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An improved understanding of etiological mechanisms in Parkinson’s disease (PD) is urgently needed because the number of affected individuals is projected to increase rapidly as populations age. We present results from a blood-based methylome-wide association study of PD involving meta-analysis of 229 K CpG probes in 1,132 cases and 999 controls from two independent cohorts. We identify two previously unreported epigenome-wide significant associations with PD, including cg06690548 on chromosome 4. We demonstrate that cg06690548 hypermethylation in PD is associated with down-regulation of the SLC7A11 gene and show this is consistent with an environmental exposure, as opposed to medications or genetic factors with effects on DNA methylation or gene expression. These findings are notable because SLC7A11 codes for a cysteine-glutamate anti-porter regulating levels of the antioxidant glutathione, and it is a known target of the environmental neurotoxin β-methylamino-L-alanine (BMAA). Our study identifies the SLC7A11 gene as a plausible biological target in PD., Parkinson’s disease (PD) is a common neurodegenerative disorder with a complex etiology involving genetics and the environment. Here, Vallerga et al. identify two CpG probes associated with PD in a blood cell type-corrected epigenome-wide meta-analysis, implicating the SLC7A11 gene as a plausible biological target.

Background People with neurodegenerative disorders show diverse clinical syndromes, genetic heterogeneity, and distinct brain pathological changes, but studies report overlap between these features. DNA methylation (DNAm) provides a way to explore this overlap and heterogeneity as it is determined by the combined effects of genetic variation and the environment. In this study, we aim to identify shared blood DNAm differences between controls and people with Alzheimer’s disease, amyotrophic lateral sclerosis, and Parkinson’s disease. Results We use a mixed-linear model method (MOMENT) that accounts for the effect of (un)known confounders, to test for the association of each DNAm site with each disorder. While only three probes are found to be genome-wide significant in each MOMENT association analysis of amyotrophic lateral sclerosis and Parkinson’s disease (and none with Alzheimer’s disease), a fixed-effects meta-analysis of the three disorders results in 12 genome-wide significant differentially methylated positions. Predicted immune cell-type proportions are disrupted across all neurodegenerative disorders. Protein inflammatory markers are correlated with profile sum-scores derived from disease-associated immune cell-type proportions in a healthy aging cohort. In contrast, they are not correlated with MOMENT DNAm-derived profile sum-scores, calculated using effect sizes of the 12 differentially methylated positions as weights. Conclusions We identify shared differentially methylated positions in whole blood between neurodegenerative disorders that point to shared pathogenic mechanisms. These shared differentially methylated positions may reflect causes or consequences of disease, but they are unlikely to reflect cell-type proportion differences.

Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease with an estimated heritability of around 50%. DNA methylation patterns can serve as biomarkers 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 (EWAS) meta-analysis in 10,462 samples (7,344 ALS patients and 3,118 controls), representing the largest case-control study of DNA methylation for any disease to date. 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 show that DNA-methylation-based proxies for HDL-cholesterol, BMI, white blood cell (WBC) proportions and alcohol intake were independently associated with ALS. Integration of these results with our latest GWAS showed that cholesterol biosynthesis was causally related to ALS. Finally, we found that DNA methylation levels at several DMPs and blood cell proportion estimates derived from DNA methylation data, are associated with survival rate in patients, and could represent indicators of underlying disease processes.

Most epigenome-wide association studies (EWAS) quantify DNA methylation (DNAm) in peripheral tissues such as whole blood to identify positions in the genome where variation is statistically associated with a trait or exposure. As whole blood comprises a mix of cell types, it is unclear whether trait-associated DNAm variation is specific to an individual cellular population. We collected three peripheral tissues (whole blood, buccal epithelial and nasal epithelial cells) from thirty individuals. Whole blood samples were subsequently processed using fluorescence-activated cell sorting (FACS) to purify five constituent cell-types (monocytes, granulocytes, CD4+ T cells, CD8+ T cells, and B cells). DNAm was profiled in all eight sample-types from each individual using the Illumina EPIC array. We identified significant differences in both the level and variability of DNAm between different sample types, and DNAm data-derived estimates of age and smoking were found to differ dramatically across sample types from the same individual. We found that for the majority of loci variation in DNAm in individual blood cell types was only weakly predictive of variance in DNAm measured in whole blood, although the proportion of variance explained was greater than that explained by either buccal or nasal epithelial samples. Covariation across sample types was much higher for DNAm sites influenced by genetic factors. Overall, we observe that DNAm variation in whole blood is additively influenced by a combination of the major blood cell types. For a subset of sites, however, variable DNAm detected in whole blood can be attributed to variation in a single blood cell type providing potential mechanistic insight about EWAS findings. Our results suggest that associations between whole blood DNAm and traits or exposures reflect differences in multiple cell types and our data will facilitate the interpretation of findings in epigenetic epidemiology., Author summary As epigenetic variation is cell-type specific, an ongoing challenge in epigenetic epidemiology is how to interpret studies performed using bulk tissue (for example, whole blood) which comprises a mix of different cell types. In this study, we identified major differences in DNA methylation (DNAm) across multiple peripheral tissues and different blood cell types, with each sample type being characterized by a unique signature across multiple genomic loci. We demonstrate how these differences influence commonly used prediction scores derived from DNAm data for age and tobacco smoking, with estimates for the same individual being highly variable across tissues and cell types. Our results enabled us to assess the extent to which variable DNAm in each individual blood cell type relates to variation measured in whole blood. We found that although individual blood cell types predict more of the variation in DNAm in whole blood compared to buccal and nasal epithelial cells, the actual proportion of variance explained is relatively small, except for at sites where DNAm is under genetic control. Our data indicate that for most sites variation in multiple blood cell types additively combines to drive variation in DNAm measured in whole blood. Of note, for a subset of sites, variation in DNAm detected in whole blood can be attributed to a specific blood cell type, potentially facilitating the interpretation of EWAS findings.

We conducted DNA methylation association analyses using Illumina 450K data from whole blood for an Australian amyotrophic lateral sclerosis (ALS) case–control cohort (782 cases and 613 controls). Analyses used mixed linear models as implemented in the OSCA software. We found a significantly higher proportion of neutrophils in cases compared to controls which replicated in an independent cohort from the Netherlands (1159 cases and 637 controls). The OSCA MOMENT linear mixed model has been shown in simulations to best account for confounders. When combined in a methylation profile score, the 25 most-associated probes identified by MOMENT significantly classified case–control status in the Netherlands sample (area under the curve, AUC = 0.65, CI95% = [0.62–0.68], p = 8.3 × 10−22). The maximum AUC achieved was 0.69 (CI95% = [0.66–0.71], p = 4.3 × 10−34) when cell-type proportion was included in the predictor.

Amyotrophic lateral sclerosis (ALS) is a complex late-onset, neurodegenerative disease with a genetic contribution to disease liability. Genome-wide association studies have identified eleven risk loci to date, including the TNIP1/GPX3 locus on chromosome five. Current association analysis data alone cannot determine the most plausible risk gene in this locus. Here, we undertake a comprehensive suite of studies to provide objective evidence to support or reject the relevance of these two genes. We use bioinformatic integration of genetic association data with omics reference data sets, in-vitro and in-vivo approaches to narrow down the likely candidate. TNIP1 and GPX3 are implicated in-silico (rs10463311 is an eQTL for these two genes). In-vivo expression analyses in ALS cases identify that GPX3 expression decreases with increased disability and rate of progression. Validation in-vivo, indicate GPX3 loss-of-function causes motor deficits in zebrafish embryos. Taken together, these results support GPX3 as being the ALS risk gene in this locus which has implications for understanding disease mechanisms and targeted therapeutic approaches.

The rapid increase of omic data in the past decades has greatly facilitated the investigation of associations between omic profiles such as DNA methylation (DNAm) and complex traits in large cohorts. Here, we proposed a mixed-linear-model-based method (called MOMENT) that tests for association between a DNAm probe and trait with all other distal probes fitted in multiple random-effect components to account for the effects of unobserved confounders as well as the correlations between distal probes induced by the confounders. We demonstrated by simulations that MOMENT showed a lower false positive rate and more robustness than existing methods. MOMENT has been implemented in a versatile software package (called OSCA) together with a number of other implementations for omic-data-based analysis including the estimation of variance in a trait captured by all measures of multiple omic profiles, omic-data-based quantitative trait locus (xQTL) analysis, and meta-analysis of xQTL data.