Cis-meQTL for cocaine use-associated DNA methylation in an HIV-positive cohort show pleiotropic effects on multiple traits

BACKGROUND

Cocaine use (CU) is associated with psychiatric and medical diseases. Little is known about the mechanisms of CU-related comorbidities. Findings from preclinical and clinical studies have suggested that CU is associated with aberrant DNA methylation (DNAm) that may be influenced by genetic variants [i.e., methylation quantitative trait loci (meQTLs)]. In this study, we mapped cis-meQTLs for CU-associated DNAm sites (CpGs) in an HIV-positive cohort (Ntotal = 811) and extended the meQTLs to multiple traits.

RESULTS

We conducted cis-meQTL analysis for 224 candidate CpGs selected for their association with CU in blood. We identified 7,101 significant meQTLs [false discovery rate (FDR) < 0.05], which mostly mapped to genes involved in immunological functions and were enriched in immune pathways. We followed up the meQTLs using phenome-wide association study and trait enrichment analyses, which revealed 9 significant traits. We tested for causal effects of CU on these 9 traits using Mendelian Randomization and found evidence that CU plays a causal role in increasing hypertension (p-value = 2.35E-08) and decreasing heel bone mineral density (p-value = 1.92E-19).

CONCLUSIONS

These findings suggest that genetic variants for CU-associated DNAm have pleiotropic effects on other relevant traits and provide new insights into the causal relationships between cocaine use and these complex traits.

DNA Methylation Description of Hippocampus, Cortex, Amygdala, and Blood of Drug-Resistant Temporal Lobe Epilepsy

Epigenetic changes such as DNA methylation were observed in drug-resistant temporal lobe epilepsy (DR-TLE), a disease that affects 25-30% of epilepsy patients. The main objective is to simultaneously describe DNA methylation patterns associated with DR-TLE in hippocampus, amygdala, surrounding cortex to the epileptogenic zone (SCEZ), and peripheral blood. An Illumina Infinium MethylationEPIC BeadChip array was performed in 19 DR-TLE patients and 10 postmortem non-epileptic controls. Overall, 32, 59, and 3210 differentially methylated probes (DMPs) were associated with DR-TLE in the hippocampus, amygdala, and SCEZ, respectively. These DMP-affected genes were involved in neurotrophic and calcium signaling in the hippocampus and voltage-gated channels in SCEZ, among others. One of the hippocampus DMPs (cg26834418 (CHORDC1)) showed a strong blood-brain correlation with BECon and IMAGE-CpG, suggesting that it could be a potential surrogate peripheral biomarker of DR-TLE. Moreover, in three of the top SCEZ's DMPs (SHANK3, SBF1, and MCF2L), methylation status was verified with methylation-specific qPCR. The differentially methylated CpGs were classified in DMRs: 2 in the hippocampus, 12 in the amygdala, and 531 in the SCEZ. We identified genes that had not been associated to DR-TLE so far such as TBX5, EXOC7, and WRHN. The area with more DMPs associated with DR-TLE was the SCEZ, some of them related to voltage-gated channels. The DMPs found in the amygdala were involved in inflammatory processes. We also found a potential surrogate peripheral biomarker of DR-TLE. Thus, these results provide new insights into epigenetic modifications involved in DR-TLE.

Decreased DNA Methylation of RGMA is Associated with Intracranial Hypertension After Severe Traumatic Brain Injury: An Exploratory Epigenome-Wide Association Study

BACKGROUND

Cerebral edema and intracranial hypertension are major contributors to unfavorable prognosis in traumatic brain injury (TBI). Local epigenetic changes, particularly in DNA methylation, may influence gene expression and thus host response/secondary injury after TBI. It remains unknown whether DNA methylation in the central nervous system is associated with cerebral edema severity or intracranial hypertension post TBI. We sought to identify epigenome-wide DNA methylation patterns associated with these forms of secondary injury after TBI.

METHODS

We obtained genome-wide DNA methylation profiles of DNA extracted from ventricular cerebrospinal fluid samples at three different postinjury time points from a prospective cohort of patients with severe TBI (n = 89 patients, 254 samples). Cerebral edema and intracranial pressure (ICP) measures were clustered to generate composite end points of cerebral edema and ICP severity. We performed an unbiased epigenome-wide association study (EWAS) to test associations between DNA methylation at 419,895 cytosine-phosphate-guanine (CpG) sites and cerebral edema/ICP severity categories. Given inflated p values, we conducted permutation tests for top CpG sites to filter out potential false discoveries.

RESULTS

Our data-driven hierarchical clustering across six cerebral edema and ICP measures identified two groups differing significantly in ICP based on the EWAS-identified CpG site cg22111818 in RGMA (Repulsive guidance molecule A, permutation p = 4.20 × 10-8). At 3-4 days post TBI, patients with severe intracranial hypertension had significantly lower levels of methylation at cg22111818.

CONCLUSIONS

We report a novel potential relationship between intracranial hypertension after TBI and an acute, nonsustained reduction in DNA methylation at cg22111818 in the RGMA gene. To our knowledge, this is the largest EWAS in severe TBI. Our findings are further strengthened by previous findings that RGMA modulates axonal repair in other central nervous system disorders, but a role in intracranial hypertension or TBI has not been previously identified. Additional work is warranted to validate and extend these findings, including assessment of its possible role in risk stratification, identification of novel druggable targets, and ultimately our ability to personalize therapy in TBI.

Prenatal risk factors and neonatal DNA methylation in very preterm infants

Background

Prenatal risk factors are related to poor health and developmental outcomes for infants, potentially via epigenetic mechanisms. We tested associations between person-centered prenatal risk profiles, cumulative prenatal risk models, and epigenome-wide DNA methylation (DNAm) in very preterm neonates.

Methods

We studied 542 infants from a multi-center study of infants born < 30 weeks postmenstrual age. We assessed 24 prenatal risk factors via maternal report and medical record review. Latent class analysis was used to define prenatal risk profiles. DNAm was quantified from neonatal buccal cells using the Illumina MethylationEPIC Beadarray.

Results

We identified three latent profiles of women: a group with few risk factors (61%) and groups with elevated physical (26%) and psychological (13%) risk factors. Neonates born to women in higher risk subgroups had differential DNAm at 2 CpG sites. Higher cumulative prenatal risk was associated with methylation at 15 CpG sites, 12 of which were located in genes previously linked to physical and mental health and neurodevelopment.

Conclusion

We observed associations between prenatal risk factors and DNAm in very preterm infants using both person-centered and cumulative risk approaches. Epigenetics offers a potential biological indicator of prenatal risk exposure.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13148-021-01164-9.

Estimands in epigenome-wide association studies

Background

In DNA methylation analyses like epigenome-wide association studies, effects in differentially methylated CpG sites are assessed. Two kinds of outcomes can be used for statistical analysis: Beta-values and M-values. M-values follow a normal distribution and help to detect differentially methylated CpG sites. As biological effect measures, differences of M-values are more or less meaningless. Beta-values are of more interest since they can be interpreted directly as differences in percentage of DNA methylation at a given CpG site, but they have poor statistical properties. Different frameworks are proposed for reporting estimands in DNA methylation analysis, relying on Beta-values, M-values, or both.

Results

We present and discuss four possible approaches of achieving estimands in DNA methylation analysis. In addition, we present the usage of M-values or Beta-values in the context of bioinformatical pipelines, which often demand a predefined outcome. We show the dependencies between the differences in M-values to differences in Beta-values in two data simulations: a analysis with and without confounder effect. Without present confounder effects, M-values can be used for the statistical analysis and Beta-values statistics for the reporting. If confounder effects exist, we demonstrate the deviations and correct the effects by the intercept method. Finally, we demonstrate the theoretical problem on two large human genome-wide DNA methylation datasets to verify the results.

Conclusions

The usage of M-values in the analysis of DNA methylation data will produce effect estimates, which cannot be biologically interpreted. The parallel usage of Beta-value statistics ignores possible confounder effects and can therefore not be recommended. Hence, if the differences in Beta-values are the focus of the study, the intercept method is recommendable. Hyper- or hypomethylated CpG sites must then be carefully evaluated. If an exploratory analysis of possible CpG sites is the aim of the study, M-values can be used for inference.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13148-021-01083-9.

Genetic and epigenetic analyses of panic disorder in the post-GWAS era

Panic disorder (PD) is a common and debilitating neuropsychiatric disorder characterized by panic attacks coupled with excessive anxiety. Both genetic factors and environmental factors play an important role in PD pathogenesis and response to treatment. However, PD is clinically heterogeneous and genetically complex, and the exact genetic or environmental causes of this disorder remain unclear. Various approaches for detecting disease-causing genes have recently been made available. In particular, genome-wide association studies (GWAS) have attracted attention for the identification of disease-associated loci of multifactorial disorders. This review introduces GWAS of PD, followed by a discussion about the limitations of GWAS and the major challenges facing geneticists in the post-GWAS era. Alternative strategies to address these challenges are then proposed, such as epigenome-wide association studies (EWAS) and rare variant association studies (RVAS) using next-generation sequencing. To date, however, few reports have described these analyses, and the evidence remains insufficient to confidently identify or exclude rare variants or epigenetic changes in PD. Further analyses are therefore required, using sample sizes in the tens of thousands, extensive functional annotations, and highly targeted hypothesis testing.

Guidance for DNA methylation studies: statistical insights from the Illumina EPIC array

Background

There has been a steady increase in the number of studies aiming to identify DNA methylation differences associated with complex phenotypes. Many of the challenges of epigenetic epidemiology regarding study design and interpretation have been discussed in detail, however there are analytical concerns that are outstanding and require further exploration. In this study we seek to address three analytical issues. First, we quantify the multiple testing burden and propose a standard statistical significance threshold for identifying DNA methylation sites that are associated with an outcome. Second, we establish whether linear regression, the chosen statistical tool for the majority of studies, is appropriate and whether it is biased by the underlying distribution of DNA methylation data. Finally, we assess the sample size required for adequately powered DNA methylation association studies.

Results

We quantified DNA methylation in the Understanding Society cohort (n = 1175), a large population based study, using the Illumina EPIC array to assess the statistical properties of DNA methylation association analyses. By simulating null DNA methylation studies, we generated the distribution of p-values expected by chance and calculated the 5% family-wise error for EPIC array studies to be 9 × 10^− 8. Next, we tested whether the assumptions of linear regression are violated by DNA methylation data and found that the majority of sites do not satisfy the assumption of normal residuals. Nevertheless, we found no evidence that this bias influences analyses by increasing the likelihood of affected sites to be false positives. Finally, we performed power calculations for EPIC based DNA methylation studies, demonstrating that existing studies with data on ~ 1000 samples are adequately powered to detect small differences at the majority of sites.

Conclusion

We propose that a significance threshold of P < 9 × 10^− 8 adequately controls the false positive rate for EPIC array DNA methylation studies. Moreover, our results indicate that linear regression is a valid statistical methodology for DNA methylation studies, despite the fact that the data do not always satisfy the assumptions of this test. These findings have implications for epidemiological-based studies of DNA methylation and provide a framework for the interpretation of findings from current and future studies.

Electronic supplementary material

The online version of this article (10.1186/s12864-019-5761-7) contains supplementary material, which is available to authorized users.

Parallel profiling of DNA methylation and hydroxymethylation highlights neuropathology-associated epigenetic variation in Alzheimer's disease

BACKGROUND

Alzheimer's disease is a progressive neurodegenerative disorder that is hypothesized to involve epigenetic dysfunction. Previous studies of DNA modifications in Alzheimer's disease have been unable to distinguish between DNA methylation and DNA hydroxymethylation. DNA hydroxymethylation has been shown to be enriched in the human brain, although its role in Alzheimer's disease has not yet been fully explored. Here, we utilize oxidative bisulfite conversion, in conjunction with the Illumina Infinium Human Methylation 450K microarray, to identify neuropathology-associated differential DNA methylation and DNA hydroxymethylation in the entorhinal cortex.

RESULTS

We identified one experiment-wide significant differentially methylated position residing in the WNT5B gene. Next, we investigated pathology-associated regions consisting of multiple adjacent loci. We identified one significant differentially hydroxymethylated region consisting of four probes spanning 104 bases in the FBXL16 gene. We also identified two significant differentially methylated regions: one consisting of two probes in a 93 base-pair region in the ANK1 gene and the other consisting of six probes in a 99-base pair region in the ARID5B gene. We also highlighted three regions that show alterations in unmodified cytosine: two probes in a 39-base pair region of ALLC, two probes in a 69-base pair region in JAG2, and the same six probes in ARID5B that were differentially methylated. Finally, we replicated significant ANK1 disease-associated hypermethylation and hypohydroxymethylation patterns across eight CpG sites in an extended 118-base pair region in an independent cohort using oxidative-bisulfite pyrosequencing.

CONCLUSIONS

Our study represents the first epigenome-wide association study of both DNA methylation and hydroxymethylation in Alzheimer's disease entorhinal cortex. We demonstrate that previous estimates of DNA hypermethylation in ANK1 in Alzheimer's disease were underestimates as it is confounded by hypohydroxymethylation.

Elevated polygenic burden for autism is associated with differential DNA methylation at birth

BACKGROUND

Autism spectrum disorder (ASD) is a severe neurodevelopmental disorder characterized by deficits in social communication and restricted, repetitive behaviors, interests, or activities. The etiology of ASD involves both inherited and environmental risk factors, with epigenetic processes hypothesized as one mechanism by which both genetic and non-genetic variation influence gene regulation and pathogenesis. The aim of this study was to identify DNA methylation biomarkers of ASD detectable at birth.

METHODS

We quantified neonatal methylomic variation in 1263 infants-of whom ~ 50% went on to subsequently develop ASD-using DNA isolated from archived blood spots taken shortly after birth. We used matched genotype data from the same individuals to examine the molecular consequences of ASD-associated genetic risk variants, identifying methylomic variation associated with elevated polygenic burden for ASD. In addition, we performed DNA methylation quantitative trait loci (mQTL) mapping to prioritize target genes from ASD GWAS findings.

RESULTS

We identified robust epigenetic signatures of gestational age and prenatal tobacco exposure, confirming the utility of DNA methylation data generated from neonatal blood spots. Although we did not identify specific loci showing robust differences in neonatal DNA methylation associated with later ASD, there was a significant association between increased polygenic burden for autism and methylomic variation at specific loci. Each unit of elevated ASD polygenic risk score was associated with a mean increase in DNA methylation of - 0.14% at two CpG sites located proximal to a robust GWAS signal for ASD on chromosome 8.

CONCLUSIONS

This study is the largest analysis of DNA methylation in ASD undertaken and the first to integrate genetic and epigenetic variation at birth. We demonstrate the utility of using a polygenic risk score to identify molecular variation associated with disease, and of using mQTL to refine the functional and regulatory variation associated with ASD risk variants.

Elevated DNA methylation across a 48-kb region spanning the HOXA gene cluster is associated with Alzheimer's disease neuropathology

Introduction:

Alzheimer’s disease is a neurodegenerative disorder that is hypothesized to involve epigenetic dysregulation of gene expression in the brain.

Methods:

We performed an epigenome-wide association study to identify differential DNA methylation associated with neuropathology in prefrontal cortex and superior temporal gyrus samples from 147 individuals, replicating our findings in two independent data sets (N = 117 and 740).

Results:

We identify elevated DNA methylation associated with neuropathology across a 48-kb region spanning 208 CpG sites within the HOXA gene cluster. A meta-analysis of the top-ranked probe within the HOXA3 gene (cg22962123) highlighted significant hypermethylation across all three cohorts (P = 3.11 × 10⁻¹⁸).

Discussion:

We present robust evidence for elevated DNA methylation associated with Alzheimer’s disease neuropathology spanning the HOXA gene cluster on chromosome 7. These data add to the growing evidence highlighting a role for epigenetic variation in Alzheimer’s disease, implicating the HOX gene family as a target for future investigation.

Genome-wide methylation data mirror ancestry information

Background

Genetic data are known to harbor information about human demographics, and genotyping data are commonly used for capturing ancestry information by leveraging genome-wide differences between populations. In contrast, it is not clear to what extent population structure is captured by whole-genome DNA methylation data.

Results

We demonstrate, using three large-cohort 450K methylation array data sets, that ancestry information signal is mirrored in genome-wide DNA methylation data and that it can be further isolated more effectively by leveraging the correlation structure of CpGs with cis-located SNPs. Based on these insights, we propose a method, EPISTRUCTURE, for the inference of ancestry from methylation data, without the need for genotype data.

Conclusions

EPISTRUCTURE can be used to infer ancestry information of individuals based on their methylation data in the absence of corresponding genetic data. Although genetic data are often collected in epigenetic studies of large cohorts, these are typically not made publicly available, making the application of EPISTRUCTURE especially useful for anyone working on public data. Implementation of EPISTRUCTURE is available in GLINT, our recently released toolset for DNA methylation analysis at: http://glint-epigenetics.readthedocs.io.

Electronic supplementary material

The online version of this article (doi:10.1186/s13072-016-0108-y) contains supplementary material, which is available to authorized users.

An integrated genetic-epigenetic analysis of schizophrenia: evidence for co-localization of genetic associations and differential DNA methylation

BACKGROUND

Schizophrenia is a highly heritable, neuropsychiatric disorder characterized by episodic psychosis and altered cognitive function. Despite success in identifying genetic variants associated with schizophrenia, there remains uncertainty about the causal genes involved in disease pathogenesis and how their function is regulated.

RESULTS

We performed a multi-stage epigenome-wide association study, quantifying genome-wide patterns of DNA methylation in a total of 1714 individuals from three independent sample cohorts. We have identified multiple differentially methylated positions and regions consistently associated with schizophrenia across the three cohorts; these effects are independent of important confounders such as smoking. We also show that epigenetic variation at multiple loci across the genome contributes to the polygenic nature of schizophrenia. Finally, we show how DNA methylation quantitative trait loci in combination with Bayesian co-localization analyses can be used to annotate extended genomic regions nominated by studies of schizophrenia, and to identify potential regulatory variation causally involved in disease.

CONCLUSIONS

This study represents the first systematic integrated analysis of genetic and epigenetic variation in schizophrenia, introducing a methodological approach that can be used to inform epigenome-wide association study analyses of other complex traits and diseases. We demonstrate the utility of using a polygenic risk score to identify molecular variation associated with etiological variation, and of using DNA methylation quantitative trait loci to refine the functional and regulatory variation associated with schizophrenia risk variants. Finally, we present strong evidence for the co-localization of genetic associations for schizophrenia and differential DNA methylation.

Profiling placental and fetal DNA methylation in human neural tube defects

Background

The incidence of neural tube defects (NTDs) declined by about 40 % in Canada with the introduction of a national folic acid (FA) fortification program. Despite the fact that few Canadians currently exhibit folate deficiency, NTDs are still the second most common congenital abnormality. FA fortification may have aided in reducing the incidence of NTDs by overcoming abnormal one carbon metabolism cycling, the process which provides one carbon units for methylation of DNA. We considered that NTDs persisting in a folate-replete population may also occur in the context of FA-independent compromised one carbon metabolism, and that this might manifest as abnormal DNA methylation (DNAm). Second trimester human placental chorionic villi, kidney, spinal cord, brain, and muscle were collected from 19 control, 22 spina bifida, and 15 anencephalic fetuses in British Columbia, Canada. DNA was extracted, assessed for methylenetetrahydrofolate reductase (MTHFR) genotype and for genome-wide DNAm using repetitive elements, in addition to the Illumina Infinium HumanMethylation450 (450k) array.

Results

No difference in repetitive element DNAm was noted between NTD status groups. Using a false discovery rate <0.05 and average group difference in DNAm ≥0.05, differentially methylated array sites were identified only in (1) the comparison of anencephaly to controls in chorionic villi (n = 4 sites) and (2) the comparison of spina bifida to controls in kidney (n = 3342 sites).

Conclusions

We suggest that the distinctive DNAm of spina bifida kidneys may be consequent to the neural tube defect or reflective of a common etiology for abnormal neural tube and renal development. Though there were some small shifts in DNAm in the other tested tissues, our data do not support the long-standing hypothesis of generalized altered genome-wide DNAm in NTDs. This finding may be related to the fact that most Canadians are not folate deficient, but it importantly opens the field to the investigation of other epigenetic and non-epigenetic mechanisms in the etiology of NTDs.

Electronic supplementary material

The online version of this article (doi:10.1186/s13072-016-0054-8) contains supplementary material, which is available to authorized users.