Developmentally dynamic changes in DNA methylation in the human pancreas.

Development of the human pancreas requires the precise temporal control of gene expression via epigenetic mechanisms and the binding of key transcription factors. We quantified genome-wide patterns of DNA methylation in human fetal pancreatic samples from donors aged 6 to 21 post-conception weeks. We found dramatic changes in DNA methylation across pancreas development, with > 21% of sites characterized as developmental differentially methylated positions (dDMPs) including many annotated to genes associated with monogenic diabetes. An analysis of DNA methylation in postnatal pancreas tissue showed that the dramatic temporal changes in DNA methylation occurring in the developing pancreas are largely limited to the prenatal period. Significant differences in DNA methylation were observed between males and females at a number of autosomal sites, with a small proportion of sites showing sex-specific DNA methylation trajectories across pancreas development. Pancreas dDMPs were not distributed equally across the genome and were depleted in regulatory domains characterized by open chromatin and the binding of known pancreatic development transcription factors. Finally, we compared our pancreas dDMPs to previous findings from the human brain, identifying evidence for tissue-specific developmental changes in DNA methylation. This study represents the first systematic exploration of DNA methylation patterns during human fetal pancreas development and confirms the prenatal period as a time of major epigenomic plasticity.

Brain cell-type shifts in Alzheimer's disease, autism, and schizophrenia interrogated using methylomics and genetics.

Few neuropsychiatric disorders have replicable biomarkers, prompting high-resolution and large-scale molecular studies. However, we still lack consensus on a more foundational question: whether quantitative shifts in cell types-the functional unit of life-contribute to neuropsychiatric disorders. Leveraging advances in human brain single-cell methylomics, we deconvolve seven major cell types using bulk DNA methylation profiling across 1270 postmortem brains, including from individuals diagnosed with Alzheimer's disease, schizophrenia, and autism. We observe and replicate cell-type compositional shifts for Alzheimer's disease (endothelial cell loss), autism (increased microglia), and schizophrenia (decreased oligodendrocytes), and find age- and sex-related changes. Multiple layers of evidence indicate that endothelial cell loss contributes to Alzheimer's disease, with comparable effect size to APOE genotype among older people. Genome-wide association identified five genetic loci related to cell-type composition, involving plausible genes for the neurovascular unit (P2RX5 and TRPV3) and excitatory neurons (DPY30 and MEMO1). These results implicate specific cell-type shifts in the pathophysiology of neuropsychiatric disorders.

Meta-analysis of epigenetic aging in schizophrenia reveals multifaceted relationships with age, sex, illness duration, and polygenic risk.

BACKGROUND: The study of biological age acceleration may help identify at-risk individuals and reduce the rising global burden of age-related diseases. Using DNA methylation (DNAm) clocks, we investigated biological aging in schizophrenia (SCZ), a mental illness that is associated with an increased prevalence of age-related disabilities and morbidities. In a whole blood DNAm sample of 1090 SCZ cases and 1206 controls across four European cohorts, we performed a meta-analysis of differential aging using three DNAm clocks (i.e., Hannum, Horvath, and Levine). To dissect how DNAm aging contributes to SCZ, we integrated information on duration of illness and SCZ polygenic risk, as well as stratified our analyses by chronological age and biological sex. RESULTS: We found that blood-based DNAm aging is significantly altered in SCZ independent from duration of the illness since onset. We observed sex-specific and nonlinear age effects that differed between clocks and point to possible distinct age windows of altered aging in SCZ. Most notably, intrinsic cellular age (Horvath clock) is decelerated in SCZ cases in young adulthood, while phenotypic age (Levine clock) is accelerated in later adulthood compared to controls. Accelerated phenotypic aging was most pronounced in women with SCZ carrying a high polygenic burden with an age acceleration of + 3.82 years (CI 2.02-5.61, P = 1.1E-03). Phenotypic aging and SCZ polygenic risk contributed additively to the illness and together explained up to 14.38% of the variance in disease status. CONCLUSIONS: Our study contributes to the growing body of evidence of altered DNAm aging in SCZ and points to intrinsic age deceleration in younger adulthood and phenotypic age acceleration in later adulthood in SCZ. Since increased phenotypic age is associated with increased risk of all-cause mortality, our findings indicate that specific and identifiable patient groups are at increased mortality risk as measured by the Levine clock. Our study did not find that DNAm aging could be explained by the duration of illness of patients, but we did observe age- and sex-specific effects that warrant further investigation. Finally, our results show that combining genetic and epigenetic predictors can improve predictions of disease outcomes and may help with disease management in schizophrenia.

Quantifying the proportion of different cell types in the human cortex using DNA methylation profiles.

BACKGROUND: Due to interindividual variation in the cellular composition of the human cortex, it is essential that covariates that capture these differences are included in epigenome-wide association studies using bulk tissue. As experimentally derived cell counts are often unavailable, computational solutions have been adopted to estimate the proportion of different cell types using DNA methylation data. Here, we validate and profile the use of an expanded reference DNA methylation dataset incorporating two neuronal and three glial cell subtypes for quantifying the cellular composition of the human cortex. RESULTS: We tested eight reference panels containing different combinations of neuronal- and glial cell types and characterised their performance in deconvoluting cell proportions from computationally reconstructed or empirically derived human cortex DNA methylation data. Our analyses demonstrate that while these novel brain deconvolution models produce accurate estimates of cellular proportions from profiles generated on postnatal human cortex samples, they are not appropriate for the use in prenatal cortex or cerebellum tissue samples. Applying our models to an extensive collection of empirical datasets, we show that glial cells are twice as abundant as neuronal cells in the human cortex and identify significant associations between increased Alzheimer's disease neuropathology and the proportion of specific cell types including a decrease in NeuNNeg/SOX10Neg nuclei and an increase of NeuNNeg/SOX10Pos nuclei. CONCLUSIONS: Our novel deconvolution models produce accurate estimates for cell proportions in the human cortex. These models are available as a resource to the community enabling the control of cellular heterogeneity in epigenetic studies of brain disorders performed on bulk cortex tissue.

Accelerated Pace of Aging in Schizophrenia: Five Case-Control Studies.

BACKGROUND: Schizophrenia is associated with increased risk of developing multiple aging-related diseases, including metabolic, respiratory, and cardiovascular diseases, and Alzheimer's and related dementias, leading to the hypothesis that schizophrenia is accompanied by accelerated biological aging. This has been difficult to test because there is no widely accepted measure of biological aging. Epigenetic clocks are promising algorithms that are used to calculate biological age on the basis of information from combined cytosine-phosphate-guanine sites (CpGs) across the genome, but they have yielded inconsistent and often negative results about the association between schizophrenia and accelerated aging. Here, we tested the schizophrenia-aging hypothesis using a DNA methylation measure that is uniquely designed to predict an individual's rate of aging. METHODS: We brought together 5 case-control datasets to calculate DunedinPACE (Pace of Aging Calculated from the Epigenome), a new measure trained on longitudinal data to detect differences between people in their pace of aging over time. Data were available from 1812 psychosis cases (schizophrenia or first-episode psychosis) and 1753 controls. Mean chronological age was 38.9 (SD = 13.6) years. RESULTS: We observed consistent associations across datasets between schizophrenia and accelerated aging as measured by DunedinPACE. These associations were not attributable to tobacco smoking or clozapine medication. CONCLUSIONS: Schizophrenia is accompanied by accelerated biological aging by midlife. This may explain the wide-ranging risk among people with schizophrenia for developing multiple different age-related physical diseases, including metabolic, respiratory, and cardiovascular diseases, and dementia. Measures of biological aging could prove valuable for assessing patients' risk for physical and cognitive decline and for evaluating intervention effectiveness.

Whole blood DNA methylation changes are associated with anti-TNF drug concentration in patients with Crohn's disease.

BACKGROUND AND AIMS: Anti-TNF treatment failure in patients with inflammatory bowel disease (IBD) is common and frequently related to low drug concentrations. In order to identify patients who may benefit from dose optimisation at the outset of anti-TNF therapy, we sought to define epigenetic biomarkers in whole blood at baseline associated with anti-TNF drug concentrations at week 14. METHODS: DNA methylation from 1,104 whole blood samples from 385 patients in the Personalised Anti-TNF Therapy in Crohn's disease (PANTS) study were assessed using the Illumina EPIC Beadchip (v1.0) at baseline, weeks 14, 30 and 54. We compared DNA methylation profiles in anti-TNF-treated patients who experienced primary non-response at week 14 and if they were assessed at subsequent time points, were not in remission at week 30 or 54 (infliximab n = 99, adalimumab n = 94), with patients who responded at week 14 and when assessed at subsequent time points, were in remission at week 30 or 54 (infliximab n = 99, adalimumab n = 93). RESULTS: Overall, between baseline and week 14, we observed 4,999 differentially methylated probes (DMPs) annotated to 2376 genes following anti-TNF treatment. Pathway analysis identified 108 significant gene ontology terms enriched in biological processes related to immune system processes and responses.Epigenome-wide association (EWAS) analysis identified 323 DMPs annotated to 210 genes at baseline associated with higher anti-TNF drug concentrations at week 14. Of these, 125 DMPs demonstrated shared associations with other common traits (proportion of shared CpGs compared to DMPs) including body mass index (23.2%), followed by CRP (11.5%), smoking (7.4%), alcohol consumption per day (7.1%) and IBD type (6.8%). EWAS of primary non-response to anti-TNF identified 20 DMPs that were associated with both anti-TNF drug concentration and primary non-response to anti-TNF with a strong correlation of the coefficients (Spearman's rho = -0.94, p < 0.001). CONCLUSION: Baseline DNA methylation profiles may be used as a predictor for anti-TNF drug concentration at week 14 to identify patients who may benefit from dose optimisation at the outset of anti-TNF therapy.

Genetic impacts on DNA methylation help elucidate regulatory genomic processes.

BACKGROUND: Pinpointing genetic impacts on DNA methylation can improve our understanding of pathways that underlie gene regulation and disease risk. RESULTS: We report heritability and methylation quantitative trait locus (meQTL) analysis at 724,499 CpGs profiled with the Illumina Infinium MethylationEPIC array in 2358 blood samples from three UK cohorts. Methylation levels at 34.2% of CpGs are affected by SNPs, and 98% of effects are cis-acting or within 1 Mbp of the tested CpG. Our results are consistent with meQTL analyses based on the former Illumina Infinium HumanMethylation450 array. Both SNPs and CpGs with meQTLs are overrepresented in enhancers, which have improved coverage on this platform compared to previous approaches. Co-localisation analyses across genetic effects on DNA methylation and 56 human traits identify 1520 co-localisations across 1325 unique CpGs and 34 phenotypes, including in disease-relevant genes, such as USP1 and DOCK7 (total cholesterol levels), and ICOSLG (inflammatory bowel disease). Enrichment analysis of meQTLs and integration with expression QTLs give insights into mechanisms underlying cis-meQTLs (e.g. through disruption of transcription factor binding sites for CTCF and SMC3) and trans-meQTLs (e.g. through regulating the expression of ACD and SENP7 which can modulate DNA methylation at distal sites). CONCLUSIONS: Our findings improve the characterisation of the mechanisms underlying DNA methylation variability and are informative for prioritisation of GWAS variants for functional follow-ups. The MeQTL EPIC Database and viewer are available online at https://epicmeqtl.kcl.ac.uk .

A comparison of feature selection methodologies and learning algorithms in the development of a DNA methylation-based telomere length estimator.

BACKGROUND: The field of epigenomics holds great promise in understanding and treating disease with advances in machine learning (ML) and artificial intelligence being vitally important in this pursuit. Increasingly, research now utilises DNA methylation measures at cytosine-guanine dinucleotides (CpG) to detect disease and estimate biological traits such as aging. Given the challenge of high dimensionality of DNA methylation data, feature-selection techniques are commonly employed to reduce dimensionality and identify the most important subset of features. In this study, our aim was to test and compare a range of feature-selection methods and ML algorithms in the development of a novel DNA methylation-based telomere length (TL) estimator. We utilised both nested cross-validation and two independent test sets for the comparisons. RESULTS: We found that principal component analysis in advance of elastic net regression led to the overall best performing estimator when evaluated using a nested cross-validation analysis and two independent test cohorts. This approach achieved a correlation between estimated and actual TL of 0.295 (83.4% CI [0.201, 0.384]) on the EXTEND test data set. Contrastingly, the baseline model of elastic net regression with no prior feature reduction stage performed less well in general-suggesting a prior feature-selection stage may have important utility. A previously developed TL estimator, DNAmTL, achieved a correlation of 0.216 (83.4% CI [0.118, 0.310]) on the EXTEND data. Additionally, we observed that different DNA methylation-based TL estimators, which have few common CpGs, are associated with many of the same biological entities. CONCLUSIONS: The variance in performance across tested approaches shows that estimators are sensitive to data set heterogeneity and the development of an optimal DNA methylation-based estimator should benefit from the robust methodological approach used in this study. Moreover, our methodology which utilises a range of feature-selection approaches and ML algorithms could be applied to other biological markers and disease phenotypes, to examine their relationship with DNA methylation and predictive value.

Exploring the mediation of DNA methylation across the epigenome between childhood adversity and First Episode of Psychosis-findings from the EU-GEI study.

ABTRACT: Studies conducted in psychotic disorders have shown that DNA-methylation (DNAm) is sensitive to the impact of Childhood Adversity (CA). However, whether it mediates the association between CA and psychosis is yet to be explored. Epigenome wide association studies (EWAS) using the Illumina Infinium-Methylation EPIC array in peripheral blood tissue from 366 First-episode of psychosis and 517 healthy controls was performed. Adversity scores were created for abuse, neglect and composite adversity with the Childhood Trauma Questionnaire (CTQ). Regressions examining (I) CTQ scores with psychosis; (II) with DNAm EWAS level and (III) between DNAm and caseness, adjusted for a variety of confounders were conducted. Divide-Aggregate Composite-null Test for the composite null-hypothesis of no mediation effect was conducted. Enrichment analyses were conducted with missMethyl package and the KEGG database. Our results show that CA was associated with psychosis (Composite: OR = 1.68; p = <0.001; abuse: OR = 2.16; p < 0.001; neglect: OR = 2.27; p = <0.001). None of the CpG sites significantly mediated the adversity-psychosis association after Bonferroni correction (p < 8.1 × 10-8). However, 28, 34 and 29 differentially methylated probes associated with 21, 27, 20 genes passed a less stringent discovery threshold (p < 5 × 10-5) for composite, abuse and neglect respectively, with a lack of overlap between abuse and neglect. These included genes previously associated to psychosis in EWAS studies, such as PANK1, SPEG TBKBP1, TSNARE1 or H2R. Downstream gene ontology analyses did not reveal any biological pathways that survived false discovery rate correction. Although at a non-significant level, DNAm changes in genes previously associated with schizophrenia in EWAS studies may mediate the CA-psychosis association. These results and associated involved processes such as mitochondrial or histaminergic disfunction, immunity or neural signalling requires replication in well powered samples. The lack of overlap between mediating genes associated with abuse and neglect suggests differential biological trajectories linking CA subtypes and psychosis.

Cross-National and Cross-Generational Evidence That Educational Attainment May Slow the Pace of Aging in European-Descent Individuals.

OBJECTIVES: Individuals with more education are at lower risk of developing multiple, different age-related diseases than their less-educated peers. A reason for this might be that individuals with more education age slower. There are 2 complications in testing this hypothesis. First, there exists no definitive measure of biological aging. Second, shared genetic factors contribute toward both lower educational attainment and the development of age-related diseases. Here, we tested whether the protective effect of educational attainment was associated with the pace of aging after accounting for genetic factors. METHODS: We examined data from 5 studies together totaling almost 17,000 individuals with European ancestry born in different countries during different historical periods, ranging in age from 16 to 98 years old. To assess the pace of aging, we used DunedinPACE, a DNA methylation algorithm that reflects an individual's rate of aging and predicts age-related decline and Alzheimer's disease and related disorders. To assess genetic factors related to education, we created a polygenic score based on the results of a genome-wide association study of educational attainment. RESULTS: Across the 5 studies, and across the life span, higher educational attainment was associated with a slower pace of aging even after accounting for genetic factors (meta-analysis effect size = -0.20; 95% confidence interval [CI]: -0.30 to -0.10; p = .006). Further, this effect persisted after taking into account tobacco smoking (meta-analysis effect size = -0.13; 95% CI: -0.21 to -0.05; p = .01). DISCUSSION: These results indicate that higher levels of education have positive effects on the pace of aging, and that the benefits can be realized irrespective of individuals' genetics.

An overview of DNA methylation-derived trait score methods and applications.

Microarray technology has been used to measure genome-wide DNA methylation in thousands of individuals. These studies typically test the associations between individual DNA methylation sites ("probes") and complex traits or diseases. The results can be used to generate methylation profile scores (MPS) to predict outcomes in independent data sets. Although there are many parallels between MPS and polygenic (risk) scores (PGS), there are key differences. Here, we review motivations, methods, and applications of DNA methylation-based trait prediction, with a focus on common diseases. We contrast MPS with PGS, highlighting where assumptions made in genetic modeling may not hold in epigenetic data.

Uncertainty quantification of reference-based cellular deconvolution algorithms.

The majority of epigenetic epidemiology studies to date have generated genome-wide profiles from bulk tissues (e.g., whole blood) however these are vulnerable to confounding from variation in cellular composition. Proxies for cellular composition can be mathematically derived from the bulk tissue profiles using a deconvolution algorithm; however, there is no method to assess the validity of these estimates for a dataset where the true cellular proportions are unknown. In this study, we describe, validate and characterize a sample level accuracy metric for derived cellular heterogeneity variables. The CETYGO score captures the deviation between a sample's DNA methylation profile and its expected profile given the estimated cellular proportions and cell type reference profiles. We demonstrate that the CETYGO score consistently distinguishes inaccurate and incomplete deconvolutions when applied to reconstructed whole blood profiles. By applying our novel metric to >6,300 empirical whole blood profiles, we find that estimating accurate cellular composition is influenced by both technical and biological variation. In particular, we show that when using a common reference panel for whole blood, less accurate estimates are generated for females, neonates, older individuals and smokers. Our results highlight the utility of a metric to assess the accuracy of cellular deconvolution, and describe how it can enhance studies of DNA methylation that are reliant on statistical proxies for cellular heterogeneity. To facilitate incorporating our methodology into existing pipelines, we have made it freely available as an R package (https://github.com/ds420/CETYGO).

Social mobility across the lifecourse and DNA methylation age acceleration in adults in the UK.

Disadvantaged socio-economic position (SEP) is associated with greater biological age, relative to chronological age, measured by DNA methylation (positive 'age acceleration', AA). Social mobility has been proposed to ameliorate health inequalities. This study aimed to understand the association of social mobility with positive AA. Diagonal reference modelling and ordinary least square regression techniques were applied to explore social mobility and four measures of age acceleration (first-generation: 'Horvath', 'Hannum' and second-generation: 'Phenoage', DunedinPoAm) in n = 3140 participants of the UK Household Longitudinal Study. Disadvantaged SEP in early life is associated with positive AA for three (Hannum, Phenoage and DunedinPoAm) of the four measures examined while the second generation biomarkers are associated with SEP in adulthood (p < 0.01). Social mobility was associated with AA measured with Hannum only such that compared to no mobility, upward mobility was associated with greater age independently of origin and destination SEP. Compared to continuously advantaged groups, downward mobility was associated with positive Phenoage (1.06y [- 0.03, 2.14]) and DunedinPoAm assessed AA (0.96y [0.24, 1.68]). For these two measures, upward mobility was associated with negative AA (Phenoage, - 0.65y [- 1.30, - 0.002]; DunedinPoAm, - 0.96y [- 1.47, - 0.46]) compared to continually disadvantaged groups. While we find some support for three models of lifecourse epidemiology with early life as a sensitive period, SEP across the lifecourse and social mobility for age acceleration measured with DNA methylation, our findings suggest that disadvantaged SEP across the lifecourse is most consistently associated with positive AA.

DNA methylation signatures of Alzheimer's disease neuropathology in the cortex are primarily driven by variation in non-neuronal cell-types.

Alzheimer's disease (AD) is a chronic neurodegenerative disease characterized by the progressive accumulation of amyloid-beta and neurofibrillary tangles of tau in the neocortex. We profiled DNA methylation in two regions of the cortex from 631 donors, performing an epigenome-wide association study of multiple measures of AD neuropathology. We meta-analyzed our results with those from previous studies of DNA methylation in AD cortex (total n = 2013 donors), identifying 334 cortical differentially methylated positions (DMPs) associated with AD pathology including methylomic variation at loci not previously implicated in dementia. We subsequently profiled DNA methylation in NeuN+ (neuronal-enriched), SOX10+ (oligodendrocyte-enriched) and NeuN-/SOX10- (microglia- and astrocyte-enriched) nuclei, finding that the majority of DMPs identified in 'bulk' cortex tissue reflect DNA methylation differences occurring in non-neuronal cells. Our study highlights the power of utilizing multiple measures of neuropathology to identify epigenetic signatures of AD and the importance of characterizing disease-associated variation in purified cell-types.

Epigenome-wide association study of human frontal cortex identifies differential methylation in Lewy body pathology.

Parkinson's disease (PD) and dementia with Lewy bodies (DLB) are closely related progressive disorders with no available disease-modifying therapy, neuropathologically characterized by intraneuronal aggregates of misfolded α-synuclein. To explore the role of DNA methylation changes in PD and DLB pathogenesis, we performed an epigenome-wide association study (EWAS) of 322 postmortem frontal cortex samples and replicated results in an independent set of 200 donors. We report novel differentially methylated replicating loci associated with Braak Lewy body stage near TMCC2, SFMBT2, AKAP6 and PHYHIP. Differentially methylated probes were independent of known PD genetic risk alleles. Meta-analysis provided suggestive evidence for a differentially methylated locus within the chromosomal region affected by the PD-associated 22q11.2 deletion. Our findings elucidate novel disease pathways in PD and DLB and generate hypotheses for future molecular studies of Lewy body pathology.

Functional characterization of the schizophrenia associated gene AS3MT identifies a role in neuronal development.

Genome-wide association studies (GWAS) have identified multiple genomic regions associated with schizophrenia, although many variants reside in noncoding regions characterized by high linkage disequilibrium (LD) making the elucidation of molecular mechanisms challenging. A genomic region on chromosome 10q24 has been consistently associated with schizophrenia with risk attributed to the AS3MT gene. Although AS3MT is hypothesized to play a role in neuronal development and differentiation, work to fully understand the function of this gene has been limited. In this study we explored the function of AS3MT using a neuronal cell line (SH-SY5Y). We confirm previous findings of isoform specific expression of AS3MT during SH-SY5Y differentiation toward neuronal fates. Using CRISPR-Cas9 gene editing we generated AS3MT knockout SH-SY5Y cell lines and used RNA-seq to identify significant changes in gene expression in pathways associated with neuronal development, inflammation, extracellular matrix formation, and RNA processing, including dysregulation of other genes strongly implicated in schizophrenia. We did not observe any morphological changes in cell size and neurite length following neuronal differentiation and MAP2 immunocytochemistry. These results provide novel insights into the potential role of AS3MT in brain development and identify pathways through which genetic variation in this region may confer risk for schizophrenia.

Evaluation of nanopore sequencing for epigenetic epidemiology: a comparison with DNA methylation microarrays.

Most epigenetic epidemiology to date has utilized microarrays to identify positions in the genome where variation in DNA methylation is associated with environmental exposures or disease. However, these profile less than 3% of DNA methylation sites in the human genome, potentially missing affected loci and preventing the discovery of disrupted biological pathways. Third generation sequencing technologies, including Nanopore sequencing, have the potential to revolutionize the generation of epigenetic data, not only by providing genuine genome-wide coverage but profiling epigenetic modifications direct from native DNA. Here we assess the viability of using Nanopore sequencing for epidemiology by performing a comparison with DNA methylation quantified using the most comprehensive microarray available, the Illumina EPIC array. We implemented a CRISPR-Cas9 targeted sequencing approach in concert with Nanopore sequencing to profile DNA methylation in three genomic regions to attempt to rediscover genomic positions that existing technologies have shown are differentially methylated in tobacco smokers. Using Nanopore sequencing reads, DNA methylation was quantified at 1779 CpGs across three regions, providing a finer resolution of DNA methylation patterns compared to the EPIC array. The correlation of estimated levels of DNA methylation between platforms was high. Furthermore, we identified 12 CpGs where hypomethylation was significantly associated with smoking status, including 10 within the AHRR gene. In summary, Nanopore sequencing is a valid option for identifying genomic loci where large differences in DNAm are associated with a phenotype and has the potential to advance our understanding of the role differential methylation plays in the etiology of complex disease.

Methylome analysis of ALS patients and presymptomatic mutation carriers in blood cells.

Amyotrophic lateral sclerosis (ALS) is a fatal motoneuron disease with a monogenic cause in approximately 10% of cases. However, familial clustering of disease without inheritance in a Mendelian manner and the broad range of phenotypes suggest the presence of epigenetic mechanisms. Hence, we performed an epigenome-wide association study on sporadic, symptomatic and presymptomatic familial ALS cases with mutations in C9ORF72 and FUS and healthy controls studying DNA methylation in blood cells. We found differentially methylated DNA positions (DMPs) and regions embedding DMPs associated with either disease status, C9ORF72 or FUS mutation status. One DMP reached methylome-wide significance and is attributed to a region encoding a long non-coding RNA (LOC389247). Furthermore, we could demonstrate co-localization of DMPs with an ALS-associated GWAS region near the SCN7A/SCN9A and XIRP2 genes. Finally, a classifier model that predicts disease status (ALS, healthy) classified all but one presymptomatic mutation carrier as healthy, suggesting that the presence of ALS symptoms rather than the presence of ALS-associated genetic mutations is associated with blood cell DNA methylation.

Genome-wide study of DNA methylation shows alterations in metabolic, inflammatory, and cholesterol pathways in ALS.

Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease with an estimated heritability between 40 and 50%. DNA methylation patterns can serve as proxies of (past) exposures and disease progression, as well as providing a potential mechanism that mediates genetic or environmental risk. Here, we present a blood-based epigenome-wide association study meta-analysis in 9706 samples passing stringent quality control (6763 patients, 2943 controls). We identified a total of 45 differentially methylated positions (DMPs) annotated to 42 genes, which are enriched for pathways and traits related to metabolism, cholesterol biosynthesis, and immunity. We then tested 39 DNA methylation-based proxies of putative ALS risk factors and found that high-density lipoprotein cholesterol, body mass index, white blood cell proportions, and alcohol intake were independently associated with ALS. Integration of these results with our latest genome-wide association study showed that cholesterol biosynthesis was potentially causally related to ALS. Last, DNA methylation at several DMPs and blood cell proportion estimates derived from DNA methylation data were associated with survival rate in patients, suggesting that they might represent indicators of underlying disease processes potentially amenable to therapeutic interventions.