and SMAD3) are involved in multiple pathways, FCAR is highly specific to IgA because it encodes the receptor for the Fc fragment of IgA. In blood, FCAR interacts with IgA to initiate inflammatory reactions and phagocytosis. Fcγ-mediated phagocytosis, related to the action of IgG, was also among the top findings in our pathway analysis.

Table 3 shows the replication results (for design features of pyrosequencing assays and for full replication results, see eTables 2 and 3, respectively, in the Supplement). Except for the control sites, the direction of effects was the same in the replication as in the MWAS. Although all 5 top findings had replication P values of less than .05, only FAM63B remained significant after applying our very conservative correction for multiple testing. FAM63B was our top MWAS finding with a P = 6.3 × 10−11 (q = 2.1 × 10−4). The replication assay contained 3 CpG sites. The highest P value (2.3 × 10−10) of these 3 CpG sites was below our multiple testing threshold of P = 3.3 × 10−3. Table 2 shows that FAM63B is part of 4 networks regulated by miRNA. Three types of these miRNA (miR-218, miR-9, and miR-504) can be linked to neuronal differentiation and dopaminergic gene expression.5456

Table Graphic Jump LocationTable 3.  Replication Results for MWAS Top Findings, Network Findings, Candidate Gene, and Control Sitesa

All genes selected from hypoxia pathways had a nominal P < .05. Whereas the most hypoxia-specific gene (ARNT) replicated after correcting for multiple testing, the most specific immune response–related gene, FCAR, was only nominally significant. To perform our second “replication” effort, we first removed the top MWAS findings that were used to detect the networks/pathways in the initial analyses and then performed 10 000 permutations to test whether the other genes in the implicated networks showed enrichment for small P values in the MWAS. For hypoxia networks created using PPIs, none of the test statistics obtained after permutation had a value more extreme than the observed test statistic (eFigure 5 in the Supplement). This implies a P < 1.0 × 10−4 (= 1/10 000), indicating that the MWAS results for the remaining group of network genes were more significant than expected under the null hypothesis. For the pathway analyses, the permutation test was also highly significant (P <.001; eFigure 5 in the Supplement). For the immune system, we combined PPI network and pathway results to avoid small sets of genes. None of the permutation test statistics had a value more extreme than the observed test statistic (P < 1.0 × 10−4; eFigure 6 in the Supplement).

Interestingly, a site in RELN had an MWAS q value of less than 0.1. RELN has previously been associated with schizophrenia via messenger RNA expression studies57,58 and, although some inconclusive results exist,25 is one of the most prominent schizophrenia candidate genes in methylation studies.19,22 Furthermore, support for a strong inverse correlation between RELN expression and promoter methylation has been observed in mice59 and humans.60Table 3 shows that the RELN site also replicated. Similar to previous findings,19,22 we observed increased levels of methylation in schizophrenia cases. Traditionally, methylation studies of RELN have focused on the promoter region. Our best finding was located in the first intron and did not directly overlap the previous findings.

Table 3 shows that our negative control did not replicate, nor did the 2 most significant sites obtained after we performed an MWAS without regressing out the principal components. This suggests that the principal components were useful to prevent false positives. Regressing out the covariates from Table 1 did not alter results (eTables 4, 5, and 6 in the Supplement). For example, cigarette smoking can result in impaired oxygen release to tissues,61 and nicotine can upregulate HIF1A.62 However, we did not observe correlations between the methylation of genes in hypoxia networks and smoking, nor did the inclusion of smoking status as a covariate change the replication results (eTable 4 in the Supplement).

Our top MWAS finding (FAM63B) replicated with a P = 2.3 × 10−10. It was part of the networks regulated by miRNA that can be linked to neuronal differentiation and dopaminergic gene expression,5456 functions of potential relevance for schizophrenia. Many of our other top MWAS results could be linked to hypoxia and sometimes infection. Replicated findings also implicated RELN, one of the most frequently studied candidates in methylation studies of schizophrenia.22 Interestingly, RELN is regulated by HIF1/2a and can therefore also be linked to hypoxia.63,64

The hypoxia findings were very robust. Regardless of whether we used PPI networks, pathway databases, or an miRNA target gene database, hypoxia was a dominant theme. The hypoxia genes replicated in independent samples using a different technology. Furthermore, genes that were not among the top findings in the MWAS but were in the hypoxia pathways were also significantly enriched for small P values. Although the scope and quality of our phenotype data were limited, smoking or other covariates did not account for the hypoxia findings. Although we can only speculate about the cause, we note that a substantial amount of literature exists showing that hypoxia during fetal development increases the risk of schizophrenia.3 It is known that environmentally induced methylation changes can be preserved over a prolonged period of time.65,66 One intriguing hypothesis is that early hypoxia events alter methylation profiles in blood DNA, traces of which are preserved in the adult patient.

Many MWAS results reflected environmental insults. Because environmental effects cannot alter sequence variation, these phenomena cannot be detected with genome-wide association studies or exome-sequencing studies. Although there was some thematic overlap (eg, genome-wide association studies have implicated genes involved in immune response67), this likely explains why we found genes that were different from those found in studies of sequence variants. To find overlapping loci, different analytical strategies may be required. For example, the DNA sequence can regulate methylation patterns,9,6870 and we are currently conducting analyses to find loci where these regulatory mechanisms may be disrupted in schizophrenia. Because these analyses combine sequence variants with methylation patterns, they are more likely to yield results that overlap with genome-wide association study findings. Methylation signatures of environmental insults may not impact gene expression in blood. Thus, whole transcriptome studies may not be able to capture the phenomena detected in this study; therefore, methylation studies provide unique possibilities compared with other technologies.

Our results demonstrate how methylation studies in whole blood can advance schizophrenia research. First, they suggest that a record of pathogenic events may be preserved in the methylome. Etiologically distinct disease subtypes may be distinguishable from each other with respect to prognosis, course, or response to treatment.71 The possibility of identifying these subtypes using methylation markers that tend to have large effect sizes and can be measured with cost-effective assays, using DNA from blood that is stable and easy to collect, would be of great clinical importance. Second, methylation studies can generate testable hypotheses about disease mechanisms. For example, the hypoxia findings show how methylation studies can point to disease-causing factors. As postulated by the “signature model,”5 the causal factors may affect schizophrenia through processes that have nothing to do with methylation (eg, possible causal mechanisms include disruption of the laminar organization of the cerebral cortex72). Our RELN finding possibly demonstrates the second possible model, in which the methylation status of disease-relevant sites in the brain is mirrored by the corresponding sites in the blood. Thus, previous studies19,22 have implicated methylation sites in RELN in schizophrenia using postmortem brain samples. The fact that we find this gene in whole blood provides a possible illustration of the “functional mirror-site model.”5

A variety of efforts were taken to control for potential confounders. Our results suggested genes related to hypoxia, the immune system, and brain function rather than genes that, for example, are potentially relevant to medication and life style differences. This suggests that our efforts worked satisfactorily. For biomarkers other than genetic variants, there is always the inherent risk of confounding effects. Experiments studying model systems in controlled environments (eg, cell culture) would be the next step to rule out confounders completely.

Studies have suggested that 30 to 60 million reads per sample may be sufficient to reveal valuable information for whole-genome methylation analysis.33,73 We obtained, on average, 68.0 million reads, of which 32.4 million high-quality reads (47.6%) remained after stringent quality control. The MWAS was performed on “blocks” that summed reads across correlated CpG sites to improve the reliability of the measurements. This appeared to be sufficient to detect methylation markers that replicated in independent samples. It is possible, however, that increasing the number of reads would allow the detection of sites (eg, in CpG-poor regions) that could currently not be measured reliably.

In summary, to our knowledge, the present study is one of the first MWASs of disease with a large sample size using a technology that provided good coverage of methylation sites across the genome. Our results demonstrate how methylation studies can suggest new avenues to increase our understanding of disease and yield biomarkers that can be used to potentially improve disease management.

Submitted for Publication: April 22, 2013; final revision received June 22, 2013; accepted July 29, 2013.

Corresponding Author: Edwin J. C. G. van den Oord, PhD, Center for Biomarker Research and Personalized Medicine, Virginia Commonwealth University, PO Box 980533, Richmond, VA 23298-0581 (ejvandenoord@vcu.edu).

Published Online: January 8, 2014. doi:10.1001/jamapsychiatry.2013.3730.

Author Contributions: Dr van den Oord had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Study concept and design: Aberg, McClay, Chen, Xie, Gao, Sullivan, van den Oord.

Acquisition of data: Aberg, Hudson, Harada, Hultman, Magnusson, van den Oord.

Analysis and interpretation of data: Aberg, McClay, Nerella, Clark, Kumar, Chen, Khachane, Xie, Hudson, Gao, Hultman, Magnusson, van den Oord.

Drafting of the manuscript: Aberg, McClay, Nerella, Clark, Kumar, Hudson, Gao, Hultman, van den Oord.

Critical revision of the manuscript for important intellectual content: Aberg, Clark, Chen, Khachane, Xie, Harada, Sullivan, Magnusson, van den Oord.

Statistical analysis: Aberg, McClay, Clark, Chen, Gao, van den Oord,

Obtained funding: Aberg, Hultman, Sullivan, Magnusson, van den Oord.

Administrative, technical, or material support: Aberg, McClay, Nerella, Khachane, Hudson, Harada, Hultman, Magnusson, van den Oord.

Study supervision: Aberg, Khachane, Hultman, van den Oord.

Conflict of Interest Disclosures: None reported.

Funding/Support: This study was supported by the National Institute of Mental Health (grant RC2MH089996) and is part of a larger project entitled “A Large-Scale Schizophrenia Association Study in Sweden” that is supported by grants from the National Institute of Mental Health (grant MH077139) and the Stanley Medical Research Institute. The institutions involved in this project are the Karolinska Institutet, the Icahn School of Medicine at Mount Sinai in New York, the University of North Carolina at Chapel Hill, Virginia Commonwealth University, the Broad Institute in Cambridge, Massachusetts, and the US National Institute of Mental Health in Bethesda, Maryland.

Role of the Sponsor: The sponsors had no role in the design and conduct of the study; collection, management, analysis, or interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Additional Contributions: Library construction and next-generation sequencing were performed by EdgeBio. We thank the Swedish Schizophrenia Consortium and Life Technologies for their advice.

Mill  J, Tang  T, Kaminsky  Z,  et al.  Epigenomic profiling reveals DNA-methylation changes associated with major psychosis. Am J Hum Genet. 2008;82(3):696-711.
PubMed   |  Link to Article
Mill  J, Petronis  A.  Molecular studies of major depressive disorder: the epigenetic perspective. Mol Psychiatry. 2007;12(9):799-814.
PubMed   |  Link to Article
van Os  J, Kapur  S.  Schizophrenia. Lancet. 2009;374(9690):635-645.
PubMed   |  Link to Article
Boks  MP, de Jong  NM, Kas  MJ,  et al.  Current status and future prospects for epigenetic psychopharmacology. Epigenetics. 2012;7(1):20-28.
PubMed   |  Link to Article
Aberg  KA, Xie  LY, McClay  JL,  et al.  Testing two models describing how methylome-wide studies in blood are informative for psychiatric conditions. Epigenomics. 2013;5(4):367-377.
PubMed   |  Link to Article
Christensen  BC, Houseman  EA, Marsit  CJ,  et al.  Aging and environmental exposures alter tissue-specific DNA methylation dependent upon CpG island context. PLoS Genet. 2009;5(8):e1000602.
PubMed   |  Link to Article
Efstratiadis  A.  Parental imprinting of autosomal mammalian genes. Curr Opin Genet Dev. 1994;4(2):265-280.
PubMed   |  Link to Article
Sutherland  JE, Costa  M.  Epigenetics and the environment. Ann N Y Acad Sci. 2003;983:151-160.
PubMed   |  Link to Article
Kerkel  K, Spadola  A, Yuan  E,  et al.  Genomic surveys by methylation-sensitive SNP analysis identify sequence-dependent allele-specific DNA methylation. Nat Genet. 2008;40(7):904-908.
PubMed   |  Link to Article
Carrard  A, Salzmann  A, Malafosse  A, Karege  F.  Increased DNA methylation status of the serotonin receptor 5HTR1A gene promoter in schizophrenia and bipolar disorder. J Affect Disord. 2011;132(3):450-453.
PubMed   |  Link to Article
Chen  Y, Zhang  J, Zhang  L, Shen  Y, Xu  Q.  Effects of MAOA promoter methylation on susceptibility to paranoid schizophrenia. Hum Genet. 2012;131(7):1081-1087.
PubMed   |  Link to Article
Dempster  EL, Pidsley  R, Schalkwyk  LC,  et al.  Disease-associated epigenetic changes in monozygotic twins discordant for schizophrenia and bipolar disorder. Hum Mol Genet. 2011;20(24):4786-4796.
PubMed   |  Link to Article
Lott  SA, Burghardt  PR, Burghardt  KJ, Bly  MJ, Grove  TB, Ellingrod  VL.  The influence of metabolic syndrome, physical activity and genotype on catechol-O-methyl transferase promoter-region methylation in schizophrenia. Pharmacogenomics J. 2013;13(3):264-271.
PubMed   |  Link to Article
Melas  PA, Rogdaki  M, Ösby  U, Schalling  M, Lavebratt  C, Ekström  TJ.  Epigenetic aberrations in leukocytes of patients with schizophrenia: association of global DNA methylation with antipsychotic drug treatment and disease onset. FASEB J. 2012;26(6):2712-2718.
PubMed   |  Link to Article
Petronis  A, Gottesman  II, Kan  P,  et al.  Monozygotic twins exhibit numerous epigenetic differences: clues to twin discordance? Schizophr Bull. 2003;29(1):169-178.
PubMed   |  Link to Article
Zhang  AP, Yu  J, Liu  JX,  et al.  The DNA methylation profile within the 5′-regulatory region of DRD2 in discordant sib pairs with schizophrenia. Schizophr Res. 2007;90(1-3):97-103.
PubMed   |  Link to Article
Scarr  E, Craig  JM, Cairns  MJ,  et al.  Decreased cortical muscarinic M1 receptors in schizophrenia are associated with changes in gene promoter methylation, mRNA and gene targeting microRNA. Transl Psychiatry. 2013;3:e230.
PubMed   |  Link to Article
Abdolmaleky  HM, Cheng  KH, Faraone  SV,  et al.  Hypomethylation of MB-COMT promoter is a major risk factor for schizophrenia and bipolar disorder. Hum Mol Genet. 2006;15(21):3132-3145.
PubMed   |  Link to Article
Abdolmaleky  HM, Cheng  KH, Russo  A,  et al.  Hypermethylation of the reelin (RELN) promoter in the brain of schizophrenic patients: a preliminary report. Am J Med Genet B Neuropsychiatr Genet. 2005;134B(1):60-66.
PubMed   |  Link to Article
Abdolmaleky  HM, Yaqubi  S, Papageorgis  P,  et al.  Epigenetic dysregulation of HTR2A in the brain of patients with schizophrenia and bipolar disorder. Schizophr Res. 2011;129(2-3):183-190.
PubMed   |  Link to Article
Dempster  EL, Mill  J, Craig  IW, Collier  DA.  The quantification of COMT mRNA in post mortem cerebellum tissue: diagnosis, genotype, methylation and expression. BMC Med Genet. 2006;7:10.
PubMed   |  Link to Article
Grayson  DR, Jia  X, Chen  Y,  et al.  Reelin promoter hypermethylation in schizophrenia. Proc Natl Acad Sci U S A. 2005;102(26):9341-9346.
PubMed   |  Link to Article
Iwamoto  K, Bundo  M, Yamada  K,  et al.  DNA methylation status of SOX10 correlates with its downregulation and oligodendrocyte dysfunction in schizophrenia. J Neurosci. 2005;25(22):5376-5381.
PubMed   |  Link to Article
Nohesara  S, Ghadirivasfi  M, Mostafavi  S,  et al.  DNA hypomethylation of MB-COMT promoter in the DNA derived from saliva in schizophrenia and bipolar disorder. J Psychiatr Res. 2011;45(11):1432-1438.
PubMed   |  Link to Article
Tochigi  M, Iwamoto  K, Bundo  M,  et al.  Methylation status of the reelin promoter region in the brain of schizophrenic patients. Biol Psychiatry. 2008;63(5):530-533.
PubMed   |  Link to Article
Tolosa  A, Sanjuán  J, Dagnall  AM, Moltó  MD, Herrero  N, de Frutos  R.  FOXP2 gene and language impairment in schizophrenia: association and epigenetic studies. BMC Med Genet. 2010;11:114.
PubMed   |  Link to Article
Rakyan  VK, Down  TA, Balding  DJ, Beck  S.  Epigenome-wide association studies for common human diseases. Nat Rev Genet. 2011;12(8):529-541.
PubMed   |  Link to Article
Serre  D, Lee  BH, Ting  AH.  MBD-isolated Genome Sequencing provides a high-throughput and comprehensive survey of DNA methylation in the human genome. Nucleic Acids Res. 2010;38(2):391-399.
PubMed   |  Link to Article
Aberg  KA, McClay  JL, Nerella  S,  et al.  MBD-seq as a cost-effective approach for methylome-wide association studies: demonstration in 1500 case-control samples. Epigenomics. 2012;4(6):605-621.
PubMed   |  Link to Article
Chen  W, Gao  G, Nerella  S,  et al.  MethylPCA: a toolkit to control for confounders in methylome-wide association studies. BMC Bioinformatics. 2013;14:74.
PubMed   |  Link to Article
van den Oord  EJ, Bukszar  J, Rudolf  G,  et al.  Estimation of CpG coverage in whole methylome next-generation sequencing studies. BMC Bioinformatics. 2013;14(1):50.
PubMed   |  Link to Article
Bergen  SE, O’Dushlaine  CT, Ripke  S,  et al.  Genome-wide association study in a Swedish population yields support for greater CNV and MHC involvement in schizophrenia compared with bipolar disorder. Mol Psychiatry. 2012;17(9):880-886.
PubMed   |  Link to Article
Bock  C, Tomazou  EM, Brinkman  AB,  et al.  Quantitative comparison of genome-wide DNA methylation mapping technologies. Nat Biotechnol. 2010;28(10):1106-1114.
PubMed   |  Link to Article
Bollen  KA. Structural Equations With Latent Variables. New York, NY: Wiley; 1989.
Gower  JC.  Some distance properties of latent root and vector methods used in multivariate analysis. Biometrika. 1966;53:325-338. doi:10.2307/2333639.
Houseman  EA, Accomando  WP, Koestler  DC,  et al.  DNA methylation arrays as surrogate measures of cell mixture distribution. BMC Bioinformatics. 2012;13:86.
PubMed   |  Link to Article
Liu  Y, Aryee  MJ, Padyukov  L,  et al.  Epigenome-wide association data implicate DNA methylation as an intermediary of genetic risk in rheumatoid arthritis. Nat Biotechnol. 2013;31(2):142-147.
PubMed   |  Link to Article
Sun  YV, Turner  ST, Smith  JA,  et al.  Comparison of the DNA methylation profiles of human peripheral blood cells and transformed B-lymphocytes. Hum Genet. 2010;127(6):651-658.
PubMed   |  Link to Article
Kamburov  A, Pentchev  K, Galicka  H, Wierling  C, Lehrach  H, Herwig  R.  ConsensusPathDB: toward a more complete picture of cell biology. Nucleic Acids Res. 2011;39(Database issue):D712-D717.
PubMed   |  Link to Article
Pentchev  K, Ono  K, Herwig  R, Ideker  T, Kamburov  A.  Evidence mining and novelty assessment of protein-protein interactions with the ConsensusPathDB plugin for Cytoscape. Bioinformatics. 2010;26(21):2796-2797.
PubMed   |  Link to Article
Kamburov  A, Wierling  C, Lehrach  H, Herwig  R.  ConsensusPathDB—a database for integrating human functional interaction networks. Nucleic Acids Res. 2009;37(Database issue):D623-D628.
PubMed   |  Link to Article
Croft  D, O’Kelly  G, Wu  G,  et al.  Reactome: a database of reactions, pathways and biological processes. Nucleic Acids Res. 2011;39(Database issue):D691-D697.
PubMed   |  Link to Article
Kanehisa  M, Goto  S, Sato  Y, Furumichi  M, Tanabe  M.  KEGG for integration and interpretation of large-scale molecular data sets. Nucleic Acids Res. 2012;40(Database issue):D109-D114.
PubMed   |  Link to Article
Tost  J, Dunker  J, Gut  IG.  Analysis and quantification of multiple methylation variable positions in CpG islands by Pyrosequencing. Biotechniques. 2003;35(1):152-156.
PubMed
Aparicio  A, North  B, Barske  L,  et al.  LINE-1 methylation in plasma DNA as a biomarker of activity of DNA methylation inhibitors in patients with solid tumors. Epigenetics. 2009;4(3):176-184.
PubMed   |  Link to Article
Storey  JD.  The positive false discovery rate: a Bayesian interpretation and the q-value. Ann Stat. 2003;31(6):2013-2035. doi:10.1214/aos/1074290335.
Link to Article
Storey  JD, Tibshirani  R.  Statistical significance for genomewide studies. Proc Natl Acad Sci U S A. 2003;100(16):9440-9445.
PubMed   |  Link to Article
Elvert  G, Kappel  A, Heidenreich  R,  et al.  Cooperative interaction of hypoxia-inducible factor-2alpha (HIF-2alpha) and Ets-1 in the transcriptional activation of vascular endothelial growth factor receptor-2 (Flk-1). J Biol Chem. 2003;278(9):7520-7530.
PubMed   |  Link to Article
Tan  XL, Zhai  Y, Gao  WX,  et al.  p300 expression is induced by oxygen deficiency and protects neuron cells from damage. Brain Res. 2009;1254:1-9.
PubMed   |  Link to Article
Hu  CJ, Wang  LY, Chodosh  LA, Keith  B, Simon  MC.  Differential roles of hypoxia-inducible factor 1alpha (HIF-1alpha) and HIF-2alpha in hypoxic gene regulation. Mol Cell Biol. 2003;23(24):9361-9374.
PubMed   |  Link to Article
Wenger  RH.  Cellular adaptation to hypoxia: O2-sensing protein hydroxylases, hypoxia-inducible transcription factors, and O2-regulated gene expression. FASEB J. 2002;16(10):1151-1162.
PubMed   |  Link to Article
Xue  Y, Li  NL, Yang  JY, Chen  Y, Yang  LL, Liu  WC.  Phosphatidylinositol 3′-kinase signaling pathway is essential for Rac1-induced hypoxia-inducible factor-1(alpha) and vascular endothelial growth factor expression. Am J Physiol Heart Circ Physiol. 2011;300(6):H2169-H2176.
PubMed   |  Link to Article
Beckman  JD, Chen  C, Nguyen  J,  et al.  Regulation of heme oxygenase-1 protein expression by miR-377 in combination with miR-217. J Biol Chem. 2011;286(5):3194-3202.
PubMed   |  Link to Article
Sempere  LF, Freemantle  S, Pitha-Rowe  I, Moss  E, Dmitrovsky  E, Ambros  V.  Expression profiling of mammalian microRNAs uncovers a subset of brain-expressed microRNAs with possible roles in murine and human neuronal differentiation. Genome Biol. 2004;5(3):R13.
PubMed   |  Link to Article
Huang  T, Liu  Y, Huang  M, Zhao  X, Cheng  L.  Wnt1-cre-mediated conditional loss of Dicer results in malformation of the midbrain and cerebellum and failure of neural crest and dopaminergic differentiation in mice. J Mol Cell Biol. 2010;2(3):152-163.
PubMed   |  Link to Article
Huang  W, Li  MD.  Differential allelic expression of dopamine D1 receptor gene (DRD1) is modulated by microRNA miR-504. Biol Psychiatry. 2009;65(8):702-705.
PubMed   |  Link to Article
Guidotti  A, Auta  J, Davis  JM,  et al.  Decrease in reelin and glutamic acid decarboxylase67 (GAD67) expression in schizophrenia and bipolar disorder: a postmortem brain study [published correction appears in Arch Gen Psychiatry. 2002;59(1):12]. Arch Gen Psychiatry. 2000;57(11):1061-1069.
PubMed   |  Link to Article
Impagnatiello  F, Guidotti  AR, Pesold  C,  et al.  A decrease of reelin expression as a putative vulnerability factor in schizophrenia. Proc Natl Acad Sci U S A. 1998;95(26):15718-15723.
PubMed   |  Link to Article
Dong  E, Agis-Balboa  RC, Simonini  MV, Grayson  DR, Costa  E, Guidotti  A.  Reelin and glutamic acid decarboxylase67 promoter remodeling in an epigenetic methionine-induced mouse model of schizophrenia. Proc Natl Acad Sci U S A. 2005;102(35):12578-12583.
PubMed   |  Link to Article
Tamura  Y, Kunugi  H, Ohashi  J, Hohjoh  H.  Epigenetic aberration of the human REELIN gene in psychiatric disorders. Mol Psychiatry. 2007;12(6):593-600.
PubMed   |  Link to Article
Sørensen  LT, Jørgensen  S, Petersen  LJ,  et al.  Acute effects of nicotine and smoking on blood flow, tissue oxygen, and aerobe metabolism of the skin and subcutis. J Surg Res. 2009;152(2):224-230.
PubMed   |  Link to Article
Guo  L, Li  L, Wang  W, Pan  Z, Zhou  Q, Wu  Z.  Mitochondrial reactive oxygen species mediates nicotine-induced hypoxia-inducible factor-1α expression in human non-small cell lung cancer cells. Biochim Biophys Acta. 2012;1822(6):852-861.
PubMed   |  Link to Article
Schmidt-Kastner  R, van Os  J, Steinbusch  HWM, Schmitz  C.  Gene regulation by hypoxia and the neurodevelopmental origin of schizophrenia. Schizophr Res. 2006;84(2-3):253-271.
PubMed   |  Link to Article
Ralph  GS, Parham  S, Lee  SR,  et al.  Identification of potential stroke targets by lentiviral vector mediated overexpression of HIF-1 alpha and HIF-2 alpha in a primary neuronal model of hypoxia. J Cereb Blood Flow Metab. 2004;24(2):245-258.
PubMed   |  Link to Article
Murgatroyd  C, Patchev  AV, Wu  Y,  et al.  Dynamic DNA methylation programs persistent adverse effects of early-life stress. Nat Neurosci. 2009;12(12):1559-1566.
PubMed   |  Link to Article
Nestler  EJ.  Epigenetics: stress makes its molecular mark. Nature. 2012;490(7419):171-172.
PubMed   |  Link to Article
Sullivan  PF, Daly  MJ, O’Donovan  M.  Genetic architectures of psychiatric disorders: the emerging picture and its implications. Nat Rev Genet. 2012;13(8):537-551.
PubMed   |  Link to Article
Schilling  E, El Chartouni  C, Rehli  M.  Allele-specific DNA methylation in mouse strains is mainly determined by cis-acting sequences. Genome Res. 2009;19(11):2028-2035.
PubMed   |  Link to Article
Zhang  Y, Rohde  C, Reinhardt  R, Voelcker-Rehage  C, Jeltsch  A.  Non-imprinted allele-specific DNA methylation on human autosomes. Genome Biol. 2009;10(12):R138.
PubMed   |  Link to Article
Schalkwyk  LC, Meaburn  EL, Smith  R,  et al.  Allelic skewing of DNA methylation is widespread across the genome. Am J Hum Genet. 2010;86(2):196-212.
PubMed   |  Link to Article
Kennedy  JL, Farrer  LA, Andreasen  NC, Mayeux  R, St George-Hyslop  P.  The genetics of adult-onset neuropsychiatric disease: complexities and conundra? Science. 2003;302(5646):822-826.
PubMed   |  Link to Article
Herr  KJ, Herr  DR, Lee  CW, Noguchi  K, Chun  J.  Stereotyped fetal brain disorganization is induced by hypoxia and requires lysophosphatidic acid receptor 1 (LPA1) signaling. Proc Natl Acad Sci U S A. 2011;108(37):15444-15449.
PubMed   |  Link to Article
Chavez  L, Jozefczuk  J, Grimm  C,  et al.  Computational analysis of genome-wide DNA methylation during the differentiation of human embryonic stem cells along the endodermal lineage. Genome Res. 2010;20(10):1441-1450.
PubMed   |  Link to Article

Figures

Place holder to copy figure label and caption
Figure.
Methylome-Wide Association Study Manhattan Plot

The 22 autosomes are displayed along the x-axis, with the negative logarithm of the association P value for each block displayed on the y-axis. All P values above the upper (red) line have q values of less than 0.01, and those above the lower (blue) line have q values of less than 0.1.

Graphic Jump Location

Tables

Table Graphic Jump LocationTable 1.  Key Findings of Methylome-Wide Association Study of Schizophreniaa
Table Graphic Jump LocationTable 2.  Gene Network Analyses of 139 Findings From MWAS of Schizophrenia
Table Graphic Jump LocationTable 3.  Replication Results for MWAS Top Findings, Network Findings, Candidate Gene, and Control Sitesa

References

Mill  J, Tang  T, Kaminsky  Z,  et al.  Epigenomic profiling reveals DNA-methylation changes associated with major psychosis. Am J Hum Genet. 2008;82(3):696-711.
PubMed   |  Link to Article
Mill  J, Petronis  A.  Molecular studies of major depressive disorder: the epigenetic perspective. Mol Psychiatry. 2007;12(9):799-814.
PubMed   |  Link to Article
van Os  J, Kapur  S.  Schizophrenia. Lancet. 2009;374(9690):635-645.
PubMed   |  Link to Article
Boks  MP, de Jong  NM, Kas  MJ,  et al.  Current status and future prospects for epigenetic psychopharmacology. Epigenetics. 2012;7(1):20-28.
PubMed   |  Link to Article
Aberg  KA, Xie  LY, McClay  JL,  et al.  Testing two models describing how methylome-wide studies in blood are informative for psychiatric conditions. Epigenomics. 2013;5(4):367-377.
PubMed   |  Link to Article
Christensen  BC, Houseman  EA, Marsit  CJ,  et al.  Aging and environmental exposures alter tissue-specific DNA methylation dependent upon CpG island context. PLoS Genet. 2009;5(8):e1000602.
PubMed   |  Link to Article
Efstratiadis  A.  Parental imprinting of autosomal mammalian genes. Curr Opin Genet Dev. 1994;4(2):265-280.
PubMed   |  Link to Article
Sutherland  JE, Costa  M.  Epigenetics and the environment. Ann N Y Acad Sci. 2003;983:151-160.
PubMed   |  Link to Article
Kerkel  K, Spadola  A, Yuan  E,  et al.  Genomic surveys by methylation-sensitive SNP analysis identify sequence-dependent allele-specific DNA methylation. Nat Genet. 2008;40(7):904-908.
PubMed   |  Link to Article
Carrard  A, Salzmann  A, Malafosse  A, Karege  F.  Increased DNA methylation status of the serotonin receptor 5HTR1A gene promoter in schizophrenia and bipolar disorder. J Affect Disord. 2011;132(3):450-453.
PubMed   |  Link to Article
Chen  Y, Zhang  J, Zhang  L, Shen  Y, Xu  Q.  Effects of MAOA promoter methylation on susceptibility to paranoid schizophrenia. Hum Genet. 2012;131(7):1081-1087.
PubMed   |  Link to Article
Dempster  EL, Pidsley  R, Schalkwyk  LC,  et al.  Disease-associated epigenetic changes in monozygotic twins discordant for schizophrenia and bipolar disorder. Hum Mol Genet. 2011;20(24):4786-4796.
PubMed   |  Link to Article
Lott  SA, Burghardt  PR, Burghardt  KJ, Bly  MJ, Grove  TB, Ellingrod  VL.  The influence of metabolic syndrome, physical activity and genotype on catechol-O-methyl transferase promoter-region methylation in schizophrenia. Pharmacogenomics J. 2013;13(3):264-271.
PubMed   |  Link to Article
Melas  PA, Rogdaki  M, Ösby  U, Schalling  M, Lavebratt  C, Ekström  TJ.  Epigenetic aberrations in leukocytes of patients with schizophrenia: association of global DNA methylation with antipsychotic drug treatment and disease onset. FASEB J. 2012;26(6):2712-2718.
PubMed   |  Link to Article
Petronis  A, Gottesman  II, Kan  P,  et al.  Monozygotic twins exhibit numerous epigenetic differences: clues to twin discordance? Schizophr Bull. 2003;29(1):169-178.
PubMed   |  Link to Article
Zhang  AP, Yu  J, Liu  JX,  et al.  The DNA methylation profile within the 5′-regulatory region of DRD2 in discordant sib pairs with schizophrenia. Schizophr Res. 2007;90(1-3):97-103.
PubMed   |  Link to Article
Scarr  E, Craig  JM, Cairns  MJ,  et al.  Decreased cortical muscarinic M1 receptors in schizophrenia are associated with changes in gene promoter methylation, mRNA and gene targeting microRNA. Transl Psychiatry. 2013;3:e230.
PubMed   |  Link to Article
Abdolmaleky  HM, Cheng  KH, Faraone  SV,  et al.  Hypomethylation of MB-COMT promoter is a major risk factor for schizophrenia and bipolar disorder. Hum Mol Genet. 2006;15(21):3132-3145.
PubMed   |  Link to Article
Abdolmaleky  HM, Cheng  KH, Russo  A,  et al.  Hypermethylation of the reelin (RELN) promoter in the brain of schizophrenic patients: a preliminary report. Am J Med Genet B Neuropsychiatr Genet. 2005;134B(1):60-66.
PubMed   |  Link to Article
Abdolmaleky  HM, Yaqubi  S, Papageorgis  P,  et al.  Epigenetic dysregulation of HTR2A in the brain of patients with schizophrenia and bipolar disorder. Schizophr Res. 2011;129(2-3):183-190.
PubMed   |  Link to Article
Dempster  EL, Mill  J, Craig  IW, Collier  DA.  The quantification of COMT mRNA in post mortem cerebellum tissue: diagnosis, genotype, methylation and expression. BMC Med Genet. 2006;7:10.
PubMed   |  Link to Article
Grayson  DR, Jia  X, Chen  Y,  et al.  Reelin promoter hypermethylation in schizophrenia. Proc Natl Acad Sci U S A. 2005;102(26):9341-9346.
PubMed   |  Link to Article
Iwamoto  K, Bundo  M, Yamada  K,  et al.  DNA methylation status of SOX10 correlates with its downregulation and oligodendrocyte dysfunction in schizophrenia. J Neurosci. 2005;25(22):5376-5381.
PubMed   |  Link to Article
Nohesara  S, Ghadirivasfi  M, Mostafavi  S,  et al.  DNA hypomethylation of MB-COMT promoter in the DNA derived from saliva in schizophrenia and bipolar disorder. J Psychiatr Res. 2011;45(11):1432-1438.
PubMed   |  Link to Article
Tochigi  M, Iwamoto  K, Bundo  M,  et al.  Methylation status of the reelin promoter region in the brain of schizophrenic patients. Biol Psychiatry. 2008;63(5):530-533.
PubMed   |  Link to Article
Tolosa  A, Sanjuán  J, Dagnall  AM, Moltó  MD, Herrero  N, de Frutos  R.  FOXP2 gene and language impairment in schizophrenia: association and epigenetic studies. BMC Med Genet. 2010;11:114.
PubMed   |  Link to Article
Rakyan  VK, Down  TA, Balding  DJ, Beck  S.  Epigenome-wide association studies for common human diseases. Nat Rev Genet. 2011;12(8):529-541.
PubMed   |  Link to Article
Serre  D, Lee  BH, Ting  AH.  MBD-isolated Genome Sequencing provides a high-throughput and comprehensive survey of DNA methylation in the human genome. Nucleic Acids Res. 2010;38(2):391-399.
PubMed   |  Link to Article
Aberg  KA, McClay  JL, Nerella  S,  et al.  MBD-seq as a cost-effective approach for methylome-wide association studies: demonstration in 1500 case-control samples. Epigenomics. 2012;4(6):605-621.
PubMed   |  Link to Article
Chen  W, Gao  G, Nerella  S,  et al.  MethylPCA: a toolkit to control for confounders in methylome-wide association studies. BMC Bioinformatics. 2013;14:74.
PubMed   |  Link to Article
van den Oord  EJ, Bukszar  J, Rudolf  G,  et al.  Estimation of CpG coverage in whole methylome next-generation sequencing studies. BMC Bioinformatics. 2013;14(1):50.
PubMed   |  Link to Article
Bergen  SE, O’Dushlaine  CT, Ripke  S,  et al.  Genome-wide association study in a Swedish population yields support for greater CNV and MHC involvement in schizophrenia compared with bipolar disorder. Mol Psychiatry. 2012;17(9):880-886.
PubMed   |  Link to Article
Bock  C, Tomazou  EM, Brinkman  AB,  et al.  Quantitative comparison of genome-wide DNA methylation mapping technologies. Nat Biotechnol. 2010;28(10):1106-1114.
PubMed   |  Link to Article
Bollen  KA. Structural Equations With Latent Variables. New York, NY: Wiley; 1989.
Gower  JC.  Some distance properties of latent root and vector methods used in multivariate analysis. Biometrika. 1966;53:325-338. doi:10.2307/2333639.
Houseman  EA, Accomando  WP, Koestler  DC,  et al.  DNA methylation arrays as surrogate measures of cell mixture distribution. BMC Bioinformatics. 2012;13:86.
PubMed   |  Link to Article
Liu  Y, Aryee  MJ, Padyukov  L,  et al.  Epigenome-wide association data implicate DNA methylation as an intermediary of genetic risk in rheumatoid arthritis. Nat Biotechnol. 2013;31(2):142-147.
PubMed   |  Link to Article
Sun  YV, Turner  ST, Smith  JA,  et al.  Comparison of the DNA methylation profiles of human peripheral blood cells and transformed B-lymphocytes. Hum Genet. 2010;127(6):651-658.
PubMed   |  Link to Article
Kamburov  A, Pentchev  K, Galicka  H, Wierling  C, Lehrach  H, Herwig  R.  ConsensusPathDB: toward a more complete picture of cell biology. Nucleic Acids Res. 2011;39(Database issue):D712-D717.
PubMed   |  Link to Article
Pentchev  K, Ono  K, Herwig  R, Ideker  T, Kamburov  A.  Evidence mining and novelty assessment of protein-protein interactions with the ConsensusPathDB plugin for Cytoscape. Bioinformatics. 2010;26(21):2796-2797.
PubMed   |  Link to Article
Kamburov  A, Wierling  C, Lehrach  H, Herwig  R.  ConsensusPathDB—a database for integrating human functional interaction networks. Nucleic Acids Res. 2009;37(Database issue):D623-D628.
PubMed   |  Link to Article
Croft  D, O’Kelly  G, Wu  G,  et al.  Reactome: a database of reactions, pathways and biological processes. Nucleic Acids Res. 2011;39(Database issue):D691-D697.
PubMed   |  Link to Article
Kanehisa  M, Goto  S, Sato  Y, Furumichi  M, Tanabe  M.  KEGG for integration and interpretation of large-scale molecular data sets. Nucleic Acids Res. 2012;40(Database issue):D109-D114.
PubMed   |  Link to Article
Tost  J, Dunker  J, Gut  IG.  Analysis and quantification of multiple methylation variable positions in CpG islands by Pyrosequencing. Biotechniques. 2003;35(1):152-156.
PubMed
Aparicio  A, North  B, Barske  L,  et al.  LINE-1 methylation in plasma DNA as a biomarker of activity of DNA methylation inhibitors in patients with solid tumors. Epigenetics. 2009;4(3):176-184.
PubMed   |  Link to Article
Storey  JD.  The positive false discovery rate: a Bayesian interpretation and the q-value. Ann Stat. 2003;31(6):2013-2035. doi:10.1214/aos/1074290335.
Link to Article
Storey  JD, Tibshirani  R.  Statistical significance for genomewide studies. Proc Natl Acad Sci U S A. 2003;100(16):9440-9445.
PubMed   |  Link to Article
Elvert  G, Kappel  A, Heidenreich  R,  et al.  Cooperative interaction of hypoxia-inducible factor-2alpha (HIF-2alpha) and Ets-1 in the transcriptional activation of vascular endothelial growth factor receptor-2 (Flk-1). J Biol Chem. 2003;278(9):7520-7530.
PubMed   |  Link to Article
Tan  XL, Zhai  Y, Gao  WX,  et al.  p300 expression is induced by oxygen deficiency and protects neuron cells from damage. Brain Res. 2009;1254:1-9.
PubMed   |  Link to Article
Hu  CJ, Wang  LY, Chodosh  LA, Keith  B, Simon  MC.  Differential roles of hypoxia-inducible factor 1alpha (HIF-1alpha) and HIF-2alpha in hypoxic gene regulation. Mol Cell Biol. 2003;23(24):9361-9374.
PubMed   |  Link to Article
Wenger  RH.  Cellular adaptation to hypoxia: O2-sensing protein hydroxylases, hypoxia-inducible transcription factors, and O2-regulated gene expression. FASEB J. 2002;16(10):1151-1162.
PubMed   |  Link to Article
Xue  Y, Li  NL, Yang  JY, Chen  Y, Yang  LL, Liu  WC.  Phosphatidylinositol 3′-kinase signaling pathway is essential for Rac1-induced hypoxia-inducible factor-1(alpha) and vascular endothelial growth factor expression. Am J Physiol Heart Circ Physiol. 2011;300(6):H2169-H2176.
PubMed   |  Link to Article
Beckman  JD, Chen  C, Nguyen  J,  et al.  Regulation of heme oxygenase-1 protein expression by miR-377 in combination with miR-217. J Biol Chem. 2011;286(5):3194-3202.
PubMed   |  Link to Article
Sempere  LF, Freemantle  S, Pitha-Rowe  I, Moss  E, Dmitrovsky  E, Ambros  V.  Expression profiling of mammalian microRNAs uncovers a subset of brain-expressed microRNAs with possible roles in murine and human neuronal differentiation. Genome Biol. 2004;5(3):R13.
PubMed   |  Link to Article
Huang  T, Liu  Y, Huang  M, Zhao  X, Cheng  L.  Wnt1-cre-mediated conditional loss of Dicer results in malformation of the midbrain and cerebellum and failure of neural crest and dopaminergic differentiation in mice. J Mol Cell Biol. 2010;2(3):152-163.
PubMed   |  Link to Article
Huang  W, Li  MD.  Differential allelic expression of dopamine D1 receptor gene (DRD1) is modulated by microRNA miR-504. Biol Psychiatry. 2009;65(8):702-705.
PubMed   |  Link to Article
Guidotti  A, Auta  J, Davis  JM,  et al.  Decrease in reelin and glutamic acid decarboxylase67 (GAD67) expression in schizophrenia and bipolar disorder: a postmortem brain study [published correction appears in Arch Gen Psychiatry. 2002;59(1):12]. Arch Gen Psychiatry. 2000;57(11):1061-1069.
PubMed   |  Link to Article
Impagnatiello  F, Guidotti  AR, Pesold  C,  et al.  A decrease of reelin expression as a putative vulnerability factor in schizophrenia. Proc Natl Acad Sci U S A. 1998;95(26):15718-15723.
PubMed   |  Link to Article
Dong  E, Agis-Balboa  RC, Simonini  MV, Grayson  DR, Costa  E, Guidotti  A.  Reelin and glutamic acid decarboxylase67 promoter remodeling in an epigenetic methionine-induced mouse model of schizophrenia. Proc Natl Acad Sci U S A. 2005;102(35):12578-12583.
PubMed   |  Link to Article
Tamura  Y, Kunugi  H, Ohashi  J, Hohjoh  H.  Epigenetic aberration of the human REELIN gene in psychiatric disorders. Mol Psychiatry. 2007;12(6):593-600.
PubMed   |  Link to Article
Sørensen  LT, Jørgensen  S, Petersen  LJ,  et al.  Acute effects of nicotine and smoking on blood flow, tissue oxygen, and aerobe metabolism of the skin and subcutis. J Surg Res. 2009;152(2):224-230.
PubMed   |  Link to Article
Guo  L, Li  L, Wang  W, Pan  Z, Zhou  Q, Wu  Z.  Mitochondrial reactive oxygen species mediates nicotine-induced hypoxia-inducible factor-1α expression in human non-small cell lung cancer cells. Biochim Biophys Acta. 2012;1822(6):852-861.
PubMed   |  Link to Article
Schmidt-Kastner  R, van Os  J, Steinbusch  HWM, Schmitz  C.  Gene regulation by hypoxia and the neurodevelopmental origin of schizophrenia. Schizophr Res. 2006;84(2-3):253-271.
PubMed   |  Link to Article
Ralph  GS, Parham  S, Lee  SR,  et al.  Identification of potential stroke targets by lentiviral vector mediated overexpression of HIF-1 alpha and HIF-2 alpha in a primary neuronal model of hypoxia. J Cereb Blood Flow Metab. 2004;24(2):245-258.
PubMed   |  Link to Article
Murgatroyd  C, Patchev  AV, Wu  Y,  et al.  Dynamic DNA methylation programs persistent adverse effects of early-life stress. Nat Neurosci. 2009;12(12):1559-1566.
PubMed   |  Link to Article
Nestler  EJ.  Epigenetics: stress makes its molecular mark. Nature. 2012;490(7419):171-172.
PubMed   |  Link to Article
Sullivan  PF, Daly  MJ, O’Donovan  M.  Genetic architectures of psychiatric disorders: the emerging picture and its implications. Nat Rev Genet. 2012;13(8):537-551.
PubMed   |  Link to Article
Schilling  E, El Chartouni  C, Rehli  M.  Allele-specific DNA methylation in mouse strains is mainly determined by cis-acting sequences. Genome Res. 2009;19(11):2028-2035.
PubMed   |  Link to Article
Zhang  Y, Rohde  C, Reinhardt  R, Voelcker-Rehage  C, Jeltsch  A.  Non-imprinted allele-specific DNA methylation on human autosomes. Genome Biol. 2009;10(12):R138.
PubMed   |  Link to Article
Schalkwyk  LC, Meaburn  EL, Smith  R,  et al.  Allelic skewing of DNA methylation is widespread across the genome. Am J Hum Genet. 2010;86(2):196-212.
PubMed   |  Link to Article
Kennedy  JL, Farrer  LA, Andreasen  NC, Mayeux  R, St George-Hyslop  P.  The genetics of adult-onset neuropsychiatric disease: complexities and conundra? Science. 2003;302(5646):822-826.
PubMed   |  Link to Article
Herr  KJ, Herr  DR, Lee  CW, Noguchi  K, Chun  J.  Stereotyped fetal brain disorganization is induced by hypoxia and requires lysophosphatidic acid receptor 1 (LPA1) signaling. Proc Natl Acad Sci U S A. 2011;108(37):15444-15449.
PubMed   |  Link to Article
Chavez  L, Jozefczuk  J, Grimm  C,  et al.  Computational analysis of genome-wide DNA methylation during the differentiation of human embryonic stem cells along the endodermal lineage. Genome Res. 2010;20(10):1441-1450.
PubMed   |  Link to Article

Correspondence

CME
Also Meets CME requirements for:
Browse CME for all U.S. States
Accreditation Information
The American Medical Association is accredited by the Accreditation Council for Continuing Medical Education to provide continuing medical education for physicians. The AMA designates this journal-based CME activity for a maximum of 1 AMA PRA Category 1 CreditTM per course. Physicians should claim only the credit commensurate with the extent of their participation in the activity. Physicians who complete the CME course and score at least 80% correct on the quiz are eligible for AMA PRA Category 1 CreditTM.
Note: You must get at least of the answers correct to pass this quiz.
Please click the checkbox indicating that you have read the full article in order to submit your answers.
Your answers have been saved for later.
You have not filled in all the answers to complete this quiz
The following questions were not answered:
Sorry, you have unsuccessfully completed this CME quiz with a score of
The following questions were not answered correctly:
Commitment to Change (optional):
Indicate what change(s) you will implement in your practice, if any, based on this CME course.
Your quiz results:
The filled radio buttons indicate your responses. The preferred responses are highlighted
For CME Course: A Proposed Model for Initial Assessment and Management of Acute Heart Failure Syndromes
Indicate what changes(s) you will implement in your practice, if any, based on this CME course.
Submit a Comment

Multimedia

Supplement.

eFigure 1. Severity of block QC versus number of sites remaining in the analysis

eFigure 2. Scree plot from principal component analysis

eFigure 3. QQ plot

eFigure 4. Relation between severity of block QC and lambda

eFigure 5. Permutation test results for remaining hypoxia genes

eFigure 6. Permutation test results for remaining immune system genes

eTable 1. Descriptive statistics for sequencing parameters

eTable 2. Design features of pyrosequencing assays

eTable 3. Full replication results

eTable 4. Smoking and hypoxia: bivariate correlations and regression analyses

eTable 5. Alcohol: bivariate correlations and regression analyses

eTable 6. Narcotics: bivariate correlations and regression analyses

Supplemental Content

Some tools below are only available to our subscribers or users with an online account.

Web of Science® Times Cited: 23

Related Content

Customize your page view by dragging & repositioning the boxes below.

Articles Related By Topic
Related Collections
PubMed Articles