Autism spectrum disorders (ASD) are increasingly common neurodevelopmental disorders defined clinically by a triad of features including impairment in social interaction, impairment in communication in social situations and restricted and repetitive patterns of behavior and interests, with considerable phenotypic heterogeneity among individuals. Although heritability estimates for ASD are high, conventional genetic-based efforts to identify genes involved in ASD have yielded only few reproducible candidate genes that account for only a small proportion of ASDs. There is mounting evidence to suggest environmental and epigenetic factors play a stronger role in the etiology of ASD than previously thought. To begin to understand the contribution of epigenetics to ASD, we have examined DNA methylation (DNAm) in a pilot study of postmortem brain tissue from 19 autism cases and 21 unrelated controls, among three brain regions including dorsolateral prefrontal cortex, temporal cortex and cerebellum. We measured over 485 000 CpG loci across a diverse set of functionally relevant genomic regions using the Infinium HumanMethylation450 BeadChip and identified four genome-wide significant differentially methylated regions (DMRs) using a bump hunting approach and a permutation-based multiple testing correction method. We replicated 3/4 DMRs identified in our genome-wide screen in a different set of samples and across different brain regions. The DMRs identified in this study represent suggestive evidence for commonly altered methylation sites in ASD and provide several promising new candidate genes.
Autism spectrum disorders (ASD) are common neurodevelopmental disorders defined by three core clinical features: (1) impairment in social interaction; (2) impairment in communication in social situations; and (3) restricted and repetitive patterns of behaviors and interests.1, 2 Given the striking recent increase in ASD prevalence and its associated social and economic impact,3 there has been considerable interest in understanding the biological basis of ASD;4 however the molecular underpinnings of ASD still remain elusive.
Familial aggregation, rare chromosomal abnormality and twin studies suggest there is a strong genetic basis to ASD. Numerous genetic studies have examined common variants, rare mutations and copy number variants (CNVs) across many thousands of autistic children but have had limited success. The strongest genetic candidates identified to date involve rare mutations. For example, rare variants have been identified in only 6/461 ASD cases for CACNA1H5, 6 and CNTN4 deletions have been identified in 7/∼2000 cases.7, 8, 9, 10 These genes represent two of the five leading candidate genes in ASD to date.11 Several promising recent studies have revealed an association between de novo mutations and CNVs with ASD; however de novo events do not directly explain the high heritability of ASD.12 A recent review suggests that at most 10–15% of all non-syndromic cases of ASD are associated with genetic alterations,13 and to date, whole-genome and in-depth exon sequencing studies have suggested ASD is minimally explained by genetics alone.14
Although epigenetic involvement has been a persistent minority view of ASD etiology, there is mounting evidence to suggest the contribution of epigenetics to ASD is stronger than previously thought. Direct evidence15 comes from identification of ASD-associated chromosomal abnormalities in imprinted regions, ASD linkage and association in areas of known imprinting, and evidence of parent-of-origin effects in association and linkage signals.16, 17 Additional support stems from the observation of autistic features, sometimes reaching the diagnostic threshold, in individuals affected with known epigenetic disorders such as Angelman, fragile X and Rett syndromes. Rett syndrome is caused by mutations in MECP2,18 a gene that encodes a protein responsible for recognizing DNA methylation (DNAm) marks throughout the genome. Angelman syndrome involves alterations of the chromosome 15q11-q13 imprinted gene cluster.19 Finally, fragile X syndrome is caused by the expansion of a methylated CGG repeat in the 5′ untranslated region of the FMR1 gene,20 resulting in inappropriate gene silencing.
Previous genome-wide DNAm assays, applied to ASD21, 22 and other diseases, have focused on measuring DNAm at CpG sites assumed to be functionally important, that is, CpG islands (CGI) and promoters. However, we recently demonstrated that most disease and tissue-associated changes in DNAm occur outside of CGI and promoter regions, specifically at CGI shores.23, 24 Non-island and promoter regions are the predominant sites of aberrant DNAm for many diseases including several types of cancers23, 25, 26, 27, 28, 29 and rheumatoid arthritis.30,31 Previous limited studies of specific candidate genes and CGI or promoter arrays have shown epigenetic differences in lymphoblastoid cell lines and brain samples from individuals with ASD.21, 22, 32, 33, 34, 35 However, these studies were limited either in scope, focused on particular regions of the genome, or by sample size, and/or by cell type (lymphoblast cell lines). There are 20 probes on the Infinium HumanMethylation450 BeadChip (450 k array), the DNAm platform used in this study, located in regions previously identified by candidate region studies (Supplementary Table 1).32, 33 One recent study that examined trimethylated histone 3 lysine 4 marks in the postmortem prefrontal cortex (PFC) brain tissue identified a slight spreading of histone 3 lysine 4 marks, normally restricted to promoter regions, into gene bodies among 4/16 autistic individuals.36 Nonetheless, to date, no study has examined DNAm on a genome scale, not limited to CGIs, in the brain samples of ASD cases.
Here, we report the first genome-wide examination of DNAm in ASD among 40 postmortem brain samples, including 19 cases and 21 controls, across three brain regions, using the 450 k array. Availability of ASD brain samples is extremely limited,37 with the numbers examined in this study being comparable to other molecular analyses of ASD postmortem brain samples.36, 38 The 450 k array quantitatively measures DNAm at 485 577 genomic loci at high value content regions including all CGI and promoter regions as well as CpG shores, cancer and tissue-differentially methylated regions (DMRs), non-coding RNAs and DNase hypersensitive sites.39, 40 We applied a methodology termed ‘bump hunting’41 to our analysis, which allows effective modeling of measurement error and biological variability, detection of DMRs, and assessment of genome-wide statistical significance (via permutation testing). Identifying regions of differential methylation is advantageous to single site analysis for multiple reasons including better protection from technical artifacts associated with individual probes and functional significance of regional methylation change versus at a single CpG.
Materials and methods
Human postmortem brain tissue samples
We acquired 41 postmortem brain tissue samples, including 20 cases and 21 controls, representing three brain regions: temporal cortex (TC, n=16), PFC (n=12) and cerebellum (CBL, n=13) from the Autism Tissue Program and Harvard Brain Tissue Resource Center at McLean Hospital, Belmont, MA, USA (TC and PFC samples) and the National Institute of Child Health and Human Development Brain and Tissue Bank for Developmental Disorders at the University of Maryland, Baltimore, MD, USA (CBL samples). These 41 samples represent 36 individuals; five individuals have both a TC and PFC sample included in our analyses (Figure 1a). One of the 20 case samples was removed from downstream analyses due to quality control concerns (described in detail in the statistical methods section below). For each brain region, autistic and control samples were matched as best as possible for age, sex and postmortem interval; no significant differences were detected (t-test, P⩽0.05 and Supplementary Table 2). Furthermore, we examined the relationship between DNAm and age/PMI for all four DMRs identified in our genomic screen; no correlation between the covariates and DNAm was observed (Supplementary Figures 1 and 2). All autistic individuals included in our analysis were diagnosed using scores from the Autism Diagnostic Interview and/or the Autism Diagnostic Observation Schedule. In Supplementary Table 3, we provide a list of postmortem brain samples and their associated features.
Illumina Infinium 450 k array methylation measurements
Genomic DNA was extracted from TC and PFC samples using Trizol (Invitrogen, Carlsbad, CA, USA) and from CBL samples using the MasterPure DNA purification kit (Epicentre Biotechnologies, Madison, WI, USA) according to the manufacturer’s specifications for each kit. Each DNA sample was bisulfite treated, 500 ng of gDNA, using the EZ DNAm kit (Zymo Research, Irvine, CA, USA) according to the manufacturer’s specifications, optimized for the 450 k array. All of the samples per brain region were processed on the same plate with cases and controls randomized across the wells to minimize potential plate or batch effects.
Bisulfite pyrosequencing validation experiments
We performed validation experiments using 13/16 TC samples that were examined on 450 k array. We bisulfite converted 500 ng of gDNA using the EZ DNAm kit (Zymo Research) according to the manufacturers specifications. Primers were designed using MethPrimer42 and nested PCR reactions were performed under standard conditions, using a 50 °C annealing temperature. Genome coordinates (HG19) of the measured sites and primer sequences are provided in Supplementary Tables 4 and 5, respectively.
Overview and quality control measures
All statistical analyses were performed using R 2.15 and Bioconductor 2.9. Raw intensity files (.idat) were obtained and processed using minfi package43 to obtain log ratios of methylation percentage (M-values). We applied several quality control measures to remove any spurious samples or probes. First, we examined 450 k array control probes to assess bisulfite conversion, extension, hybridization, staining, specificity, negative control and others; no outlier samples were detected. Next, we checked for sex discrepancies by comparing self-reported genders against data-derived sex values. No sex discrepancies were identified. We then looked for poorly performing arrays by calculating the total array intensity (sum of the methylated and unmethylated signals across all probes on the array) for each sample; no substantial differences were detected. Unsupervised clustering of samples identified one sample that was inappropriately clustering with a different brain region (PFC sample clustering with CBL samples); this sample was removed from downstream analysis. We removed probes that had an annotated SNP (dbSNP134) at the single-base extension or CpG site, as it is possible that SNP differences in these locations may manifest as differential methylation on the 450 k array, leaving a total of 428 526 probes.
Preprocessing and normalization
The signal in the methylated and unmethylated channels was first computed without background correction. Each channel was normalized separately, by first quantile normalizing the signal for all autosomal probes and second by quantile normalizing the signal for the sex chromosomes for each sex separately. After normalization, log ratios of methylation percentages were computed (M-values). Previous work44 has shown M-values to perform better than beta values for analysis of differential methylation for Illumina methylation arrays.
Identification of DMRs
We adapted the bump hunting technique previously described41 to the 450 k array. Probes were assigned to clusters so that two neighboring probes in the same cluster are separated by at most 500 bp, resulting in a total of 181 762 clusters of which 39 654 contain three or more probes. For each probe, we estimated the difference in average log ratios between cases and controls, controlled for sex, and smoothed these estimated differences using running medians. Next, the smoothed estimated differences were thresholded based on the 97.5% percent quantile of the empirical distribution of the smoothed estimated differences. This yielded 1179, 1347 and 1227 putative DMRs for TC, PFC and CBL analyses, respectively. Significance was assigned by permutation testing. For each of 1000 permutations of case–control status, a new list of putative DMRs was obtained. The genome-wide family-wise error rate for each observed DMR was calculated as the proportion of null-derived DMRs across the genome with more CpGs and greater difference between cases and controls than the observed DMR. Because a given observed DMR is compared against findings in the entire genome, the procedure automatically adjusts for multiple testing. The numbers of putative DMRs and the number of measured CpGs contained within these putative DMRs are 1179 (5278 CpGs) for TC, 1347 (6058 CpGs) for PFC and 1227 (5277 CpGs) for CBL. Note that most of the genome, roughly 98.7% of analyzed CpGs, is not inside a DMR, and is therefore not assigned an empirical significance via this approach. Similar to previously reported genome-wide DMR screens,45 the adjusted P-value cutoff (FWER) used for our genome-wide analyses was 0.1. We decided to use this cutoff, as opposed to 0.05, because we wanted to be slightly more inclusive and were willing to test a larger number of DMRs in our downstream replication analyses. Using P⩽0.1 resulted in a total of four DMRs compared with three DMRs with P⩽0.05.
For the three DMRs identified in our genome-wide screen of TC tissue, we performed replication analyses in PFC and CBL tissues using methylation data obtained using the 450 k array. For replication purposes, the PFC and CBL data were restricted to include only the 74 probes that were located within the three significant TC DMRs. Similarly, for the DMR we identified in CBL tissue, we performed replication analyses by restricting our analysis in PFC and TC tissues to the 12 sites located within the CBL DMR. For each probe, we computed a probe-specific P-value using a permutation test (permuting sample labels) in a linear model controlling for sex. Under the null hypothesis of no difference between cases and controls, these probe-specific P-values are uniformly distributed. Hence, assuming independence, the total number of probes in a DMR with a probe-specific P-value of less than 5% follows a binomial distribution with a size factor equal to the number of probes in the DMR and a probability parameter of 0.05. We use this fact to combine the probe-specific P-values into an overall DMR-specific P-value for replication purposes. An overall DMR-specific P-value of less than 5% was considered replication.
Assessment of cell composition differences
Brain cell type-specific DNAm data and cell epigenotype-specific patterns46 were acquired using the cell epigenotype-specific package (http://psychiatry.igm.jhmi.edu/kaminsky/software.htm) in R/Bioconductor. Overlap between the 450 k array probes identified in our brain samples and the top 10 000 cell epigenotype-specific marks for brain cell types was computed by simply comparing the two lists of 450 k array probe names. Using the cell epigenotype-specific package, we then estimated the proportion of neuronal and glial cells for each postmortem brain tissue sample in our study, as specified previously.46
Figure 1a depicts an overview of our experimental design and the brain samples utilized in our analyses. Briefly, we performed individual methylome analyses for each of the three brain regions: dorsolateral PFC (6 cases/5 controls), TC (6 cases/10 controls) and CBL (7 cases/6 controls) using a ‘bump hunting’ approach.41 Bump hunting uses a permutation procedure to assess significance in such a way that (1) reported P-values are adjusted for multiple testing and (2) only a small subset of the genome, specifically a set of putative interesting regions, is assigned a P-value.
For each of the three brain regions, we performed a genome-wide screen and identified a total of four genome-wide significant (adjusted P⩽0.1) DMRs, three in the TC and one in the CBL (Figures 1b and c and Table 1). Because bump hunting identifies a differentially methylated region (DMR) rather than a differentially methylated position, the data display is similar but with a somewhat more austere appearance than a conventional Manhattan plot. Therefore, we have termed it a ‘dry Manhattan’ plot suitable for this type of DNAm analysis. As described in detail in the Methods section, empirical P-values reported in our dry Manhattan plots (Figures 1b and c) were calculated by permutation with a family-wise error rate of 0.1.
As shown in Figure 2a, we identified a DMR near the end of the 3′ UTR of PRRT1, proline-rich transmembrane protein 1, that extends a few hundred base pairs upstream, with relative hypomethylation in the TC tissue of autistic individuals (shown in green) compared with controls (shown in purple). On average, the cases are 7.8% less methylated at this DMR (empirical P=0.001) than controls (Table 1). It is possible that changes in CNVs may manifest as DNAm changes on the 450 k array. For example, changes in DNAm would be observed if a gained copy has a different methylation pattern from the original. While existing work suggests that CNV does not bias Illumina array measurements of DNAm,47 we still consider it important to address the possibility of CNV at sites of DNAm differences. We would expect CNVs to result in different total intensities, that is, the sum of the methylated and unmethylated channels, on the 450 k array. Therefore, to assess potential CNV changes, in the second panel of Figure 2a, we plot the difference between the total probe intensity for a given individual and the mean total intensity across all individuals for a given probe, with purple and green points denoting control and ASD, respectively. There are no clear differences between the case and control groups with respect to CNV status; thus, it is likely the DMR does not reflect a CNV.
We also identified a DMR (empirical P=0.013) that is on average 6.6% less methylated in ASD TC tissue, shown in green, compared with controls, shown in purple (Table 1 and Figure 2b). This DMR is located within the promoter regions of tetraspanin 32 (TSPAN32), and chromosome 11 open reading frame 21 (C11orf21), and continues into the gene body of C11orf21 (bottom panel of Figure 2b). We do not observe substantial changes in CNV levels between cases and controls for this DMR (second panel of Figure 2b).
The third DMR (empirical P=0.019) in TC tissue is on average 13.9% more methylated in ASD, shown in green, compared with controls, shown in purple (Table 1 and Figure 3). The relatively hypermethylated DMR is located in an intergenic region; the nearest gene is ZFP57, located 3.5 kb away (Figure 3a). Although a few control individuals appear to have a slight increase in copy number for a few probes, across the region there is no substantial difference in CNV state between cases and controls (Figure 3a, second panel).
Among 13 CBL samples, including seven cases and six controls, we identified one significant DMR located within SDHAP3, succinate dehydrogenase complex, subunit A, flavoprotein pseudogene 3. Cases are, on average, 15.8% more methylated than controls across this region (Table 1), although there is considerable variation among the ASD group that seems to be, at least in part, related to CNV (Figure 4a). The cases with higher methylation levels also have higher total intensities, representing potential CNVs. Thus, the methylation change detected in this region may be influenced by an underlying CNV change. Nonetheless, it still implicates this particular region as important in the etiology of ASD.
We validated our genome-wide results from TC using bisulfite pyrosequencing, a quantitative and independent method for measuring DNAm. Pyrosequencing confirmed our 450 k array results for all three DMRs tested (Supplementary Figure 3). Similar to our array-based results, PRRT1 and C11orf21 are relatively hypomethylated and ZFP57 is relatively hypermethylated in autistic individuals compared with controls.
Finally, we sought to confirm DMRs identified in our genome-wide screen in an independent set of samples. In total, 74 probes are located within the three TC DMRs and 12 probes are located within the one cerebellar DMR. Given the limited availability of postmortem brain samples from autistic individuals, we considered replication of TC DMRs via evidence of association at those probes in the PFC and CBL tissue samples, and used the TC and PFC sample sets to replicate our CBL DMR finding. As the samples for each brain region were obtained from different individuals, with the exception of five individuals who are represented in both the TC and PFC sample sets, this design is suitable to evaluate how well our genome-wide findings replicate in different individuals and across different brain regions. Figure 1 provides a summary of the total number of individuals and probes examined in our replication analyses.
For the PRRT1-associated DMR discovered in TC, we found significant methylation differences between cases and controls in PFC tissue for 14 of 33 sites (Figure 2b, blue). For clarity, we use dashed boxes to highlight methylation values that correspond to the probes with significant nominal P-values. Using a binomial distribution (Materials and Methods), we assigned this a replication P-value of 2.0e−10. We did not observe any differences related to copy number for this region in PFC samples (upper middle, Figure 2b). We also observed two significant CpG sites and others with suggestive significance among the cerebellar tissue (Figure 2c) in an independent sample of seven cases and six controls. Consistent with both our genome-wide screen and PFC replication results, the CBL tissue from autistic individuals showed less methylation than control individuals at the PRRT1 locus (Figure 2c) and no differences in CNV between cases and controls.
Replication analysis of the TC DMR associated with TSPAN32 and C11orf21 revealed 7 of 26 and 1 of 26 significant (P⩽0.05, third panel of Figures 2e and f) differentially methylated loci in the PFC and CBL tissue samples, with replication P-values of 2.2e−4 and 74%, respectively (Figures 2e and f). However, as we would expect to see one significant difference by chance, we do not report the CBL as significant in our replication result (Table 1, Figure 1a). As shown in the top panels of Figures 2e and f, autistic individuals (blue points) are less methylated than the controls (orange points) for both the PFC and CBL tissues, consistent with our genome-wide result in TC tissue. In addition, no CNV changes are observed at the significant loci (second panel, Figures 2e and f).
The third DMR identified from TC, located 3.5 kb upstream of ZFP57, showed sex-specific replication in the CBL samples (Figure 3i). For this DMR, we decided to stratify the methylation data by sex, within each brain region, because we observed two clusters of methylation values that were correlated with sex in Figure 3a. Interestingly, for both TC and CBL tissues, upon stratification by sex we found that normal females are less methylated than autistic females. As there was only one female, we could not assess significant differences between cases and controls for females in PFC tissue. However, in the one female (autistic) with PFC data, we observed high methylation levels (Figure 3f), consistent with our findings in the other two regions for autistic females (Figures 3c and i). We also observed suggestive evidence that autistic males have more DNAm variability in TC and CBL tissues than control individuals (Figures 3b and h) for this genomic region. When we examine males and females as one sample set, we do not observe statistically significant P-values in our replication data sets for this region.
As five individuals contributed both a TC and PFC sample, we also examined DNAm in PFC for the set of non-overlapping individuals, leaving one control and five cases. Although we cannot compute statistical significance with a single control sample, we observed distinct differences in DNAm between the control sample and all cases for several loci across all three genomic regions examined (Supplementary Figures 4, 5 and 6). The direction of the differential methylation was also consistent with our initial findings for all three regions. Furthermore, although five of the prefrontal and TC samples were obtained from the same individual, they are independent measurements of DNAm from two distinct biological sources and thus are appropriate for assessing the validity of DMRs identified in our genome-wide screen.
Lastly, although none of the loci located in the CBL DMR in SDHAP3 reached statistical significance in the replication sets (Figures 4b and c), we observed the same trend in PFC as we did in CBL. More specifically, a few autistic samples in PFC show methylation levels near 50% that are potentially related to CNV changes (Figure 4b).
Brain tissue heterogeneity
We evaluated the possibility that our ASD-associated DMRs may reflect underlying cell-type heterogeneity between ASD cases and controls using neuronal and glial cell type-specific methylation data.46 None of the 86 probes located within the ASD DMRs we identified are among the list of 10 000 probes with neuron-specific methylation values from a recent report.46 Furthermore, for each of our brain samples, we computed the proportion of neuronal and glial cells using the Guintivano et al.46 algorithm and found no significant differences between the ASD cases and controls for either brain region (Supplementary Table 6). Thus, our findings do not simply reflect a shift in the proportion of neuronal and glial cells between ASD case and control samples.
We provide the first evidence for significant DNAm changes in postmortem brain tissue from ASD patients. After adjusting for multiple testing, we identified a total of four DMRs, three in TC tissues and one in CBL. The magnitude of methylation change identified here, ranging from 6.6–15.8%, is comparable to those observed in other studies of DNAm in psychiatric disease.48, 49 Three of the four DMRs identified via genome-wide screen replicated in independent samples from different brain regions. Although the fourth DMR did not meet significant criteria for replication, it did show the same trend in the PFC samples, as we observed in our genome-wide screen using CBL samples. Finally, we demonstrated that the DMRs we identified are not related to underlying brain tissue cell composition differences between ASD and control individuals.
The autism-associated DMRs we identified represent biologically diverse and interesting genomic regions. For example, the DMR within the PRRT1 3′ UTR overlaps two DNase hypersensitive sites and an alternative transcript finish site. Given the location, it is possible that this DMR is an important regulatory site.50, 51 Although little is known about the function or expression patterns of PRRT1 in humans it has been shown to be specifically expressed in the hippocampus of marmoset,52 a nonhuman primate often used as an ideal model for a wide-variety of central nervous system disorders. The hippocampus brain region has been previously implicated in human studies of ASD demonstrating macroscopic and microscopic anatomical differences53, 54, 55, 56 and altered synaptic function and plasticity57 in autistic individuals. Mutations in other genes in the PRRT family have been shown to cause several neurological disorders in humans, some of them developmental in nature, including familial infantile seizures,58 paroxysmal kinesigenic dyskinesia,59 hemiplegic migraine,60 paroxysmal kinesigenic dyskinesia combined with infantile seizures,61, 62 and benign familial infantile seizures.63, 64
The second DMR identified in TC tissue is located in the promoter regions of TSPAN32 and C11orf21 and extends into the gene body of C11orf21. While there is no literature to describe the function of C11of21, TSPAN32 is important in cellular immunity65 and acts as a structural and cell signaling scaffold protein.66 Functional mutations in other tetraspanins have been identified in schizophrenia and bipolar pateints.67 Mutations in several other scaffolding proteins such as SHANK3,68, 69, 70 SHANK271 and NBEA,72 have been identified in autistic individuals. Interestingly, this DMR is also located within an imprinted region of the genome. Imprinting73 and parent-of-origin specific effects16, 17 have been previously implicated in ASD.
The third DMR is located about 3.5 kb upstream of ZFP57 and overlaps the 5′ end of an alternatively spliced EST. ZFP57 is instrumental in maintaining imprinting marks during development74, 75 by providing a mechanism for targeting DNA methyltransferase,76 responsible for transferring methyl groups to cytosines, to specific locations in the genome.77 This is directly relevant to ASD, as other methylation machinery proteins are mutated in neurodevelopmental syndromes associated with ASD, for example, Rett syndrome.18
Finally, in cerebellar tissue, we identified a DMR at an alternative promoter for SDHAP3, which is associated with a non-coding RNA and a small coding RNA. This DMR falls directly on a CTCF binding site and an active regulatory element site identified by the ENCODE project.78 Thus, it is likely to be an important regulatory site; however, currently there is no literature describing the function of this particular gene. SDHAP3 is a member of the succinate dehydrogenase gene family that includes genes that are critical components of the metabolic machinery. Mitochondrial respiratory chain complex protein dysfunction has been previously associated with ASD.79, 80, 81
For one of the DMRs, near ZPF57, we identified a striking difference in methylation between autistic and control females in the cerebellar tissue. We performed a BLAST search of the DNA sequence for this DMR and did not find alignment to any other regions of the genome, including sex chromosomes. While intriguing, we examined a relatively small number of female samples in our analyses and the biological significance of sexually dimorphic DNAm differences in the brain is unclear at this time. This result suggests future epigenetic studies may benefit from inclusion of females and additional sex-specific analyses.
The autism-associated DMRs identified in this study may be influenced by genetic variants or environmental exposures. Davies et al.82 recently showed some sites of methylation variation across individuals, potentially influenced by genetic or environmental factors, persist across blood and brain tissues. For mental health research, variation across brain regions is of particular importance. Given the scope of this study and its limited sample size, we cannot sufficiently address how DNAm varies between normal individuals and different brain regions. Defining the landscape of methylation variation, across individuals and between tissue types, is a very important question and avenue of future research.
While this study is the first to identify commonly altered DNAm in brain tissue from autistic individuals, we recognize there are several limitations that need to be carefully considered. First, from an epidemiological and statistical perspective we examined a relatively small number of samples, 40 in total. It is difficult to ascertain a large number of reliably phenotyped postmortem brain tissues from autistic individuals given the scarcity of the resource.37 This is a problem the field of ASD faces, generally. While this study focused on the brain tissue, due to disease relevance and a lack of DNAm data in previous autism brain studies, there is also a need for complementary autism epigenomic studies in peripheral tissues, such as blood. Blood-based samples are particularly useful in ASD because large numbers of samples can be collected and because useful biomarkers for disease are needed and most likely will come from this tissue, as opposed to brain tissue, for practical reasons. Second, the DMR we identified at SDHAP3 in CBL showed a DNAm change in several autistic individuals that was potentially also related to a CNV. The 450 k array is now the primary platform utilized for large epidemiology studies and this result highlights the need to examine and recognize the potential contribution of CNVs to methylation signals in these large studies. In addition, this finding demonstrates the need for new studies to clarify the relationships between CNVs and DNAm.
Despite these limitations, the findings presented in this study are of significance to the field of ASD for several reasons. To our knowledge, this is the first study to examine DNAm in ASD brains at genome scale in regions outside of CGIs and promoters, identifying four DMRs associated with the disorder. Second, while traditional genetic studies have identified rare variants in a minority of ASD cases, here, we identified genomic regions that commonly show differential methylation between autistic and control individuals. Finally, the DMRs themselves are useful candidate regions for follow up studies.
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We thank Daniel Geschwind and Neelroop Parikshak for sharing the PFC and TC samples, obtained from the Autism Tissue Program (ATP) of Autism Speaks, for these analyses. In addition, we would also like to thank the NICHD Brain and Tissue Bank for Neurodevelopmental Disorders at The University of Maryland for providing brain samples from the CBL brain region. This work was supported by the US National Institutes of Health Centers of Excellence in Genomic Science, 5P50HG003233 to APF and Department of Defense (CDMRP) AR080125 to APF and WEK.
The authors declare no conflict of interest.
Supplementary Information accompanies the paper on the Molecular Psychiatry website
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Ladd-Acosta, C., Hansen, K., Briem, E. et al. Common DNA methylation alterations in multiple brain regions in autism. Mol Psychiatry 19, 862–871 (2014). https://doi.org/10.1038/mp.2013.114
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