Ruth Pidsley

Functional annotation of the human brain methylome identifies tissue-specific epigenetic variation across brain and blood

BackgroundDynamic changes to the epigenome play a critical role in establishing and maintaining cellular phenotype during differentiation, but little is known about the normal methylomic differences that occur between functionally distinct areas of the brain. We characterized intra- and inter-individual methylomic variation across whole blood and multiple regions of the brain from multiple donors.ResultsDistinct tissue-specific patterns of DNA methylation were identified, with a highly significant over-representation of tissue-specific differentially methylated regions (TS-DMRs) observed at intragenic CpG islands and low CG density promoters. A large proportion of TS-DMRs were located near genes that are differentially expressed across brain regions. TS-DMRs were significantly enriched near genes involved in functional pathways related to neurodevelopment and neuronal differentiation, including BDNF, BMP4, CACNA1A, CACA1AF, EOMES, NGFR, NUMBL, PCDH9, SLIT1, SLITRK1 and SHANK3. Although between-tissue variation in DNA methylation was found to greatly exceed between-individual differences within any one tissue, we found that some inter-individual variation was reflected across brain and blood, indicating that peripheral tissues may have some utility in epidemiological studies of complex neurobiological phenotypes.ConclusionsThis study reinforces the importance of DNA methylation in regulating cellular phenotype across tissues, and highlights genomic patterns of epigenetic variation across functionally distinct regions of the brain, providing a resource for the epigenetics and neuroscience research communities.

Epigenome-Wide Scans Identify Differentially Methylated Regions for Age and Age-Related Phenotypes in a Healthy Ageing Population

Age-related changes in DNA methylation have been implicated in cellular senescence and longevity, yet the causes and functional consequences of these variants remain unclear. To elucidate the role of age-related epigenetic changes in healthy ageing and potential longevity, we tested for association between whole-blood DNA methylation patterns in 172 female twins aged 32 to 80 with age and age-related phenotypes. Twin-based DNA methylation levels at 26,690 CpG-sites showed evidence for mean genome-wide heritability of 18%, which was supported by the identification of 1,537 CpG-sites with methylation QTLs in cis at FDR 5%. We performed genome-wide analyses to discover differentially methylated regions (DMRs) for sixteen age-related phenotypes (ap-DMRs) and chronological age (a-DMRs). Epigenome-wide association scans (EWAS) identified age-related phenotype DMRs (ap-DMRs) associated with LDL (STAT5A), lung function (WT1), and maternal longevity (ARL4A, TBX20). In contrast, EWAS for chronological age identified hundreds of predominantly hyper-methylated age DMRs (490 a-DMRs at FDR 5%), of which only one (TBX20) was also associated with an age-related phenotype. Therefore, the majority of age-related changes in DNA methylation are not associated with phenotypic measures of healthy ageing in later life. We replicated a large proportion of a-DMRs in a sample of 44 younger adult MZ twins aged 20 to 61, suggesting that a-DMRs may initiate at an earlier age. We next explored potential genetic and environmental mechanisms underlying a-DMRs and ap-DMRs. Genome-wide overlap across cis-meQTLs, genotype-phenotype associations, and EWAS ap-DMRs identified CpG-sites that had cis-meQTLs with evidence for genotype–phenotype association, where the CpG-site was also an ap-DMR for the same phenotype. Monozygotic twin methylation difference analyses identified one potential environmentally-mediated ap-DMR associated with total cholesterol and LDL (CSMD1). Our results suggest that in a small set of genes DNA methylation may be a candidate mechanism of mediating not only environmental, but also genetic effects on age-related phenotypes.

Disease-associated epigenetic changes in monozygotic twins discordant for schizophrenia and bipolar disorder

Studies of the major psychoses, schizophrenia (SZ) and bipolar disorder (BD), have traditionally focused on genetic and environmental risk factors, although more recent work has highlighted an additional role for epigenetic processes in mediating susceptibility. Since monozygotic (MZ) twins share a common DNA sequence, their study represents an ideal design for investigating the contribution of epigenetic factors to disease etiology. We performed a genome-wide analysis of DNA methylation on peripheral blood DNA samples obtained from a unique sample of MZ twin pairs discordant for major psychosis. Numerous loci demonstrated disease-associated DNA methylation differences between twins discordant for SZ and BD individually, and together as a combined major psychosis group. Pathway analysis of our top loci highlighted a significant enrichment of epigenetic changes in biological networks and pathways directly relevant to psychiatric disorder and neurodevelopment. The top psychosis-associated, differentially methylated region, significantly hypomethylated in affected twins, was located in the promoter of ST6GALNAC1 overlapping a previously reported rare genomic duplication observed in SZ. The mean DNA methylation difference at this locus was 6%, but there was considerable heterogeneity between families, with some twin pairs showing a 20% difference in methylation. We subsequently assessed this region in an independent sample of postmortem brain tissue from affected individuals and controls, finding marked hypomethylation (>25%) in a subset of psychosis patients. Overall, our data provide further evidence to support a role for DNA methylation differences in mediating phenotypic differences between MZ twins and in the etiology of both SZ and BD.

A data-driven approach to preprocessing Illumina 450K methylation array data

BackgroundAs the most stable and experimentally accessible epigenetic mark, DNA methylation is of great interest to the research community. The landscape of DNA methylation across tissues, through development and in disease pathogenesis is not yet well characterized. Thus there is a need for rapid and cost effective methods for assessing genome-wide levels of DNA methylation. The Illumina Infinium HumanMethylation450 (450K) BeadChip is a very useful addition to the available methods for DNA methylation analysis but its complex design, incorporating two different assay methods, requires careful consideration. Accordingly, several normalization schemes have been published. We have taken advantage of known DNA methylation patterns associated with genomic imprinting and X-chromosome inactivation (XCI), in addition to the performance of SNP genotyping assays present on the array, to derive three independent metrics which we use to test alternative schemes of correction and normalization. These metrics also have potential utility as quality scores for datasets.ResultsThe standard index of DNA methylation at any specific CpG site is β = M/(M + U + 100) where M and U are methylated and unmethylated signal intensities, respectively. Betas (βs) calculated from raw signal intensities (the default GenomeStudio behavior) perform well, but using 11 methylomic datasets we demonstrate that quantile normalization methods produce marked improvement, even in highly consistent data, by all three metrics. The commonly used procedure of normalizing betas is inferior to the separate normalization of M and U, and it is also advantageous to normalize Type I and Type II assays separately. More elaborate manipulation of quantiles proves to be counterproductive.ConclusionsCareful selection of preprocessing steps can minimize variance and thus improve statistical power, especially for the detection of the small absolute DNA methylation changes likely associated with complex disease phenotypes. For the convenience of the research community we have created a user-friendly R software package called wateRmelon, downloadable from bioConductor, compatible with the existing methylumi, minfi and IMA packages, that allows others to utilize the same normalization methods and data quality tests on 450K data.

Integrated Genetic and Epigenetic Analysis Identifies Haplotype-Specific Methylation in the FTO Type 2 Diabetes and Obesity Susceptibility Locus

Recent multi-dimensional approaches to the study of complex disease have revealed powerful insights into how genetic and epigenetic factors may underlie their aetiopathogenesis. We examined genotype-epigenotype interactions in the context of Type 2 Diabetes (T2D), focussing on known regions of genomic susceptibility. We assayed DNA methylation in 60 females, stratified according to disease susceptibility haplotype using previously identified association loci. CpG methylation was assessed using methylated DNA immunoprecipitation on a targeted array (MeDIP-chip) and absolute methylation values were estimated using a Bayesian algorithm (BATMAN). Absolute methylation levels were quantified across LD blocks, and we identified increased DNA methylation on the FTO obesity susceptibility haplotype, tagged by the rs8050136 risk allele A (p = 9.40×10−4, permutation p = 1.0×10−3). Further analysis across the 46 kb LD block using sliding windows localised the most significant difference to be within a 7.7 kb region (p = 1.13×10−7). Sequence level analysis, followed by pyrosequencing validation, revealed that the methylation difference was driven by the co-ordinated phase of CpG-creating SNPs across the risk haplotype. This 7.7 kb region of haplotype-specific methylation (HSM), encapsulates a Highly Conserved Non-Coding Element (HCNE) that has previously been validated as a long-range enhancer, supported by the histone H3K4me1 enhancer signature. This study demonstrates that integration of Genome-Wide Association (GWA) SNP and epigenomic DNA methylation data can identify potential novel genotype-epigenotype interactions within disease-associated loci, thus providing a novel route to aid unravelling common complex diseases.

Methylation QTLs in the developing brain and their enrichment in schizophrenia risk loci

We characterized DNA methylation quantitative trait loci (mQTLs) in a large collection (n = 166) of human fetal brain samples spanning 56–166 d post-conception, identifying >16,000 fetal brain mQTLs. Fetal brain mQTLs were primarily cis-acting, enriched in regulatory chromatin domains and transcription factor binding sites, and showed substantial overlap with genetic variants that were also associated with gene expression in the brain. Using tissue from three distinct regions of the adult brain (prefrontal cortex, striatum and cerebellum), we found that most fetal brain mQTLs were developmentally stable, although a subset was characterized by fetal-specific effects. Fetal brain mQTLs were enriched amongst risk loci identified in a recent large-scale genome-wide association study (GWAS) of schizophrenia, a severe psychiatric disorder with a hypothesized neurodevelopmental component. Finally, we found that mQTLs can be used to refine GWAS loci through the identification of discrete sites of variable fetal brain methylation associated with schizophrenia risk variants.We characterized DNA methylation quantitative trait loci (mQTLs) in a large collection (n=166) of human fetal brain samples spanning 56–166 days post-conception, identifying >16,000 fetal brain mQTLs. Fetal brain mQTLs are primarily cis-acting, enriched in regulatory chromatin domains and transcription factor binding sites, and show significant overlap with genetic variants also associated with gene expression in the brain. Using tissue from three distinct regions of the adult brain (prefrontal cortex, striatum and cerebellum) we show that most fetal brain mQTLs are developmentally stable, although a subset is characterized by fetal-specific effects. We show that fetal brain mQTLs are enriched amongst risk loci identified in a recent large-scale genome-wide association study (GWAS) of schizophrenia, a severe psychiatric disorder with a hypothesized neurodevelopmental component. Finally, we demonstrate how mQTLs can be used to refine GWAS loci through the identification of discrete sites of variable fetal brain methylation associated with schizophrenia risk variants.

De novo identification of differentially methylated regions in the human genome

BackgroundThe identification and characterisation of differentially methylated regions (DMRs) between phenotypes in the human genome is of prime interest in epigenetics. We present a novel method, DMRcate, that fits replicated methylation measurements from the Illumina HM450K BeadChip (or 450K array) spatially across the genome using a Gaussian kernel. DMRcate identifies and ranks the most differentially methylated regions across the genome based on tunable kernel smoothing of the differential methylation (DM) signal. The method is agnostic to both genomic annotation and local change in the direction of the DM signal, removes the bias incurred from irregularly spaced methylation sites, and assigns significance to each DMR called via comparison to a null model.ResultsWe show that, for both simulated and real data, the predictive performance of DMRcate is superior to those of Bumphunter and Probe Lasso, and commensurate with that of comb-p. For the real data, we validate all array-derived DMRs from the candidate methods on a suite of DMRs derived from whole-genome bisulfite sequencing called from the same DNA samples, using two separate phenotype comparisons.ConclusionsThe agglomeration of genomically localised individual methylation sites into discrete DMRs is currently best served by a combination of DM-signal smoothing and subsequent threshold specification. The findings also suggest the design of the 450K array shows preference for CpG sites that are more likely to be differentially methylated, but its overall coverage does not adequately reflect the depth and complexity of methylation signatures afforded by sequencing.For the convenience of the research community we have created a user-friendly R software package called DMRcate, downloadable from Bioconductor and compatible with existing preprocessing packages, which allows others to apply the same DMR-finding method on 450K array data.

Epigenetic Studies of Psychosis: Current Findings, Methodological Approaches, and Implications for Postmortem Research

It has been widely speculated that epigenetic changes may play a role in the etiology of psychotic illnesses such as schizophrenia and bipolar disorder. Epigenetics is the study of mitotically heritable, but reversible, changes in gene expression that occur without a change in the genomic DNA sequence, brought about principally through alterations in DNA methylation and chromatin structure. Although numerous studies have examined psychosis-associated gene expression changes in postmortem brain samples, epigenetic studies of psychosis are in their infancy. In this article, we discuss methodologic and logistic issues related to epigenomic studies using postmortem brain tissue, before discussing the future implications of such research for our understanding of psychosis.

Schizophrenia is associated with dysregulation of a Cdk5 activator that regulates synaptic protein expression and cognition

Cyclin-dependent kinase 5 is activated by small subunits, of which p35 is the most abundant. The functions of cyclin-dependent kinase 5 signalling in cognition and cognitive disorders remains unclear. Here, we show that in schizophrenia, a disorder associated with impaired cognition, p35 expression is reduced in relevant brain regions. Additionally, the expression of septin 7 and OPA1, proteins downstream of truncated p35, is decreased in schizophrenia. Mimicking a reduction of p35 in heterozygous knockout mice is associated with cognitive endophenotypes. Furthermore, a reduction of p35 in mice results in protein changes similar to schizophrenia post-mortem brain. Hence, heterozygous p35 knockout mice model both cognitive endophenotypes and molecular changes reminiscent of schizophrenia. These changes correlate with reduced acetylation of the histone deacetylase 1 target site H3K18 in mice. This site has previously been shown to be affected by truncated p35. By restoring H3K18 acetylation with the clinically used specific histone deacetylase 1 inhibitor MS-275 both cognitive and molecular endophenotypes of schizophrenia can be rescued in p35 heterozygous knockout mice. In summary, we suggest that reduced p35 expression in schizophrenia has an impact on synaptic protein expression and cognition and that these deficits can be rescued, at least in part, by the inhibition of histone deacetylase 1.

Epigenetic Studies of Schizophrenia: Progress, Predicaments, and Promises for the Future

Increased understanding about the functional complexity of the genome has led to growing recognition about the role of epigenetic variation in the etiology of schizophrenia. Epigenetic processes act to dynamically control gene expression independently of DNA sequence variation and are known to regulate key neurobiological and cognitive processes in the brain. To date, our knowledge about the role of epigenetic processes in schizophrenia is limited and based on analyses of small numbers of samples obtained from a range of different cell and tissue types. Moving forward, it will be important to establish cause and effect in epigenetic studies of schizophrenia and broaden our horizons beyond DNA methylation. Rather than investigating genetic and epigenetic factors independently, an integrative etiological research paradigm based on the combination of genomic, transcriptomic, and epigenomic analyses is required.