DNA methylation in a Scottish family multiply affected by bipolar disorder and major depressive disorder
- Rosie May Walker1,
- Andrea Nikie Christoforou1,
- Daniel L. McCartney1,
- Stewart W. Morris1,
- Nicholas A. Kennedy1,
- Peter Morten1,
- Susan Maguire Anderson1,
- Helen Scott Torrance1,
- Alix Macdonald2,
- Jessika Elizabeth Sussmann2,
- Heather Clare Whalley2,
- Douglas H. R. Blackwood2,
- Andrew Mark McIntosh2, 3,
- David John Porteous1, 3 and
- Kathryn Louise Evans1, 3Email author
© Walker et al. 2016
Received: 29 October 2015
Accepted: 11 January 2016
Published: 20 January 2016
Bipolar disorder (BD) is a severe, familial psychiatric condition. Progress in understanding the aetiology of BD has been hampered by substantial phenotypic and genetic heterogeneity. We sought to mitigate these confounders by studying a multi-generational family multiply affected by BD and major depressive disorder (MDD), who carry an illness-linked haplotype on chromosome 4p. Within a family, aetiological heterogeneity is likely to be reduced, thus conferring greater power to detect illness-related changes. As accumulating evidence suggests that altered DNA methylation confers risk for BD and MDD, we compared genome-wide methylation between (i) affected carriers of the linked haplotype (ALH) and married-in controls (MIs), (ii) well unaffected haplotype carriers (ULH) and MI, (iii) ALH and ULH and (iv) all haplotype carriers (LH) and MI.
Nominally significant differences in DNA methylation were observed in all comparisons, with differences withstanding correction for multiple testing when the ALH or LH group was compared to the MIs. In both comparisons, we observed increased methylation at a locus in FANCI, which was accompanied by increased FANCI expression in the ALH group. FANCI is part of the Fanconi anaemia complementation (FANC) gene family, which are mutated in Fanconi anaemia and participate in DNA repair. Interestingly, several FANC genes have been implicated in psychiatric disorders. Regional analyses of methylation differences identified loci implicated in psychiatric illness by genome-wide association studies, including CACNB2 and the major histocompatibility complex. Gene ontology analysis revealed enrichment for methylation differences in neurologically relevant genes.
Our results highlight altered DNA methylation as a potential mechanism by which the linked haplotype might confer risk for mood disorders. Differences in the phenotypic outcome of haplotype carriers might, in part, arise from additional changes in DNA methylation that converge on neurologically important pathways. Further work is required to investigate the underlying mechanisms and functional consequences of the observed differences in methylation.
KeywordsBipolar disorder Major depressive disorder DNA methylation 450K array FANCI Gene ontology analysis Family study
Bipolar disorder (BD) is severe psychiatric condition characterised by recurrent episodes of depression and mania. BD is highly heritable (h 2 = 0.85 ), and in recent years, progress has been made in identifying risk-associated variants through genome-wide association studies (GWASs) (reviewed in ). These risk variants are, however, of small effect and together only partially explain BD’s heritability [3, 4].
Many of the associated variants for BD fall within non-coding regions of the genome , implicating perturbed regulatory processes in the pathogenesis of this condition. Consistent with this observation, gene expression studies have identified several changes in individuals with BD [6, 7].
Some of these expression changes might be attributable to altered DNA methylation, which has been observed in individuals with BD, for example [8–12]. DNA methylation levels at many sites are, at least in part, under genetic control , indicating one potential mechanism by which regulatory variants might confer risk for BD. In addition, comparison of monozygotic twins who are discordant for BD, or the related psychiatric illness schizophrenia, has revealed several loci showing significant differences in DNA methylation . These differences might reflect the actions of environmental risk factors, which are believed to contribute to the imperfect concordance for BD observed in monozygotic twins.
To date, case-control studies of DNA methylation in BD have been limited either by a restricted focus on candidate genes [8–10, 12] or the study of a small group of unrelated individuals [15, 16]. Here, we capitalise on the increase in homogeneity conferred by the study of related individuals by assessing blood-based DNA methylation in a large Scottish family multiply affected by BD and major depressive disorder (MDD). Increased aetiological homogeneity within this family may confer greater statistical power to detect phenotypically relevant differences in DNA methylation. Previous analyses of this family have identified a ~20 Mb haplotype on chromosome 4p15-16, which shows linkage to BD and MDD (henceforth referred to as the linked haplotype (LH)) [17, 18] (maximum LOD score = 4.41). Ongoing analysis of the family, which now includes additional individuals, suggests that the LH acts in conjunction with genome-wide polygenic risk to confer risk for BD (Clarke et al., manuscript in preparation).
Here, we assessed the hypothesis that the LH confers risk via an effect on DNA methylation and identified four loci showing differences in methylation in carriers of the LH that withstood correction for multiple testing. Furthermore, as some carriers of the LH do not develop BD or MDD, we attempted to identify differences in DNA methylation that represent additional risk and/or protective loci that act in concert with the linked haplotype to determine an individual’s phenotype. In this comparison, no individual locus remained significant after correction for multiple testing; however, collectively, the most differentially methylated loci were found to map to genes involved in neurologically relevant functions. Together, these analyses aimed to assess the contribution of genetically and environmentally driven changes in DNA methylation to the development of BD and MDD.
Overview of research strategy
Genome-wide DNA methylation was measured in members of a large family multiply affected by BD and MDD, using the Infinium HumanMethylation450 BeadChip. Three groups of individuals were considered: affected (diagnosed with either BD or MDD) carriers of the linked haplotype (ALH; n = 10; (BD n = 5; MDD n = 5)); unaffected carriers of the linked haplotype (ULH; n = 10) and unaffected, non-haplotype-carrying married-in controls (MIs; n = 9). A series of comparisons was performed: ALH vs. MI, ULH vs. MI, ALH vs. ULH and LH (combined ALH and ULH) vs. MI. These comparisons permitted us to assess our hypotheses that (i) the linked haplotype confers an increase in risk for major affective disorders via an effect on DNA methylation and (ii) DNA methylation at certain loci correlates with the presence/absence of illness in haplotype carriers, reflecting the involvement of additional risk/protective loci. To gain an insight into the potential consequences of differential methylation, gene ontology analysis was carried out to identify biological processes and functions overrepresented amongst the most significantly differentially methylated loci.
Quality control and data filtering
As the performance of some probes on the array is known to be affected by the presence of single nucleotide polymorphisms (SNPs) or cross-hybridisation, these probes were removed prior to data analysis. Firstly, 17,955 probes in which the target CpG is located within 2 bp of a SNP (minor allele frequency ≥5 %) and 29,093 probes predicted to cross-hybridise  were removed from the dataset. The overall success of DNA methylation profiling was then assessed by considering plots representing the output of quality control probes, which measure the success of various stages of the profiling process. This revealed incomplete bisulphite conversion in two samples, which were omitted from downstream analyses. Finally, 2937 probes were removed as they had more than five samples with a bead count of less than three and/or ≥1 % of the samples had a detection p value of >0.05. At the end of this filtering process, the dataset comprised 435,889 probes measured in nine ALHs (BD n = 5; MDD n = 4), ten ULHs and eight MIs (Additional file 1: Table S1).
Selection of normalisation method
Twelve normalisation methods available in the R package wateRmelon  were ranked according to their ability to reduce noise attributable to technical error (Additional file 1: Table S2). Daten 2 was identified as the optimum normalisation method.
Assessment of between-group differences in cell composition
Due to the presence of cell type-specific DNA methylation patterns, individual differences in blood cellular composition can confound the assessment of methylation. As such, between-group differences in estimated cellular proportions were assessed. No significant differences were observed (p ≥ 0.276).
Surrogate variable analysis
To reduce the potentially confounding effects of unmeasured and/or unmodelled variables, surrogate variables (SVs) were estimated . This resulted in the identification of six significant SVs, which were fitted as covariates in the differentially methylated position (DMP) analysis.
Identification of DMPs
Differentially methylated positions identified when comparing the LH group with the MI group
Adjusted p valuec
2.54 × 10−7
2.99 × 10−7
3.85 × 10−7
6.58 × 10−7
Differentially methylated positions identified when comparing the ALH group with the MI group
Adjusted p valuec
1.93 × 10−7
3.26 × 10−7
Assessment of FANCI expression
Identification of DMRs
Summary of differentially methylated region (DMR) analyses
No. of probes included in DMR analysisa
No. of DMRs detected
ALH vs. MI
ULH vs. MI
ALH vs. ULH
LH vs. MI
Overlap between DMRs and regions implicated in schizophrenia by a recent large-scale GWAS 
PGC SCZ regiona
DMR p value
LH vs. MI
1.84 × 10−4
LH vs. MI
1.49 × 10−5
ULH vs. MI
1.85 × 10−5
ALH vs. ULH
5.32 × 10−5
ULH vs. MI
1.37 × 10−4
Three DMRs are particularly notable for their significance and the involvement of a large number of probes. These are a 829-bp hypomethylated region (spanning 29 probes) located at the developmentally important HOXA5 locus, observed in the ALH, ULH and LH groups when compared with the MI group (ALH vs. MI p = 3.65 × 10−62; ULH vs. MI p = 5.85 × 10−61; LH vs. MI p = 1.62 × 10−77); a 847-bp hypermethylated region (spanning either 22 (LH) or 24 (ALH) probes) located towards the 5′ end of RNF39, observed in the ALH and LH groups when compared with the MI group (ALH vs. MI p = 3.45 × 10−32; LH vs. MI p = 2.11 × 10−29) and a 1461-bp hypomethylated region (spanning 22 probes) encompassing the promoter regions and the first exons of AGPAT1 and RNF5, observed in the ALH group compared to the ULH group (p = 1.19 × 10−20).
Gene ontology analysis
In order to ascertain whether genes involved in particular biological processes, molecular functions or cellular components were overrepresented amongst the most differentially methylated loci; gene ontology (GO) analysis was performed for each of the four comparisons, ALH vs. MI, ULH vs. MI, ALH vs. ULH and LH vs. MI. This resulted in the identification of several GO categories showing FDR-significant enrichment (q ≤ 0.1; Additional file 1: Table S11–S21). Considering those GO categories that are classified as pertaining to “biological processes”, a theme common to all four comparisons was development. Within this broad theme, GO categories relating to the development of the nervous system (e.g. “regulation of nervous system development” (ALH vs. MI q = 1.99 × 10−4; LH vs. MI q = 5.36 × 10−2), “neuron projection guidance” (ULH vs. MI q = 1.17 × 10−2; ALH vs. ULH q = 1.40 × 10−3) and “nervous system development” (ALH vs. ULH q = 3.30 × 10−4)) ranked highly for all four comparisons. Continuing the theme of enrichment for neurologically relevant GO categories, both the ALH vs. MI and LH vs. MI comparisons revealed a significant enrichment for genes belonging to the “molecular function” GO category “voltage-gated ion channel activity” (ALH vs. MI q = 1.19 × 10−2; LH vs. MIC q = 6.00 × 10−2). Of those GO categories considered to relate to “cellular components”, the LH vs. MI comparison yielded several results relating to neurons and synapses, with the category “neuron part” attaining a q value of 7.72 × 10−2. Interestingly, in light of the important role believed to be played by calcium signalling in psychiatric illness , a significant enrichment for “calcium ion binding” was found when comparing the ALH and ULH groups (q = 1.23 × 10−4).
Several members of a large Scottish family carry a 20 Mb haplotype on chromosome 4p that confers risk for BD and MDD [17, 18]. Here, we sought to further our understanding of the mechanisms mediating the adverse effects of the LH by studying DNA methylation, an epigenetic mark found to be altered in individuals with psychiatric disorders (e.g. [14, 32–34]). Moreover, as some carriers of the linked haplotype remain well, we aimed to identify changes in DNA methylation that might reflect the involvement of additional risk/protective loci. To the best of our knowledge, this represents the first family-based genome-wide study of DNA methylation in the major affective disorders, BD and MDD. By studying related individuals who share a known genetic susceptibility locus, our study took advantage of the increase in statistical power conferred by the reduced genetic and aetiological heterogeneity expected in a single family compared with larger case-control studies.
Analysis of carriers of the LH resulted in the identification of four significantly differentially methylated loci, when compared with a group of MI. These loci map to an intron in FANCI, the GCOM1 and MYZAP promoter region, an intron in AHRR, and 147 bp 3′ of NBEAL2. The methylation differences at two of these loci, FANCI and NBEAL2, were also significant when considering only those carriers of the LH who had been diagnosed with either MDD or BD. Although none of these loci showed significant differential methylation when comparing the ALH and ULH groups, it is interesting to note that the mean level of methylation in the ULH group falls between those of the ALH and MI groups for all four loci. This might suggest that possession of the linked haplotype results in a change in methylation that is further modified by additional susceptibility/protective factors.
Increased whole blood FANCI methylation was accompanied by increased expression in LCLs from affected carriers of the linked haplotype, further supporting the notion that altered FANCI function might contribute to the pathogenic effects of the LH. It is important to note that we were unable to measure methylation and expression in the same tissue. This is a limitation of the present study that precludes us from drawing any definitive conclusions regarding the relationship between altered methylation and expression. Future studies should aim to characterise the relationship between FANCI expression and methylation in this family.
FANCI is a member of the Fanconi anaemia complementation (FANC) gene family. Mutations in these genes can cause Fanconi anaemia, a rare genome instability syndrome. Members of the FANC family act together in the Fanconi anaemia pathway to repair DNA damage. Phosphorylation of FANCI has been shown to act as a trigger for this process , and siRNA-induced depletion of FANCI has been shown to induce a higher baseline rate of DNA damage and reduced capacity to mend double strand breaks .
Genetic variation in members of the Fanconi anaemia pathway has been implicated in neurodevelopmental phenotypes and psychiatric illness. FANCD2/FANCI-associated nuclease 1 (FAN1) is a repair nuclease that is recruited to sites of interstrand crosslinks by interacting with a FANCD2-FANCI complex. The FAN1 gene is located at 15q13.3, a region affected by multiple microdeletions that predispose to a number of clinical phenotypes, including schizophrenia [37, 38], autism spectrum disorder (ASD), attention deficit hyperactivity disorder, epilepsy and intellectual disability . An exome sequencing study identified a cluster of rare non-synonymous variants located within a 20-kb window that spans several functional domains of FAN1, which were associated with schizophrenia with depressive features, schizoaffective disorder and ASD . More recently, a large-scale GWAS found a genome-wide significant association between the FANCL locus and schizophrenia .
GCOM1 forms part of the GRINL1A complex transcription unit, which comprises three groups of transcripts . GCOM1, which is a read-through transcript of MYZAP and POLR2M, shows similarity in amino acid sequence to the NR1 n-methyl-d-aspartate (NMDA) subunit-interactor Yotiao  and the amino termini of the NR2 and NR3 NMDA subunits . Evidence for an interaction between GCOM1 and NR1 has been identified in the rat brain, where GCOM1 facilitates NMDA receptor activity . The known roles of NMDA receptors in neurodevelopment, neuroplasticity and excitotoxicity , together with the evidence implicating altered NMDA function in psychiatric illness [45, 46] render GCOM1 a strong functional candidate. Interestingly, a three nucleotide deletion within GRINL1A has been detected in an exome sequencing study of sporadic ASD .
We detect decreased methylation at a site that falls within a DNase hypersensitive site (DHS) in the GCOM1/MYZAP promoter region . DHSs are indicative of an open chromatin structure, which makes DNA accessible to binding by transcription factors. Characterisation of the genomic locations of GWAS-significant variants has revealed that 76.6 % of associated non-coding SNPs are located within DHSs or are in complete linkage disequilibrium with a SNP located in a DHS . This suggests that the observed change in methylation might confer an effect on GCOM1 and MYZAP expression; however, further work is required to assess this possibility and to assess any effects on NMDA receptor activity.
Consideration of our data at a regional level revealed a hypermethylated region located within an intronic region of multiple CACNB2 isoforms and overlapping the promoter region of a single CACNB2 isoform in the LH group. Variation at the CACNB2 locus has been found to increase risk for schizophrenia  and for the five psychiatric disorders, schizophrenia, BD, MDD, ASD and attention deficit hyperactivity disorder, included in a recent cross-disorder GWAS . The hypermethylated region overlaps with a DHS and several chromatin immunoprecipitation (ChIP)-identified transcription factor binding sites . Taken together, it is possible that the observed increase in methylation might exert an effect on the expression of the short CACNB2 isoform; however, this would need to be assessed experimentally.
Comparison of the affected and unaffected carriers of the linked haplotype identified a region of hypomethylation upstream of HTR2A. This gene encodes the 5-HT2A receptor, a target of both atypical antipsychotics  and selective serotonin reuptake inhibitors . Moreover, variation in HTR2A has been associated with risk for MDD by candidate gene studies (reviewed in ) and a meta-analysis . Another DMR of interest falls within an intronic region of the mitotic spindle-assembly checkpoint gene MAD1L1, adjacent to a region previously associated with risk for schizophrenia and BD [28, 54]. MAD1L1 has been shown to be a target of another schizophrenia-susceptibility gene, miR-137 . A DMR identified in the comparison of these two groups worth noting for its potential functional relevance affects NPAS4. We observed reduced methylation in the ALH group in a region spanning the final exon of NPAS4. NPAS4 encodes a brain-specific transcription factor, which is involved in regulating the formation of inhibitory synapses . Stress, a well-established risk factor for psychiatric illness , has been shown to modulate NPAS4 methylation in mice .
In order to investigate the biological systems potentially impacted by the observed methylation changes, GO analysis was performed. It is interesting to note the presence of enrichment for GO categories pertaining to neurodevelopment and neuronal function in all four comparisons. This finding is particularly pertinent in light of the fact that it was necessary to assess DNA methylation differences relevant to the pathogenesis of BD and MDD in a non-neuronal tissue. Our findings indicate that it may be possible to detect methylation differences relevant to nervous system function in the blood, despite the existence of between-tissue differences in DNA methylation [59, 60].
When performing GO analysis of methylation array data, a single p value must be selected to represent each gene. We decided to select the most significant p value for any locus within a gene as a methylation change at an individual locus has the potential to confer an effect on gene function. As such, genes that contain more CpG sites are more likely to have their function altered by a change in methylation and are, therefore, more likely to obtain a more significant p value. A confounding factor, however, is that genes that are targeted by more probes also have a greater likelihood of obtaining a more significant p value by chance due to multiple testing. The conflation of these two factors presents an analytical challenge as correcting for multiple testing risks reducing true biological signal. This is a limitation of currently available methods for GO analysis of methylation data, which must be considered when interpreting our findings.
As each blood cell type has a distinct methylation profile, it was important to assess the existence of systematic between-group variation in cell type distribution, which could confound the detection of methylation differences [61, 62]. We did not observe any between-group differences in estimated cell type proportions, suggesting that variation in cell type distribution would be unlikely to exert a large effect on the differences observed in our sample. Moreover, by performing surrogate variables analysis (SVA), we were able to fit a set of SVs in our analyses that controlled for all unmodelled sources of variation, including cell type distribution . Together, the set of SVs produced by SVA efficiently represent the effects of all unmeasured or unmodelled confounding variables on DNA methylation whilst protecting the primary variable of interest. As such, SVA minimises the number of covariates that must be included, thus helping to avoid model overfitting problems. With regards to modelling the effects of cell type distribution, SVA confers the advantage of negating the need to decide which cell types to include.
Another factor that should be considered when interpreting our findings is that affected carriers of the linked haplotype were ill and taking medication prior to their blood sample being obtained for methylation analysis. As drug treatments for MDD and/or BD have previously been shown to alter DNA methylation [63–67], it is possible that DNA methylation in this group was affected by medication. To the best of our knowledge, however, methylation at the sites found to be differentially expressed in the current study has not been shown to be affected by relevant drug treatments [63, 66, 67]. Moreover, the fact that the mean methylation level in the ULH group falls between the means of the MI and ALH groups for the four significant LH vs. MI loci argues against medication being a primary driver of our results.
The small size of the sample studied here is likely to represent a limitation of the study. Although our approach of studying a large family benefits from reduced aetiological heterogeneity, it is likely that some changes remain undetectable due to insufficient power. In an attempt to maximise our chances of detecting illness-relevant changes in methylation, we used a significance threshold of FDR-adjusted p ≤ 0.1. It is, of course, possible, however, that some of our results are false positives. Future studies should expand upon the work presented here, perhaps by leveraging the increased power conferred by the study of a relevant quantitative phenotype. Ultimately, the synthesis of results obtained through family studies with the results of larger case-control studies, together with downstream functional analyses are likely to be important steps in unravelling the epigenetic contribution to psychiatric disorders.
We have found evidence for altered blood DNA methylation in carriers of a haplotype linked to BD and MDD. Many affected genes have neuronal roles, participating in neurodevelopment and ion channel activity. Our observation that significant single locus methylation differences are found in haplotype carriers regardless of affectation status is consistent with our previous finding of reduced cognitive function in haplotype carriers but no difference in cognitive function between affected and unaffected carriers of the haplotype . Nevertheless, through gene ontology analysis, we did find evidence that differences in DNA methylation between affected and unaffected haplotype carriers converge on neurologically relevant functions. Taken together, our findings suggest a scenario whereby the presence of the linked haplotype confers multiple effects on DNA methylation, which are in turn modified by additional genetic and/or environmental influences that fine-tune an individual’s risk of developing a mood disorder.
This study was approved by the Multicentre Research Ethics Committee for Scotland, and appropriate informed consent was obtained from the human subjects.
The individuals included in this study were selected from a previously described large Scottish family multiply affected by BD or MDD [17, 18]. A ~20 Mb haplotype located on chromosome 4p has previously been found to segregate with illness in this family with a maximum LOD score of 4.41 . Carriers of the haplotype were grouped according to illness-status, resulting in two groups: affected (either BD or MDD) carrier of the disease-linked haplotype (ALH) and unaffected carrier of the disease-linked haplotype (ULH). An additional group of well married-in controls (MI) who do not carry the disease-linked haplotype was included for comparison. In total, ten ALH, ten ULH and nine MI individuals were assessed.
Extraction of blood DNA
Blood (9 ml) was collected in an EDTA tube. DNA was extracted at the Wellcome Trust Clinical Research Facility at the University of Edinburgh, using the Nucleon BACC2 Genomic DNA Extraction Kit (Thermo Fisher Scientific, Loughborough, UK), following the manufacturer’s instructions.
Genome-wide methylation profiling
Whole blood genomic DNA (500 ng) was treated with sodium bisulphite using the EZ-96 DNA Methylation Kit (Zymo Research, Irvine, California), following the manufacturer’s instructions. DNA methylation was assessed using the Infinium HumanMethylation450 BeadChip (Illumina Inc, San Diego, California), according to the manufacturer’s protocol. Samples were assigned to chips such that, as far as possible, group (ALH, ULH or MI) and gender were counter-balanced across chips.
The resultant raw intensity (.idat) files were read into R using the minfi package , which was used to perform initial quality control assessments. Subsequently, filtering of poor-performing samples and sites was performed. Samples were removed from the dataset if: (i) they failed any of the quality control assessments carried out in minfi or (ii) ≥1 % sites had a detection p value of >0.05. Probes were removed from the dataset if: (i) they were located within two base pairs of SNP with a minor allele frequency of ≥5 %; (ii) they were predicted to cross-hybridise  and (iii) they had more than five samples with a bead count of less than three or (iv) ≥1 % samples had a detection p value of >0.05.
The data was normalised using the Daten 2 method, selected using the R package wateRmelon . Daten 2 involves adjusting the background difference between type I and type II assays (by adding the offset between type I and II probe intensities to type I intensities). A linear model is incorporated at this stage to eliminate positional effects. Between-array quantile normalisation is then performed for the methylated and unmethylated signal intensities separately (type I and type II assays normalised together).
Prior to downstream analyses, M-values, defined as M-value = log2((M + 100)/(U + 100)), where M represents the methylated signal intensity and U represents the unmethylated signal intensity, were calculated for the normalised data. For ease of interpretation, the data were converted to β-values (β-value = 2 M /(2 M + 1)) prior to presentation.
Assessment of between-group differences in the whole blood cellular composition
In order to assess between-group differences in the cellular composition of whole blood samples, estimated cell counts for B-lymphocytes, granulocytes, monocytes, natural killer cells, CD4+ T-lymphocytes and CD8+ T-lymphocytes were generated using the estimate CellCounts function in minfi. This function implements Jaffe and Irizarry’s  modified version of Houseman’s  algorithm. Between-group differences in cell composition were assessed using Student’s t tests. A p value of ≤0.05 was deemed to represent a significant between-group difference.
Identification of significant surrogate variables
DNA methylation can be affected by many sources of variation, and it is, therefore, important to account for these variables when assessing differential methylation. A complicating factor is that many sources of variation are unknown or unmeasured. Moreover, even when potential sources of variation are measured, it can be unclear how best to model these potential confounding variables . Surrogate variable analysis (SVA) identifies a set of significant SVs that together represent variation in DNA methylation that is not attributable to the primary variable of interest. These SVs are then fitted as covariates in the linear models implemented to identify DMPs. Covarying for the identified SVs controls for sources of unmeasured/unmodelled variation (e.g. age, genetic relatedness, smoking status, cell composition), which might otherwise confound the relationship between DNA methylation and the independent variable of interest [21, 70]. A key advantage of SVA is that it permits complex relationships between confounders and DNA methylation, for example, interactions between multiple confounding variables. SVA was carried out using the “be” method with the R package SVA .
Identification of differentially methylated positions
DMPs were then identified using the R package limma  by fitting linear models with the outcome variable “M-value” and the predictor variables “group” (ALH, ULH or MI) and “gender”, together with the significant SVs identified by SVA. The following comparisons were carried out as follows: (i) ALH and MI, (ii) ULH and MI, (iii) ALH and ULH and (iv) a combined linked haplotype carrier group (ALH and ULH; henceforth referred to as LH) and MI. Correction for multiple testing was implemented using the Benjamini-Hochberg false discovery rate (FDR), with adjusted p values of ≤0.1 deemed to be significant.
Identification of differentially methylated regions
Differentially methylated regions (DMRs) were identified using a modified version of the champ.lasso function implemented in the R package ChAMP . DMRs were defined as regions containing three or more adjacent probes within a defined lasso region showing unidirectional changes in methylation that attained nominal significance (unadjusted p ≤ 0.05) in the DMP analysis. The lasso region was set to 2 kb and was scaled according to the local genomic/epigenomic landscape in order to account for uneven probe spacing across the genome . DMRs were merged with neighbouring DMRs where they were separated by less than 1 kb, using the “minDmrSep” parameter in the champ.lasso function. P values were estimated for each DMR as described by Butcher et al. . Briefly, Stouffer’s method was used to combine individual probe p values, which were weighted by the underlying correlation structure of the M- values. P values from correlated probes were down-weighted whilst p values from uncorrelated probes were up-weighted. DMRs with p values meeting a Benjamini-Hochberg FDR-corrected threshold of ≤0.05 were included in the final DMR lists.
Comparison of DMRs with GWAS results
DMRs were assessed for overlap with regions previously implicated in BD, MDD and the related condition, schizophrenia, by GWAS. A literature search was carried out using PubMed (on 7 October 2015) to identify case-control GWAS studies and GWAS meta-analyses involving individuals with either BD, MDD or schizophrenia. Only GWAS results attaining genome-wide significance (p ≤ 5 × 10−8) were considered. Significantly associated genes/regions from the GWAS studies were defined as per the original study. Overlap was defined as either complete or partial overlap between the DMR and a GWAS associated gene/region.
Lymphoblastoid cell line culture and extraction of RNA
Human Epstein-Barr virus (EBV)-transformed LCLs were derived from blood lymphocytes obtained from family members at the European Collection of Cell Cultures (http://www.phe-culturecollections.org.uk/collections/ecacc.aspx). LCLs were maintained in Roswell Park Memorial Institute medium with 10 % foetal bovine serum at 37 °C and 5 % CO2.
Total RNA was extracted using the RNeasy Mini Kit (Qiagen, Manchester, UK), according to the manufacturer’s instructions. An on-column DNase digest step was performed using the RNase-Free DNase Set (Qiagen, Manchester, UK), according to the manufacturer’s instructions. RNA quantity and quality were assessed using the Agilent Bioanalyzer at the WTCRF. The Agilent Bioanalyzer reports RNA integrity numbers (RINs), which indicate how intact an RNA sample is (1 = completely degraded, 10 = completely intact). Sample RINs ranged from 8.6 to 10, indicating that the RNA was suitable for use in qRT-PCR .
qRT-PCR assessment of Fanconi anaemia, complementation group I (FANCI) expression
RNA (1 μg) was reverse transcribed using the Transcriptor First Strand cDNA Synthesis Kit (Roche) using random hexamer primers, according to the manufacturer’s instructions.
cDNA samples were diluted by a factor of 1/100. FANCI expression was assessed using a TaqMan Gene Expression Assay (Hs01105308_m1, Applied Biosystems by Thermo Fisher, Loughborough, UK). Briefly, 4.5 μl cDNA was added to 5 μl TaqMan Universal PCR Master Mix, No AmpErase UNG (2×) (Applied Biosystems) and 0.5 μl 20× TaqMan Gene Expression Assay (Applied Biosystems by Thermo Fisher, Loughborough, UK) in a 384-well plate. qPCRs were performed on a 7900HT PCR system with the following assay conditions: 15 s at 95 °C, followed by 1 min at 60 °C for 40 cycles. FANCI expression was normalised to the geometric mean of the expression levels of three reference genes, ATP5B, RPLP0 and UBC, which were selected from an initial set of seven using geNorm . Assay details are described in Additional file 1: Table S22. Each sample was measured in technical triplicates and the mean of this triplicate used in downstream analyses. Outlier samples, defined as data points falling outside of the range defined by median ± 1.5 × inter-quartile range were excluded.
Differences in FANCI expression were assessed by linear regression, covarying for gender. A p value of ≤0.05 was deemed to be significant.
Gene ontology analysis
Gene symbols representing all genes targeted by reliably detected probes (n = 17,686 genes) were ranked according to the probe p value calculated when identifying DMPs (where an individual gene was targeted by multiple probes, the best p value was retained for GO analysis) and ranked-list GO analysis performed using GOrilla [76, 77]. GOrilla performs a hypergeometric test to assign a p value to each GO category and then calculates the Benjamini-Hochberg FDR to reflect the number of GO categories assessed. GO categories with a q value ≤0.1 were considered statistically significant.
affected carrier of the linked haplotype
autism spectrum disorder
DNase hypersensitive site
differentially methylated position
differentially methylated region
false discovery rate
genome-wide association study
lymphoblastoid cell line
all carriers of the linked haplotype
major depressive disorder
married in control
quantitative polymerase chain reaction
RNA integrity number
surrogate variables analysis
unaffected carrier of the linked haplotype
KLE acknowledges the support of the Brain & Behavior Research Foundation through a NARSAD Independent Investigator Award. AMM acknowledges the support of the Health Foundation through a Clinician Scientist Fellowship, a NARSAD Independent Investigator Award from the Brain & Behavior Research Foundation and Wellcome Trust Strategic Support (104036/Z/14/Z). AMM and KLE would also like to acknowledge the support of funding from the NC3Rs, which has permitted re-contact of the family. This study was funded, in part, by an NIH R01 MH102068-1 award (to DJP, KLE and AMM) and a Mental Health Research UK studentship to DLMcC. The research leading to these results has received funding from the European Community’s Seventh Framework Programme (FP7/2007-2013) under Grant No. 602450 through the Imaging Genetics for Mental Disorders (IMAGEMEND). JES was supported by a Clinical Research Fellowship from the Wellcome Trust (087727/Z/08/Z). HCW would like to acknowledge the support of a Royal Society Dorothy Hodgkin Fellowship (DH080018) and a JMAS SIM Fellowship from the Royal College of Physicians of Edinburgh. NAK was funded by a Wellcome Trust Research Training Fellowship (Grant No. 097943).
We are grateful to the family for their participation in this study. DNA extraction, bisulphite conversion and the processing of the Infinium HumanMethylation450 BeadChips were performed at the Wellcome Trust Clinical Research Facility, Edinburgh.
Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
- McGuffin P, Rijsdijk F, Andrew M, Sham P, Katz R, Cardno A. The heritability of bipolar affective disorder and the genetic relationship to unipolar depression. Arch Gen Psychiatry. 2003;60(5):497–502. doi:10.1001/archpsyc.60.5.497.View ArticlePubMedGoogle Scholar
- Shinozaki G, Potash JB. New developments in the genetics of bipolar disorder. Curr Psychiatry Rep. 2014;16(11):493. doi:10.1007/s11920-014-0493-5.View ArticlePubMedGoogle Scholar
- Muhleisen TW, Leber M, Schulze TG, Strohmaier J, Degenhardt F, Treutlein J, et al. Genome-wide association study reveals two new risk loci for bipolar disorder. Nat Commun. 2014;5:3339. doi:10.1038/ncomms4339.View ArticlePubMedGoogle Scholar
- Visscher PM, Goddard ME, Derks EM, Wray NR. Evidence-based psychiatric genetics, AKA the false dichotomy between common and rare variant hypotheses. Mol Psychiatry. 2012;17(5):474–85. doi:10.1038/mp.2011.65.View ArticlePubMedGoogle Scholar
- Maurano MT, Humbert R, Rynes E, Thurman RE, Haugen E, Wang H, et al. Systematic localization of common disease-associated variation in regulatory DNA. Science. 2012;337(6099):1190–5. doi:10.1126/science.1222794.PubMed CentralView ArticlePubMedGoogle Scholar
- Seifuddin F, Pirooznia M, Judy JT, Goes FS, Potash JB, Zandi PP. Systematic review of genome-wide gene expression studies of bipolar disorder. BMC Psychiatry. 2013;13:213. doi:10.1186/1471-244X-13-213.PubMed CentralView ArticlePubMedGoogle Scholar
- Beech RD, Lowthert L, Leffert JJ, Mason PN, Taylor MM, Umlauf S, et al. Increased peripheral blood expression of electron transport chain genes in bipolar depression. Bipolar Disord. 2010;12(8):813–24. doi:10.1111/j.1399-5618.2010.00882.x.PubMed CentralView ArticlePubMedGoogle Scholar
- Kaminsky Z, Jones I, Verma R, Saleh L, Trivedi H, Guintivano J, et al. DNA methylation and expression of KCNQ3 in bipolar disorder. Bipolar Disord. 2014. doi:10.1111/bdi.12230.PubMedGoogle Scholar
- Strauss JS, Khare T, De Luca V, Jeremian R, Kennedy JL, Vincent JB, et al. Quantitative leukocyte BDNF promoter methylation analysis in bipolar disorder. Int J Bipolar Disord. 2013;1:28. doi:10.1186/2194-7511-1-28.PubMed CentralView ArticlePubMedGoogle Scholar
- Ruzicka WB, Subburaju S, Benes FM. Circuit- and diagnosis-specific DNA methylation changes at gamma-aminobutyric acid-related genes in postmortem human hippocampus in schizophrenia and bipolar disorder. JAMA psychiatry. 2015;72(6):541–51. doi:10.1001/jamapsychiatry.2015.49.View ArticlePubMedGoogle Scholar
- Rao JS, Keleshian VL, Klein S, Rapoport SI. Epigenetic modifications in frontal cortex from Alzheimer’s disease and bipolar disorder patients. Translational psychiatry. 2012;2:e132. doi:10.1038/tp.2012.55.PubMed CentralView ArticlePubMedGoogle Scholar
- Kaminsky Z, Tochigi M, Jia P, Pal M, Mill J, Kwan A, et al. A multi-tissue analysis identifies HLA complex group 9 gene methylation differences in bipolar disorder. Mol Psychiatry. 2012;17(7):728–40. doi:10.1038/mp.2011.64.View ArticlePubMedGoogle Scholar
- Lemire M, Zaidi SH, Ban M, Ge B, Aissi D, Germain M, et al. Long-range epigenetic regulation is conferred by genetic variation located at thousands of independent loci. Nat Commun. 2015;6:6326. doi:10.1038/ncomms7326.PubMed CentralView ArticlePubMedGoogle Scholar
- Dempster EL, Pidsley R, Schalkwyk LC, Owens S, Georgiades A, Kane F, et al. Disease-associated epigenetic changes in monozygotic twins discordant for schizophrenia and bipolar disorder. Hum Mol Genet. 2011;20(24):4786–96. doi:10.1093/hmg/ddr416.PubMed CentralView ArticlePubMedGoogle Scholar
- Li Y, Camarillo C, Xu J, Arana TB, Xiao Y, Zhao Z, et al. Genome-wide methylome analyses reveal novel epigenetic regulation patterns in schizophrenia and bipolar disorder. BioMed research international. 2015;2015:201587. doi:10.1155/2015/201587.PubMed CentralPubMedGoogle Scholar
- Xiao Y, Camarillo C, Ping Y, Arana TB, Zhao H, Thompson PM, et al. The DNA methylome and transcriptome of different brain regions in schizophrenia and bipolar disorder. PLoS One. 2014;9(4):e95875. doi:10.1371/journal.pone.0095875.PubMed CentralView ArticlePubMedGoogle Scholar
- Blackwood DH, He L, Morris SW, McLean A, Whitton C, Thomson M, et al. A locus for bipolar affective disorder on chromosome 4p. Nat Genet. 1996;12(4):427–30. doi:10.1038/ng0496-427.View ArticlePubMedGoogle Scholar
- Le Hellard S, Lee AJ, Underwood S, Thomson PA, Morris SW, Torrance HS, et al. Haplotype analysis and a novel allele-sharing method refines a chromosome 4p locus linked to bipolar affective disorder. Biol Psychiatry. 2007;61(6):797–805. doi:10.1016/j.biopsych.2006.06.029.View ArticlePubMedGoogle Scholar
- Chen YA, Lemire M, Choufani S, Butcher DT, Grafodatskaya D, Zanke BW, et al. Discovery of cross-reactive probes and polymorphic CpGs in the Illumina Infinium HumanMethylation450 microarray. Epigenetics. 2013;8(2):203–9. doi:10.4161/epi.23470.PubMed CentralView ArticlePubMedGoogle Scholar
- Pidsley R, Y Wong CC, Volta M, Lunnon K, Mill J, Schalkwyk LC. A data-driven approach to preprocessing Illumina 450K methylation array data. BMC Genomics. 2013;14:293. doi:10.1186/1471-2164-14-293.PubMed CentralView ArticlePubMedGoogle Scholar
- Leek JT, Storey JD. Capturing heterogeneity in gene expression studies by surrogate variable analysis. PLoS Genet. 2007;3(9):1724–35. doi:10.1371/journal.pgen.0030161.View ArticlePubMedGoogle Scholar
- Ong ML, Holbrook JD. Novel region discovery method for Infinium 450K DNA methylation data reveals changes associated with aging in muscle and neuronal pathways. Aging Cell. 2014;13(1):142–55. doi:10.1111/acel.12159.PubMed CentralView ArticlePubMedGoogle Scholar
- Baum AE, Akula N, Cabanero M, Cardona I, Corona W, Klemens B, et al. A genome-wide association study implicates diacylglycerol kinase eta (DGKH) and several other genes in the etiology of bipolar disorder. Mol Psychiatry. 2008;13(2):197–207. doi:10.1038/sj.mp.4002012.PubMed CentralView ArticlePubMedGoogle Scholar
- Cichon S, Muhleisen TW, Degenhardt FA, Mattheisen M, Miro X, Strohmaier J, et al. Genome-wide association study identifies genetic variation in neurocan as a susceptibility factor for bipolar disorder. Am J Hum Genet. 2011;88(3):372–81. doi:10.1016/j.ajhg.2011.01.017.PubMed CentralView ArticlePubMedGoogle Scholar
- Ferreira MA, O’Donovan MC, Meng YA, Jones IR, Ruderfer DM, Jones L, et al. Collaborative genome-wide association analysis supports a role for ANK3 and CACNA1C in bipolar disorder. Nat Genet. 2008;40(9):1056–8. doi:10.1038/ng.209.PubMed CentralView ArticlePubMedGoogle Scholar
- Psychiatric GCBDWG. Large-scale genome-wide association analysis of bipolar disorder identifies a new susceptibility locus near ODZ4. Nat Genet. 2011;43(10):977–83. doi:10.1038/ng.943.View ArticleGoogle Scholar
- Consortium C. Sparse whole-genome sequencing identifies two loci for major depressive disorder. Nature. 2015;523(7562):588–91. doi:10.1038/nature14659.View ArticleGoogle Scholar
- Consortium SWGotPG. Biological insights from 108 schizophrenia-associated genetic loci. Nature. 2014;511(7510):421–7. doi:10.1038/nature13595.View ArticleGoogle Scholar
- Ripke S, O’Dushlaine C, Chambert K, Moran JL, Kahler AK, Akterin S, et al. Genome-wide association analysis identifies 13 new risk loci for schizophrenia. Nat Genet. 2013;45(10):1150–9. doi:10.1038/ng.2742.View ArticlePubMedGoogle Scholar
- Shi Y, Li Z, Xu Q, Wang T, Li T, Shen J, et al. Common variants on 8p12 and 1q24.2 confer risk of schizophrenia. Nat Genet. 2011;43(12):1224–7. doi:10.1038/ng.980.PubMed CentralView ArticlePubMedGoogle Scholar
- Berridge MJ. Calcium signalling and psychiatric disease: bipolar disorder and schizophrenia. Cell Tissue Res. 2014;357(2):477–92. doi:10.1007/s00441-014-1806-z.View ArticlePubMedGoogle Scholar
- Dempster EL, Wong CC, Lester KJ, Burrage J, Gregory AM, Mill J, et al. Genome-wide methylomic analysis of monozygotic twins discordant for adolescent depression. Biol Psychiatry. 2014;76(12):977–83. doi:10.1016/j.biopsych.2014.04.013.PubMed CentralView ArticlePubMedGoogle Scholar
- Mill J, Tang T, Kaminsky Z, Khare T, Yazdanpanah S, Bouchard L, et al. Epigenomic profiling reveals DNA-methylation changes associated with major psychosis. Am J Hum Genet. 2008;82(3):696–711. doi:10.1016/j.ajhg.2008.01.008.PubMed CentralView ArticlePubMedGoogle Scholar
- Cordova-Palomera A, Fatjo-Vilas M, Gasto C, Navarro V, Krebs MO, Fananas L. Genome-wide methylation study on depression: differential methylation and variable methylation in monozygotic twins. Translational psychiatry. 2015;5:e557. doi:10.1038/tp.2015.49.PubMed CentralView ArticlePubMedGoogle Scholar
- Ishiai M, Kitao H, Smogorzewska A, Tomida J, Kinomura A, Uchida E, et al. FANCI phosphorylation functions as a molecular switch to turn on the Fanconi anemia pathway. Nat Struct Mol Biol. 2008;15(11):1138–46. doi:10.1038/nsmb.1504.PubMed CentralView ArticlePubMedGoogle Scholar
- Smogorzewska A, Matsuoka S, Vinciguerra P, McDonald 3rd ER, Hurov KE, Luo J, et al. Identification of the FANCI protein, a monoubiquitinated FANCD2 paralog required for DNA repair. Cell. 2007;129(2):289–301. doi:10.1016/j.cell.2007.03.009.PubMed CentralView ArticlePubMedGoogle Scholar
- International Schizophrenia C. Rare chromosomal deletions and duplications increase risk of schizophrenia. Nature. 2008;455(7210):237–41. doi:10.1038/nature07239.View ArticleGoogle Scholar
- Stefansson H, Rujescu D, Cichon S, Pietilainen OP, Ingason A, Steinberg S, et al. Large recurrent microdeletions associated with schizophrenia. Nature. 2008;455(7210):232–6. doi:10.1038/nature07229.PubMed CentralView ArticlePubMedGoogle Scholar
- Malhotra D, Sebat J. CNVs: harbingers of a rare variant revolution in psychiatric genetics. Cell. 2012;148(6):1223–41. doi:10.1016/j.cell.2012.02.039.PubMed CentralView ArticlePubMedGoogle Scholar
- Ionita-Laza I, Xu B, Makarov V, Buxbaum JD, Roos JL, Gogos JA, et al. Scan statistic-based analysis of exome sequencing data identifies FAN1 at 15q13.3 as a susceptibility gene for schizophrenia and autism. Proc Natl Acad Sci U S A. 2014;111(1):343–8. doi:10.1073/pnas.1309475110.PubMed CentralView ArticlePubMedGoogle Scholar
- Roginski RS, Mohan Raj BK, Birditt B, Rowen L. The human GRINL1A gene defines a complex transcription unit, an unusual form of gene organization in eukaryotes. Genomics. 2004;84(2):265–76. doi:10.1016/j.ygeno.2004.04.004.View ArticlePubMedGoogle Scholar
- Lin JW, Wyszynski M, Madhavan R, Sealock R, Kim JU, Sheng M. Yotiao, a novel protein of neuromuscular junction and brain that interacts with specific splice variants of NMDA receptor subunit NR1. J Neurosci. 1998;18(6):2017–27.PubMedGoogle Scholar
- Roginski RS, Goubaeva F, Mikami M, Fried-Cassorla E, Nair MR, Yang J. GRINL1A colocalizes with n-methyl d-aspartate receptor NR1 subunit and reduces n-methyl d-aspartate toxicity. Neuroreport. 2008;19(17):1721–6. doi:10.1097/WNR.0b013e328317f05f.View ArticlePubMedGoogle Scholar
- Vyklicky V, Korinek M, Smejkalova T, Balik A, Krausova B, Kaniakova M, et al. Structure, function, and pharmacology of NMDA receptor channels. Physiological research / Academia Scientiarum Bohemoslovaca. 2014;63 Suppl 1:S191–203.PubMedGoogle Scholar
- Harrison PJ. Recent genetic findings in schizophrenia and their therapeutic relevance. J Psychopharmacol. 2015;29(2):85–96. doi:10.1177/0269881114553647.PubMed CentralView ArticlePubMedGoogle Scholar
- Ghasemi M, Phillips C, Trillo L, De Miguel Z, Das D, Salehi A. The role of NMDA receptors in the pathophysiology and treatment of mood disorders. Neurosci Biobehav Rev. 2014;47:336–58. doi:10.1016/j.neubiorev.2014.08.017.View ArticlePubMedGoogle Scholar
- O’Roak BJ, Vives L, Girirajan S, Karakoc E, Krumm N, Coe BP, et al. Sporadic autism exomes reveal a highly interconnected protein network of de novo mutations. Nature. 2012;485(7397):246–50. doi:10.1038/nature10989.PubMed CentralView ArticlePubMedGoogle Scholar
- Rosenbloom KR, Sloan CA, Malladi VS, Dreszer TR, Learned K, Kirkup VM, et al. ENCODE data in the UCSC Genome Browser: year 5 update. Nucleic Acids Res. 2013;41(Database issue):D56–63. doi:10.1093/nar/gks1172.PubMed CentralView ArticlePubMedGoogle Scholar
- Cross-Disorder Group of the Psychiatric Genomics C. Identification of risk loci with shared effects on five major psychiatric disorders: a genome-wide analysis. Lancet. 2013;381(9875):1371–9. doi:10.1016/S0140-6736(12)62129-1.View ArticleGoogle Scholar
- Amato D. Serotonin in antipsychotic drugs action. Behav Brain Res. 2015;277:125–35. doi:10.1016/j.bbr.2014.07.025.View ArticlePubMedGoogle Scholar
- Celada P, Puig M, Amargos-Bosch M, Adell A, Artigas F. The therapeutic role of 5-HT1A and 5-HT2A receptors in depression. J Psychiatry Neurosci. 2004;29(4):252–65.PubMed CentralPubMedGoogle Scholar
- Lacerda-Pinheiro SF, Pinheiro Junior RF, Pereira de Lima MA, Lima da Silva CG, Vieira dos Santos Mdo S, Teixeira Junior AG, et al. Are there depression and anxiety genetic markers and mutations? A systematic review. Journal of affective disorders. 2014;168:387–98. doi:10.1016/j.jad.2014.07.016.View ArticlePubMedGoogle Scholar
- Zhao X, Sun L, Sun YH, Ren C, Chen J, Wu ZQ, et al. Association of HTR2A T102C and A-1438G polymorphisms with susceptibility to major depressive disorder: a meta-analysis. Neurol Sci. 2014;35(12):1857–66. doi:10.1007/s10072-014-1970-7.View ArticlePubMedGoogle Scholar
- Ruderfer DM, Fanous AH, Ripke S, McQuillin A, Amdur RL, Schizophrenia Working Group of Psychiatric Genomics, et al. Polygenic dissection of diagnosis and clinical dimensions of bipolar disorder and schizophrenia. Mol Psychiatry. 2014;19(9):1017–24. doi:10.1038/mp.2013.138.PubMed CentralView ArticlePubMedGoogle Scholar
- Hill MJ, Donocik JG, Nuamah RA, Mein CA, Sainz-Fuertes R, Bray NJ. Transcriptional consequences of schizophrenia candidate miR-137 manipulation in human neural progenitor cells. Schizophr Res. 2014;153(1-3):225–30. doi:10.1016/j.schres.2014.01.034.PubMed CentralView ArticlePubMedGoogle Scholar
- Lin Y, Bloodgood BL, Hauser JL, Lapan AD, Koon AC, Kim TK, et al. Activity-dependent regulation of inhibitory synapse development by Npas4. Nature. 2008;455(7217):1198–204. doi:10.1038/nature07319.PubMed CentralView ArticlePubMedGoogle Scholar
- de Kloet ER, Joels M, Holsboer F. Stress and the brain: from adaptation to disease. Nat Rev Neurosci. 2005;6(6):463–75. doi:10.1038/nrn1683.View ArticlePubMedGoogle Scholar
- Furukawa-Hibi Y, Nagai T, Yun J, Yamada K. Stress increases DNA methylation of the neuronal PAS domain 4 (Npas4) gene. Neuroreport. 2015;26(14):827–32. doi:10.1097/WNR.0000000000000430.PubMedGoogle Scholar
- Davies MN, Volta M, Pidsley R, Lunnon K, Dixit A, Lovestone S, et al. Functional annotation of the human brain methylome identifies tissue-specific epigenetic variation across brain and blood. Genome Biol. 2012;13(6):R43. doi:10.1186/gb-2012-13-6-r43.PubMed CentralView ArticlePubMedGoogle Scholar
- Lokk K, Modhukur V, Rajashekar B, Martens K, Magi R, Kolde R, et al. DNA methylome profiling of human tissues identifies global and tissue-specific methylation patterns. Genome Biol. 2014;15(4):r54. doi:10.1186/gb-2014-15-4-r54.PubMed CentralView ArticlePubMedGoogle Scholar
- Houseman EA, Accomando WP, Koestler DC, Christensen BC, Marsit CJ, Nelson HH, et al. DNA methylation arrays as surrogate measures of cell mixture distribution. BMC Bioinformatics. 2012;13:86. doi:10.1186/1471-2105-13-86.PubMed CentralView ArticlePubMedGoogle Scholar
- Jaffe AE, Irizarry RA. Accounting for cellular heterogeneity is critical in epigenome-wide association studies. Genome Biol. 2014;15(2):R31. doi:10.1186/gb-2014-15-2-r31.PubMed CentralView ArticlePubMedGoogle Scholar
- Asai T, Bundo M, Sugawara H, Sunaga F, Ueda J, Tanaka G, et al. Effect of mood stabilizers on DNA methylation in human neuroblastoma cells. The international journal of neuropsychopharmacology/official scientific journal of the Collegium Internationale Neuropsychopharmacologicum. 2013;16(10):2285–94. doi:10.1017/S1461145713000710.View ArticleGoogle Scholar
- Carlberg L, Scheibelreiter J, Hassler MR, Schloegelhofer M, Schmoeger M, Ludwig B, et al. Brain-derived neurotrophic factor (BDNF)-epigenetic regulation in unipolar and bipolar affective disorder. J Affect Disord. 2014;168:399–406. doi:10.1016/j.jad.2014.07.022.View ArticlePubMedGoogle Scholar
- Menke A, Binder EB. Epigenetic alterations in depression and antidepressant treatment. Dialogues Clin Neurosci. 2014;16(3):395–404.PubMed CentralPubMedGoogle Scholar
- Sugawara H, Bundo M, Asai T, Sunaga F, Ueda J, Ishigooka J, et al. Effects of quetiapine on DNA methylation in neuroblastoma cells. Prog Neuropsychopharmacol Biol Psychiatry. 2015;56:117–21. doi:10.1016/j.pnpbp.2014.08.010.View ArticlePubMedGoogle Scholar
- Lee RS, Pirooznia M, Guintivano J, Ly M, Ewald ER, Tamashiro KL, et al. Search for common targets of lithium and valproic acid identifies novel epigenetic effects of lithium on the rat leptin receptor gene. Translational psychiatry. 2015;5, e600. doi:10.1038/tp.2015.90.View ArticlePubMedGoogle Scholar
- Drysdale E, Knight HM, McIntosh AM, Blackwood DH. Cognitive endophenotypes in a family with bipolar disorder with a risk locus on chromosome 4. Bipolar Disord. 2013;15(2):215–22. doi:10.1111/bdi.12040.View ArticlePubMedGoogle Scholar
- Aryee MJ, Jaffe AE, Corrada-Bravo H, Ladd-Acosta C, Feinberg AP, Hansen KD, et al. Minfi: a flexible and comprehensive Bioconductor package for the analysis of Infinium DNA methylation microarrays. Bioinformatics. 2014;30(10):1363–9. doi:10.1093/bioinformatics/btu049.PubMed CentralView ArticlePubMedGoogle Scholar
- Gibson G. The environmental contribution to gene expression profiles. Nat Rev Genet. 2008;9(8):575–81. doi:10.1038/nrg2383.View ArticlePubMedGoogle Scholar
- Leek JT, Johnson WE, Parker HS, Jaffe AE, Storey JD. The SVA package for removing batch effects and other unwanted variation in high-throughput experiments. Bioinformatics. 2012;28(6):882–3. doi:10.1093/bioinformatics/bts034.PubMed CentralView ArticlePubMedGoogle Scholar
- Ritchie ME, Phipson B, Wu D, Hu Y, Law CW, Shi W, et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 2015;43(7):e47. doi:10.1093/nar/gkv007.PubMed CentralView ArticlePubMedGoogle Scholar
- Butcher LM, Beck S. Probe Lasso: a novel method to rope in differentially methylated regions with 450K DNA methylation data. Methods. 2014. doi:10.1016/j.ymeth.2014.10.036.PubMedGoogle Scholar
- Fleige S, Pfaffl MW. RNA integrity and the effect on the real-time qRT-PCR performance. Mol Aspects Med. 2006;27(2-3):126–39. doi:10.1016/j.mam.2005.12.003.View ArticlePubMedGoogle Scholar
- Vandesompele J, De Preter K, Pattyn F, Poppe B, Van Roy N, De Paepe A, et al. Accurate normalization of real-time quantitative RT-PCR data by geometric averaging of multiple internal control genes. Genome Biol. 2002;3(7):RESEARCH0034.PubMed CentralView ArticlePubMedGoogle Scholar
- Eden E, Lipson D, Yogev S, Yakhini Z. Discovering motifs in ranked lists of DNA sequences. PLoS Comput Biol. 2007;3(3):e39. doi:10.1371/journal.pcbi.0030039.PubMed CentralView ArticlePubMedGoogle Scholar
- Eden E, Navon R, Steinfeld I, Lipson D, Yakhini Z. GOrilla: a tool for discovery and visualization of enriched GO terms in ranked gene lists. BMC Bioinformatics. 2009;10:48. doi:10.1186/1471-2105-10-48.PubMed CentralView ArticlePubMedGoogle Scholar