Candidate biomarkers from the integration of methylation and gene expression in discordant autistic sibling pairs

doi:10.21203/rs.3.rs-2217101/v1

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Candidate biomarkers from the integration of methylation and gene expression in discordant autistic sibling pairs

https://doi.org/10.21203/rs.3.rs-2217101/v1

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published 03 Apr, 2023

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While the genetics of autism spectrum disorders (ASD) has been intensively studied, resulting in the identification of over 100 putative risk genes, the epigenetics of ASD has received less attention, and results have been inconsistent across studies. We aimed to investigate the contribution of DNA methylation (DNAm) to the risk of ASD and identify candidate biomarkers arising from the interaction of epigenetic mechanisms with genotype, gene expression, and cellular proportions. We performed DNAm differential analysis using whole blood samples from 75 discordant sibling pairs of the Italian Autism Network collection and estimated their cellular composition. We studied the correlation between DNAm and gene expression accounting for the potential effects of different genotypes on DNAm. We showed that the proportion of NK cells was significantly reduced in ASD siblings suggesting an imbalance in their immune system. We identified differentially methylated regions (DMRs) involved in neurogenesis and synaptic organization. Among candidate loci for ASD, we detected a DMR mapping to CLEC11A (neighboring SHANK1) where DNAm and gene expression were significantly and negatively correlated, independently from genotype effects. As reported in previous studies, we confirmed the involvement of immune functions in the pathophysiology of ASD. Notwithstanding the complexity of the disorder, suitable biomarkers such as CLEC11A and its neighbor SHANK1 can be discovered using integrative analyses even with peripheral tissues.

Health sciences/Diseases/Psychiatric disorders/Autism spectrum disorders

Biological sciences/Genetics/Genomics

Autism spectrum disorders (ASD) are diagnosed in children at a young age if they exhibit difficulties in social interaction and communication and have restricted interests and repetitive behaviors^1,2. Affected children are ~1-2% of the population, and the prevalence is at least three times higher in boys than in girls³. Genetic studies have been instrumental in the discovery of multiple types of risk variants and the gain of new insights into the biology of ASD^4-7. It has been acknowledged in several studies that ASD liability likely depends on additive effects of common and rare variants^8-11, and recent sequencing efforts have led to the identification of over 100 putative ASD risk genes, the majority of which are neuronally expressed⁷. Consistently, post-mortem investigations on brain samples from ASD subjects have revealed gene expression signatures associated with multiple synaptic functions^5,12-14, in addition to a dysregulation of immune function genes, which has also been shown by investigations conducted in blood^15-19. However, like for other complex disorders, genetic variants (including structural and chromosomal variations) do not fully explain the heritability of the disease, suggesting that the risk for the disease is not exclusively driven by genetic variants^20,21.

DNA methylation (DNAm) represents one of the potential epigenetic mechanisms that may contribute to the risk of ASD due to interactions with genetic elements during the development of normal brain functions^22-26. The interaction between ASD-associated genes and DNAm occurs through changes in the chromatin state driven by DNAm alterations that ultimately affect the expression of neurodevelopmental genes. As an example, Nguyen et al. provided strong evidence for epigenetic regulation via differential DNAm of the activity of two genes (BCL-2 and RORA) in the autistic brain, validating their methylation status of BCL-2 and RORA in lymphoblastoid cells of discordant monozygotic ASD twins and unaffected siblings²⁷. Addressing the relationship between DNAm and expression of disease-associated genes is crucial for a full understanding of the etiology of heterogeneous diseases, but for ASD this relationship has been examined for a limited number of risk genes^28-31.

Genes may be up-or down-regulated as a response to modifications of DNAm that are linked to environmental variables and DNA sequence variations. Environmental variables such as advanced paternal age and chemical exposures have been shown to have strong effects on DNAm profile, and at the same time to increase the risk for ASD diagnosis^28,32,33. Genetic variants may also influence DNAm levels through allele-specific methylation or methylation quantitative trait loci (mQTL), as it has been shown by large-scale investigations conducted in blood^34-37 and in brain^38,39. Of note, mQTLs have been found to be highly correlated between independent brain and blood samples⁴⁰, opening the possibility to learn about epigenetic mechanisms in the brain by studying peripheral tissues, and identifying biomarkers that reflect complex epigenetic interactions contributing to ASD risk, which would be otherwise overlooked.

Diagnosis of ASD still relies on the fulfillment of descriptive criteria, even though there is a strong motivation to develop effective biomarkers that can be used for early diagnosis. Some of the main candidate biomarkers for ASD have been identified through differential DNAm analysis. The receptor of the oxytocin hormone is one well-studied example. Its genomic region has been found differentially methylated in several studies with ASD and non-ASD controls, but the reported effects of DNAm on gene expression and protein synthesis have not been consistent across studies^41-43. Differential DNA methylation analyses have been conducted in post-mortem tissues from ASD subjects and neurotypical controls, resulting in the identification of signatures of DNAm alterations and multiple biological pathways involved^44-46. For biomarker discovery, brain tissues are less suitable than peripheral ones as they are much less accessible, and they also tend to be affected by post-mortem degradation. However, it must be taken into account that epigenetic signatures tend to be tissue-specific: interindividual variation in whole blood is not a strong predictor of interindividual variation in the brain, although DNA methylation in whole blood significantly co-varies with that in the brain at some genomic loci, including loci relevant for neurodevelopmental disorders^34,47. Therefore, whilst on the one hand, peripheral tissues might represent a valid alternative to brain tissues for detecting and developing effective biomarkers for ASD, on the other hand, they contain only a small amount of information that is shared with brain samples⁴⁸ and detecting significant signals may require very large sample sizes⁴⁹.

Here, we examined methylation data from whole blood samples of ASD discordant siblings that belonged to the Italian Autism Network (ITAN) cohort. We aimed to detect regions of the genome showing discordant levels of DNAm which correlated with the expression of nearby genes. As this association between diagnosis, DNAm, and gene expression is likely to be biased by demographic factors⁵⁰, genetic variants³⁴, biological and non-biological variables^46,51,52, we estimated the difference in DNAm between ASD and non-ASD siblings, and also the correlation between DNAm and gene expression conditionally on these confounding factors. Finally, we investigated biological pathways that were predicted to be significantly affected by the identified DNAm alterations.

Subjects

For the ITAN collection, thirteen centers were involved after approval by the Verona Hospital Ethical Review Board (study protocol AUT-SFK001, CE1419) and by the Ethical Review Committees. More than 800 individuals across 256 families were recruited and diagnosed with ASD with their consent or the parents’ consent⁵³. The diagnosis was performed by experienced child psychiatrists that followed the DSM IV⁵⁴ and used standard tools: Broader Phenotype Autism Symptom Scale (BPASS), Autism Diagnostic Interview-Revised (ADI-R), Autism Diagnostic Observation Schedule (ADOS), and Krug Asperger Disorder Index screening. Of the ITAN collection, we selected 76 families having two siblings discordant for ASD diagnosis, and the affected child between 4 and 18 years old. The total number of subjects was 152 (Supplementary Table 1).

DNAm profiling

DNA was extracted from peripheral blood samples using the Puregene Blood Kit (Gentra Systems, Minneapolis, MN, US), a modified salting-out precipitation method, following the manufacturer’s instructions. Each DNA sample was then checked for quality and quantity using NanoDrop ND 1000 spectrophotometer (Thermo Scientific, Wilmington, DE, US). Following sample randomization, DNA methylation quantification was performed at Life & Brain GmbH, Bonn, in two main steps. Firstly, DNA samples were submitted to bisulfite conversion using the EZ-96 DNA Methylation-Lightning^TM MagPrep protocol (Zymo Research, California, USA). This protocol consisted of adding the ready-to-use Lightning Conversion Reagent directly to the DNA coupled to a magnetic bead-based clean-up method. Secondly, the methylation profile of the converted DNA was determined across the human genome (hg19) using the Infinium HumanMethylationEPIC (“EPIC”) array by Illumina⁵⁵. The DNA methylation data that support the findings of this study are available from the ITAN Foundation (see https://www.fondazioneitan.org/en for details) upon submission of an official request. Data are released only for research purposes, upon assessment of a project proposal by the ITAN Scientific Committee.

DNA array genotyping and transcriptome analysis

DNA samples were genotyped by the Autism Sequencing Consortium⁵⁶ using Illumina GSA v2 arrays (~658,000 markers). Genotype data were called using the genotyping command line interface of Illumina array analysis platform. Transcriptome data were generated by Poly-A RNA sequencing on Illumina RNASeq Platform as previously described¹⁷.

EPIC array data processing

The analysis of the EPIC array data was developed from the pipeline described in the ChAMP R package⁵⁷. Raw IDAT files were imported and checked for quality using the minfi R package⁵⁸. The quality control steps included default filters for probes with a detection P-value > 0.01 in at least one sample, for probes with a bead count < 3 in at least 5% of samples, for probes that were not found in CpG islands, for SNP-related probes according to the general recommendations⁵⁹, for cross-reactive probes^60,61, for probes that aligned to multiple locations and finally, for probes located in chromosome X and Y. For each probe that passed the quality filters (i.e., CpG site), we calculated the Beta-value for each sample by taking the ratio between the number of methylated cytosines and the total number of cytosines at the specific CpG site. We then run BMIQ intra-array normalization⁶², which is an effective method for adjusting Beta-values for bias introduced by the Infinium type 2 probe design⁶³. We visualized the similarity of samples based on the normalized Beta-values of the 1,000 most variable probes using a multidimensional scaling plot, and we removed outlier families from downstream analyses if the family’s metadata were not collected in the same way as for the other families.

The contribution of batch effects to DNAm variation was determined using singular value decomposition (SVD⁶⁴), and we used ComBat⁶⁵ to correct the Beta-values for technical features that showed a significant association (P-value < 0.05) with the first 20 principal components of DNAm variation. These batch effects were slide, plate, and array number, and we ensured that the biological variable of interest (ASD vs. non-ASD) was not confounded with these technical variables^66-68. We also applied control probe adjustment⁶⁹ to further reduce the effects of technical biases on the methylation signal. This specific step involved the use of signal intensities for the EPIC array control probes which were representative of the efficiency of bisulfite conversion, and other aspects related to the chemistry of methylation quantification. We performed a principal component analysis in R⁷⁰ of these control probe intensities to deal with the high correlation between them, and then we determined whether there were significant batch effects of these principal components using SVD.

Peripheral cell-type composition

We calculated cell-type compositions for each sample from the Beta-values using the RefbaseEWAS method implemented in ChAMP⁷¹. We first assessed whether there was a significant difference in cell-type compositions between ASD and non-ASD subjects using Dirichlet regression in R. Then, we extracted the variation in cell-type compositions that was not partly explained by the diagnosis (i.e. the residuals of the Dirichlet regression), and we used these residuals of cell-type compositions as covariates of a linear model to examine changes in DNAm that were not driven by cell-type heterogeneity (see below).

Differential methylation

After filtering probes and samples for quality and correcting Beta-values for batch effects and cell-type compositions, the next step of the DNAm pipeline was to identify differentially methylated probes (DMPs) using the limma R package^72,73 implemented in ChAMP. For each probe, a linear model was fitted to the distribution of Beta-values across samples, with age, sex, family, and diagnosis as covariates. By including family in the linear model, we also expected to capture most of the variation explained by ethnicity because, for most samples, ethnicity was the same within families and different between families. A moderated gene-wise variance was then computed based on the empirical Bayes method⁷³ to test the null hypothesis that a difference in fitted values between ASD and non-ASD subjects was equal to zero. We called a CpG site a DMP if such a difference had a Benjamini-Hochberg (BH) corrected P-value < 0.05.

To address the interactions between neighboring CpG sites and the net effect that these sites have on associated gene(s), we determined the methylation status of clusters of probes by running the DMRcate algorithm⁷⁴ built in ChAMP. This algorithm identified differentially methylated regions (DMRs) between ASD and non-ASD subjects by applying a Gaussian smoothing to the moderated statistics of the limma output using windows of 1,000 bp (default setting). DMRcate does not require a priori annotations of the genome for calling DMRs, and it does not combine genomically nearby CpG sites based on the direction of DNAm. Because of these two features of the algorithm, we were able firstly to study genomic regions that either had (e.g., promoter) or did not have (e.g., intergenic) explicit gene associations and, secondly, to analyze regulatory regions that showed hypermethylated and hypomethylated probes in ASD cases compared to non-ASD siblings (or vice versa). We used a quantile-quantile (Q–Q) plot between observed DMP/DMR P-values and expected P values under a uniform distribution of range [0,1] to check for spurious estimated P-values.

Gene set enrichment

Both DMP and DMR datasets were analyzed for enriched gene sets to find biological functions significantly affected by differential DNAm between ASD and non-ASD subjects. We performed gene set enrichment analysis (GSEA) using bioinformatic tools that were developed explicitly for either DMPs or DMRs and that allowed us to correct gene sets P-values for the different numbers of CpGs between genes (i.e., probe number bias⁷⁵).

For DMPs, GSEA was performed using functional class scoring, which is an approach implemented in the methylGSA R package⁷⁶. Compared to other GSEA methods such as over-representation analysis, functional class scoring allowed us to rank all CpGs and not only DMPs by their P-values and, thus, to use the entire list of CpG-associated genes for each gene set to determine which biological functions were significantly influenced by differential DNAm between ASD and non-ASD subjects. Gene sets and the corresponding biological functions were extracted from widely used databases (Gene Ontology, KEGG, and Reactome) and also from specialized databases which were composed of genes implicated in psychiatric disorders (Gandal et al.⁷⁷ and SFARI Gene database⁷⁸), genes highly expressed in the human brain (The Human Protein Atlas⁷⁹) and finally, genes involved in synapse functions (SynGO⁸⁰).

For DMRs, we used an empirical Bayes GSEA method implemented in ChAMP⁸¹ that has been developed to overcome the probe number bias and to avoid using only CpGs below an arbitrary significance threshold⁸². All CpGs of each DMR were mapped to genes, and these genes were ranked by their overall level of differential DNAm. This list of ranked genes was then examined for enriched biological pathways which were obtained from widely used databases (Gene Ontology, KEGG, and Reactome).

Integration with gene expression and genotype data

To examine the potential effects that differential DNAm may have on transcription activity and gene regulation, we integrated the DNAm profile with gene expression data from the same set of samples¹⁷ using the ELMER R package (version 2.8.3)⁸³. This type of analysis was performed in supervised mode. From the list of DMPs generated using limma (see above), distal EPIC probes (at least 2 Kb far from transcriptional start sites) were kept for the integration with gene expression data if the DNAm difference at these probes was |0.03| between ASD and non-ASD subjects. Each differentially methylated distal probe was then paired with the closest ten upstream genes and the closest ten downstream genes. For each probe-gene pair, we computed the inverse correlation between the DNAm of the probe, and the expression of the gene and further analyzed the difference in gene expression between ASD and non-ASD subjects using the Mann-Whitney U test. We used the Benjamini-Hochberg corrected P-values for detecting probe-gene pairs that may be potentially associated with ASD diagnosis.

The correlation between DNAm and gene expression may also depend on genetic components. To understand potential genetic effects on DNAm and gene expression, we used the genotype data of our subjects and integrated them with published data of independent DNAm quantitative trait locus (mQTL)³⁴ and of cis-expression quantitative trait loci (eQTL)⁸⁴. First, we searched for cis-eQTL of the gene which was significantly associated with the differentially methylated distal probe. Then, we checked if such a genetic variant was also a mQTL. If a given SNP was a mQTL and corresponded to our distal probe, we estimated its effect on DNAm using a beta regression model⁸⁵ where the independent variables were the diagnosis and the genotype of the subjects multiplied by the Beta-value of the mQTL. Each linear model included two different top-ranked mQTL which were estimated to be in linkage equilibrium using LDlink⁸⁶.

Difference in cell-type composition

We estimated per-sample cell-type proportions from DNAm data and analyzed these compositional data using Dirichlet regression to determine whether there was a difference between ASD and non-ASD subjects. We found no significant difference in estimated cell-type compositions, except for NK cells where the proportion was significantly lower in ASD subjects compared to non-ASD subjects (ASD = 0.01, non-ASD = 0.04, P-value < 0.001) (Fig. 1; Supplementary Table 2). These differences in cell-type compositions were consistent with the results of a previous study on ASD in which the same subjects were analyzed using gene expression data¹⁷. Even though we performed a DNAm-based cell-type deconvolution method, our estimated proportions were correlated (Pearson’s r > 0.25) with the proportions that were calculated in the previous study where it was used an RNA-based deconvolution algorithm (Supplementary Fig. 1).

Identification of DMPs and DMRs

We examined EPIC methylation arrays of 76 discordant autistic sibling pairs to understand the role of epigenetics in ASD. We identified two outlier families using a multidimensional scaling plot based on DNAm Beta-values (Supplementary Fig. 2), but we discarded only one outlier family in which the sampling was not performed on the same date for both siblings; in all the other families, each pair was sampled on the same date. After removing this outlier, the methylation profile of the 75 families was overall homogeneous, and largely shared between discordant siblings of the same family (Supplementary Fig. 3). Family was, in fact, one of the factors that resulted to be significantly associated with the first 20 principal components of the SVD analysis. The other factors that explained a significant variation in Beta-values were either involved with the array processing (e.g., slide and control probes intensities) or with the heterogeneity of cell types (e.g., granulocytes and B cells), and no significant variation was explained by the diagnosis (Supplementary Fig. 4). However, after fitting a linear model to each probe’s Beta-values to correct for family, age, sex, batch effects, control probes intensities, and cell-type compositions, we were able to capture the portion of DNAm variation that was significantly associated with the diagnosis (Supplementary Fig. 4).

For the differential methylation analysis between ASD and non-ASD siblings, we used the adjusted Beta-values and we identified 37,643 DMPs across the genome with a BH corrected P-value < 0.05 and a range of Beta-values difference between -0.05 and 0.05 (Supplementary File 1; for the Q–Q plot, see Supplementary Fig. 5). The data on single DMPs may not accurately reflect the interactions between neighboring CpG sites and the net effect that these sites have on associated gene(s). To overcome these limitations, we ran the DMRcate algorithm⁷⁴ and identified 418 DMRs with Stouffer’s P-value < 0.05 that were located genome-wide, as expected from the distribution of DMPs (Supplementary File 2). Of these DMPs and DMRs, the top-ranked sites were determined to potentially affect genes that have been reported to be involved in neurodevelopmental diseases such as TBX1⁸⁷, SHANK2⁸⁸, and TTC23⁸⁹ (Fig. 2).

Enriched biological processes in ASD

We performed GSEA to understand which biological processes may have been altered by differential methylation between ASD and non-ASD subjects. We extracted biological processes from a total of seven databases (Gene Ontology, KEGG, Reactome, Gandal et al.⁷⁷, SFARI Gene⁷⁸, The Human Protein Atlas⁷⁹, and SynGO⁸⁰), and identified 457 and 2,305 processes that were significantly represented (FDR < 0.05) by the list of genes associated with DMPs and DMRs, respectively (Supplementary File 3; Supplementary File 4). Of the top 1% significantly enriched processes with the highest Normalized Enrichment Score, two processes involved genes associated with synapse disassembly and structure, three processes were associated with neurogenesis, and one process involved learning genes. Finally, we found a significant enrichment for SFARI ASD risk genes (Fig. 3).

Correlation between DNAm and gene expression

Differential methylation between different biological conditions may contribute to phenotypic differences through changes in the underlying gene expression. Here, we investigated whether ASD and non-ASD siblings showed genes or genomic regions that differed in the joint pattern of DNAm and gene expression using ELMER analysis in supervised mode. We identified 346,224 probes on EPIC arrays that were at least 2 kb away from a transcriptional start site. Six of these distal probes were hypermethylated and ten were hypomethylated in ASD subjects with FDR < 0.05 and Beta-values difference > |0.03| (Supplementary Fig. 6). We then identified putative target genes for these differentially methylated distal probes and found a significant association for three of the hypermethylated probe-gene pairs and four of the hypomethylated probe-gene pairs (Table 1). Notably, we identified significant associations for CLEC11A and MOB1A, genes known to be associated with immune and mental disorders^90,91.

Correlated DNAm and gene expression changes may be driven by genetic variants influencing DNAm at the nearby target gene. We identified two SNPs as candidate genetic variants for such an impact on DNAm of CLEC11A and MOB1A. We investigated the effects of the SNPs genotype on the estimated differential DNAm between ASD and non-ASD subjects, but only for MOB1A the genotype contribution was strong enough to remove the differential DNAm signal (Supplementary Table 3). For CLEC11A, we found that differential DNAm was not lost when taking into account the presence of a mQTL. This strong signal for CLEC11A was supported by both the DMR analysis in which the genomic region involving CLEC11A was characterized as DMR (Fig. 2; Supplementary File 2), and the estimated correlation between the median DNAm across the four DMPs at 200-1500 bases upstream of CLEC11A transcriptional start site and CLEC11A expression (Pearson’s r = -0.39, P-value < 0.001; Fig. 4). Although the difference in DNAm of CLEC11A between ASD and non-ASD siblings was significant, gene expression was only marginally different between the two groups (Fig.4).

Identifying robust biomarkers for ASD has been a challenging task. This group of neurodevelopmental disorders is polygenic, phenotypically heterogeneous, and influenced by both genetic and environmental factors^92-95. As there is no single common genetic variant showing a strong predictive value for ASD diagnosis^96,97, candidate biomarkers have been searched for outside the protein-coding sequences of genes such as in enhancers and intergenic regions. In these regulatory regions, biomarkers can be found by detecting changes in DNA methylation (DNAm) that may potentially affect gene functions^22-26. However, although the contribution of DNAm to the risk of ASD has been acknowledged, questions remain about the potential of this type of epigenetic biomarkers to help in the development of diagnostic and therapeutic approaches to ASD.

Here, we analyzed the methylation profile of whole blood samples from 75 sibling pairs with a discordant ASD diagnosis. We aimed to analyze DNAm in subjects with ASD and detect dysregulated genomic regions that may be involved in the etiology of the disorder. In addition, we investigated possible differences in cell-type proportions between ASD and non-ASD siblings that might have been involved in the pathophysiology of the disorder.

In our study, we analyzed the methylation profile of a subset of the ITAN collection, the same subset previously examined at the transcription level¹⁷. We estimated cell-type proportions using DNAm array data of whole blood mixtures and showed a reduced fraction of NK cells in ASD siblings. A similar reduction in NK cells was also found using transcriptome data of the same cohort¹⁷, consistent with previous investigations reporting either a decrease of NK cells⁹⁸ or a reduction of their activity^99-102. NK cells act against viral infections, representing an important defensive mechanism of the innate immune system¹⁰³, and their decreased level or activity would suggest a suboptimal function of the immune system in ASD. In ASD children, immune pathways are typically dysregulated, and the expression of genes that are involved in the immune response has been found to be correlated with changes of DNAm in their regulatory regions¹⁰⁴. For example, Nardone et al. ⁴⁶ found that hypomethylated sites were associated with the overexpression of transcription factors regulating the development of the microglia. It has also been reported that the immune system of mothers of ASD cases responded slowly to infections due to DNAm alterations at regulatory regions of immune genes¹⁰⁵. Such DNAm alterations may be inherited, potentially contributing to the risk of ASD in children of immunocompromised mothers. More recently, a genome-wide methylation analysis in the blood of ASD subject vs neurotypical controls has led to the identification of a putative epigenetically distinct subset of children driven by altered blood cell composition¹⁰⁶, further supporting the role of the immune systems in the pathophysiology of ASD.

Autism is a polygenic neurodevelopmental disorder and, besides immune genes, there are other genes that have been associated with ASD^5,6,8,11,19. By comparing the genome-wide methylation profile of ASD subjects versus their healthy sibling controls, we identified hundreds of genes that were differentially methylated after accounting for demographic data, batch effects, and cell-type composition. In particular, 37,643 genomic sites (DMPs) and 418 regions (DMRs) were differentially methylated between ASD and non-ASD siblings. We detected DNAm alterations in TBX1, SHANK2, and TTC23, genes that have been previously shown to be affected by epigenetic processes in ASD and other neurodevelopmental diseases^87-89. As expected, genes that have a significant contribution to the risk of ASD, such as TBX1, SHANK2, and TTC23, are regulated through DNAm, suggesting that the etiology of ASD can be better understood by also looking at DNAm alterations.

By conducting gene set enrichment analysis (GSEA), we showed that identified DMPs were associated with biological processes related to synapse functions and neurogenesis. Although neurodevelopmental pathways are preferably studied using brain tissues, our results support the potential for identifying epigenetic biomarkers of neurodevelopment even in peripheral tissues^47,48.

Differences in cellular composition, DNAm, and function of neurodevelopmental pathways are expected to be associated with differences in gene expression between affected subjects and controls. Ultimately, the association between multiple mechanisms may strongly depend on genetic variants that, for example, are responsible for differential DNAm and the regulation of gene activity¹⁰⁷. We integrated the methylation Beta-values of DMPs of our samples with the corresponding gene expression and genotype data to understand the potential relationship between genetic and non-genetic components in ASD. We first calculated the correlation between gene expression and DNAm, identifying a significant and negative correlation between DNAm and expression at the DMR that was mapped to CLEC11A. We then combined genotype data from the same samples and methylation eQTL data and showed that the correlation was not driven by genotype effects of candidate SNPs located in the same region.

In other studies, the region of CLEC11A has been identified as a candidate locus for the etiology of neurodevelopmental disorders due to its position nearby SHANK1, a gene strongly associated with ASD⁹¹. SHANK family genes encode postsynaptic proteins that are needed for functional electrochemical communication at the level of synapses, and mutations in any of the three genes (SHANK1, SHANK2, and SHANK3) have been reported to disrupt neuronal activity^91,108. Different SHANK mutations have been found in ASD individuals, including a deletion covering CLEC11A and SHANK1 in males with ASD^91,109,110. In addition, differential DNAm between ASD cases and controls has been determined at the regulatory regions of SHANK1 and SHANK3^111,112. We could not detect DNAm changes that were specifically associated with SHANK1 nor SHANK3 because these biomarkers are absent in the blood, but we did find a DMR associated with the SHANK2 gene which is instead also expressed in the blood¹¹³.

Compared to studies based on brain tissues, our analysis using whole blood samples was limited in its power to capture neurodevelopmental genes and pathways, as we could only examine those that were not exclusively regulated in the brain. Still, we found differentially methylated sites in the blood that were associated with genes involved in neurodevelopment and ASD. We are aware that for some of these candidate genes differential DNAm may reflect a correlation between ASD diagnosis and some other variables that were not measured in our study, and that further investigation would be required to validate our findings. Nevertheless, our results provide further support for the possibility of using peripheral tissues to identify candidate epigenetic ASD biomarkers through integrative analyses across genomic data, as in the case of the CLEC11A-SHANK1 region.

Acknowledgments

The authors would like to thank all family members who provided their time and samples that made this investigation possible. We would like to acknowledge the Scientific Committee of the Italian Autism Network - Fondazione ITAN) for continuous support, and Mike Talkowski and the Autism Sequencing Consortium for genotyping the DNA samples. This work was supported by a grant from the Fondazione Italiana Autismo (FIA-2018/53). This article is dedicated to the memory of our friend and colleague Alessandro Zuddas, whose contribution to the development of the Italian Autism Network is deeply acknowledged.

Conflict of Interest

The authors declare no conflict of interest.

The Italian Autism Network (Fondazione ITAN)

Giovanni Alibrio⁴, Ilaria Basadonne⁵, Arianna Benvenuto⁶, Serafino Buono⁷, Carmela Bravaccio⁸, Carlo Casonato⁹, Elisa Ceppi¹⁰, Paolo Curatolo⁶, Bernardo Dalla Bernardina¹¹, Lucio Da Ros¹², Francesca Darra¹¹, Anna Eusebi⁴, Alessandra Gabellone¹³, Andrea De Giacomo¹⁴, Grazia Distefano¹⁵, Enrico Domenici^1,3, Federica Donno¹⁶, Maurizio Elia⁷, Elisa Fazzi^4,17, Michela Gatta¹⁸, Stefania Giusto¹⁵, Serenella Grittani¹⁹, Evamaria Lanzarini²⁰, Giovanni Malerba²¹, Elisa Mani¹⁰, Barbara Manzi⁶, Lucia Margari¹³, Lucia Marzulli¹³, Gabriele Masi²², Paola Mattei⁴, Luigi Mazzone⁶, Massimo Molteni¹⁰, Pierandrea Muglia²³, Sebastiano Musumeci⁷, Antonio Narzisi²², Antonio Pascotto²⁴, Cinzia Pari¹⁹, Antonia Parmeggiani²⁵, Maria Giuseppina Petruzzelli¹³, Alessia Raffagnato¹⁸, Emiliangelo Ratti²⁶, Maria Paola Rossaro¹⁸, Maria Pia Riccio⁸, Paolo Rizzini¹², Renato Scifo¹⁵, Martina Siracusano⁶, Raffaella Tancredi²², Alessandra Tiberti⁴, Elisabetta Trabetti²¹, Annalisa Traverso¹⁸, Paola Venuti⁵, Leonardo Zoccante²⁷ & Alessandro Zuddas¹⁶

⁴Unit of Child Neurology and Psychiatry, ASST Spedali Civili of Brescia, Brescia, Italy

⁵Department of Psychology and Cognitive Sciences, University of Trento, Rovereto, Italy

⁶Child Neurology and Psychiatry Unit, Tor Vergata University of Rome, Rome, Italy

⁷ Oasi Research Institute - IRCCS, Troina, Italy

⁸Department of Translational Medical Sciences, Federico II University, Naples, Italy

⁹ Faculty of Law, University of Trento, Trento, Italy

¹⁰ Child Psychopathology Unit, Scientific Institute, IRCCS Eugenio Medea, Bosisio Parini, LC, Italy

¹¹ Child Neuropsychiatry, Department of Surgical Sciences, Dentistry, Gynecology and Pediatrics, University of Verona, Verona, Italy

¹²Fondazione Smith Kline, Verona, Italy

¹³ Child Neuropsychiatry Complex Operative Unit, University of Bari "Aldo Moro”, Bari, Italy

¹⁴ Department of Basic Medical Sciences, Neuroscience and Sense Organs, University of Bari Aldo Moro, Bari, Italy

¹⁵Center for Autism Spectrum Disorders, Child Psychiatry Unit, Provincial Health Service of Catania (ASP CT), Catania, Italy

¹⁶ Child and Adolescent Psychiatry Unit, Department of Biomedical Sciences, University of Cagliari, Cagliari, Italy

¹⁷Department of Clinical and Experimental Sciences, University of Brescia, Brescia, Italy

¹⁸ Children and Adolescents Neuropsychiatry Unit, Woman and Child's Health Department, Padova University Hospital, Padova, Italy

¹⁹ Center for Autism Spectrum Disorder - Child and Adolescent Neuropsychiatry Unit, Rimini - Romagna Health Department, Rimini, Italy

²⁰ Child and Adolescent Neuropsychiatry Unit, Infermi Hospital, Rimini, Italy

²¹Department of Neurosciences, Biomedicine and Movement Sciences, Section of Biology and Genetics, University of Verona, Verona, Italy

²²IRCCS Stella Maris Foundation, Pisa, Italy

²³ GRIN Therapeutics Inc. New York, New York, USA.

²⁴ Department of Mental and Physical Health and Preventive Medicine, University of Campania, Naples, Italy

²⁵ Child Neurology and Psychiatry Unit, IRCCS ISNB, S. Orsola-Malpighi Hospital, Department of Medical and Surgical Sciences, University of Bologna, Bologna, Italy

²⁶ Centro Ricerche Cliniche di Verona, Integrated University Hospital Verona, Verona, Italy

²⁷Child and Adolescent Neuropsychiatry Unit, Maternal-Child Integrated Care Department, Integrated University Hospital Verona, Verona, Italy

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Table 1 Probe-gene pair association using ELMER. Hypermethylated (left) and hypomethylated (right) distal probes in ASD subjects and putative target genes.

Hypermethylated	Gene	Distance	FDR
cg17729891	C19orf48	193401	0.01
cg20315590	TSEN15	-1959694	0.01
cg17729891	CLEC11A	119026	0.04

Hypomethylated	Gene	Distance	FDR
cg10841563	GNAQ	-2633643	0.01
cg10841563	KIF27	3171593	0.01
cg10841563	HNRNPK	3302978	0.01
cg05890377	MOB1A	21940	0.03

The authors have declared there is NO conflict of interest to disclose

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Candidate biomarkers from the integration of methylation and gene expression in discordant autistic sibling pairs

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Introduction

Subjects And Methods

Subjects

DNAm profiling

DNA array genotyping and transcriptome analysis

EPIC array data processing

Peripheral cell-type composition

Differential methylation

Gene set enrichment

Integration with gene expression and genotype data

Results

Difference in cell-type composition

Identification of DMPs and DMRs

Enriched biological processes in ASD

Correlation between DNAm and gene expression

Discussion

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References

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Supplementary Files

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