Mendelian randomization integrating GWAS and mQTL data identified novel pleiotropic DNA methylation loci for neuropathology of Alzheimer's disease

Linking DNA methylation in brain regions to Alzheimer's disease risk: a Mendelian randomization study

AIM

DNA methylation in brain regions represents a potential mechanism linking genetic variation to Alzheimer's disease (ad) risk, yet most studies have focused on blood-derived methylation markers. In this study, we conducted a systematic Mendelian randomization (MR) study to evaluate associations between predicted brain region-specific DNA methylation levels and ad risk, using methylation quantitative trait loci (mQTL) as genetic instruments.

METHODS

We analyzed mQTLs from five human brain regions: cerebellum (CRBLM), frontal cortex (FCTX), causal pons (PONS), and temporal cortex (TCTX) from 600 individuals in Gibbs et al's study, as well as mQTLs from dorsolateral prefrontal cortex (DLPFC) of 543 participants in the Religious Orders Study and the Rush Memory and Aging Project (ROSMAP). In our MR analyses, we integrated these mQTLs with single nucleotide polymorphisms (SNP)-ad risk summary statistics derived from 85 934 ad-related cases and 401 577 normal controls.

RESULTS

Among 62 554 cytosine-guanine dinucleotide (CpG) sites, we identified 597 CpG sites (CpGs) significantly associated with ad risk (false discovery rate (FDR) < 0.05). Of these, 289 were confirmed through colocalization and summary-based MR (SMR) analyses, including one CpG site in CRBLM, 285 in DLPFC, one in FCTX, two in PONS, and one in TCTX. By integrating gene expression data, we identified 19 CpG sites with consistent associations across methylation levels, expression of eight target genes, and ad risk, including novel regulatory mechanisms involving RITA1's modulation of cg11558705 and PCGF3's regulation of cg10009224.

CONCLUSION

Our findings highlight brain region-specific DNA methylation as a mediator of genetic risk for ad, offering insights into ad pathogenesis and identifying potential therapeutic targets.

Hallmarks of aging and Alzheimer's Disease pathogenesis: Paving the route for new therapeutic targets

Aging is the leading risk factor for Alzheimer's Disease (AD). Understanding the intricate interplay between biological aging and the AD pathophysiology may help to discover innovative treatments. The relationship between aging and core pathways of AD pathogenesis (amyloidopathy and tauopathy) have been extensively studied in preclinical models. However, the potential discordance between preclinical models and human pathology could represent a limitation in the identification of new therapeutic targets. This narrative review aims to gather the evidence currently available on the associations of β-Amyloid and Tau pathology with the hallmarks of aging in human studies. Briefly, our review suggests that while several hallmarks exhibit a robust association with AD pathogenesis (e.g., epigenetic alterations, chronic inflammation, dysbiosis), others (e.g., telomere attrition, cellular senescence, stem cell exhaustion) demonstrate either no relationship or weak associations. This is often due to limitations such as small sample sizes and study designs, being either cross-sectional or with short follow-up intervals, limiting the generalizability of the findings. Distinct hallmarks play varying roles in different stages of AD pathology, emphasizing the need for longitudinal studies with longer follow-up periods. Considering the intricate interconnections across the hallmarks of aging, future research on AD pathology should focus on multiple hallmarks simultaneously.

DNA Methylation in Alzheimer's Disease

To date, DNA methylation is the best characterized epigenetic modification in Alzheimer's disease. Involving the addition of a methyl group to the fifth carbon of the cytosine pyrimidine base, DNA methylation is generally thought to be associated with the silencing of gene expression. It has been hypothesized that epigenetics may mediate the interaction between genes and the environment in the manifestation of Alzheimer's disease, and therefore studies investigating DNA methylation could elucidate novel disease mechanisms. This chapter comprehensively reviews epigenomic studies, undertaken in human brain tissue and purified brain cell types, focusing on global methylation levels, candidate genes, epigenome wide approaches, and recent meta-analyses. We discuss key differentially methylated genes and pathways that have been highlighted to date, with a discussion on how new technologies and the integration of multiomic data may further advance the field.

Machine Learning and Mendelian Randomization Reveal Molecular Mechanisms and Causal Relationships of Immune-Related Biomarkers in Periodontitis

This study aimed to investigate the molecular mechanisms of periodontitis and identify key immune-related biomarkers using machine learning and Mendelian randomization (MR). Differentially expressed gene (DEG) analysis was performed on periodontitis datasets GSE16134 and GSE10334 from the Gene Expression Omnibus (GEO) database, followed by weighted gene co-expression network analysis (WGCNA) to identify relevant gene modules. Various machine learning algorithms were utilized to construct predictive models, highlighting core genes, while MR assessed the causal relationships between these genes and periodontitis. Additionally, immune infiltration analysis and single-cell sequencing were employed to explore the roles of key genes in immunity and their expression across different cell types. The integration of machine learning, MR, and single-cell sequencing represents a novel approach that significantly enhances our understanding of the immune dynamics and gene interactions in periodontitis. The study identified 682 significant DEGs, with WGCNA revealing seven gene modules associated with periodontitis and 471 core candidate genes. Among the 113 machine learning algorithms tested, XGBoost was the most effective in identifying periodontitis samples, leading to the selection of 19 core genes. MR confirmed significant causal relationships between CD93, CD69, and CXCL6 and periodontitis. Further analysis showed that these genes were correlated with various immune cells and exhibited specific expression patterns in periodontitis tissues. The findings suggest that CD93, CD69, and CXCL6 are closely related to the progression of periodontitis, with MR confirming their causal links to the disease. These genes have potential applications in the diagnosis and treatment of periodontitis, offering new insights into the disease's molecular mechanisms and providing valuable resources for precision medicine approaches in periodontitis management. Limitations of this study include the demographic and sample size constraints of the datasets, which may impact the generalizability of the findings. Future research is needed to validate these biomarkers in larger, diverse cohorts and to investigate their functional roles in the pathogenesis of periodontitis.

Transcriptomic and epigenomic profiling reveals altered responses to diesel emissions in Alzheimer's disease both in vitro and in population-based data

INTRODUCTION

Studies have correlated living close to major roads with Alzheimer's disease (AD) risk. However, the mechanisms responsible for this link remain unclear.

METHODS

We exposed olfactory mucosa (OM) cells of healthy individuals and AD patients to diesel emissions (DE). Cytotoxicity of exposure was assessed, mRNA, miRNA expression, and DNA methylation analyses were performed. The discovered altered pathways were validated using data from the human population-based Rotterdam Study.

RESULTS

DE exposure resulted in an almost four-fold higher response in AD OM cells, indicating increased susceptibility to DE effects. Methylation analysis detected different DNA methylation patterns, revealing new exposure targets. Findings were validated by analyzing data from the Rotterdam Study cohort and demonstrated a key role of nuclear factor erythroid 2-related factor 2 signaling in responses to air pollutants.

DISCUSSION

This study identifies air pollution exposure biomarkers and pinpoints key pathways activated by exposure. The data suggest that AD individuals may face heightened risks due to impaired cellular defenses.

HIGHLIGHTS

Healthy and AD olfactory cells respond differently to DE exposure. AD cells are highly susceptible to DE exposure. The NRF2 oxidative stress response is highly activated upon air pollution exposure. DE-exposed AD cells activate the unfolded protein response pathway. Key findings are also confirmed in a population-based study.

Characterizing the Relationship between Expression Quantitative Trait Loci (eQTLs), DNA Methylation Quantitative Trait Loci (mQTLs), and Breast Cancer Risk Variants

PURPOSE

To assess the association of a polygenic risk score (PRS) for functional genetic variants with the risk of developing breast cancer.

METHODS

Summary data-based Mendelian randomization (SMR) and heterogeneity in dependent instruments (HEIDI) were used to identify breast cancer risk variants associated with gene expression and DNA methylation levels. A new SMR-based PRS was computed from the identified variants (functional PRS) and compared to an established 313-variant breast cancer PRS (GWAS PRS). The two scores were evaluated in 3560 breast cancer cases and 3383 non-cancer controls and also in a prospective study (n = 10,213) comprising 418 cases.

RESULTS

We identified 149 variants showing pleiotropic association with breast cancer risk (eQTLHEIDI > 0.05 = 9, mQTLHEIDI > 0.05 = 165). The discriminatory ability of the functional PRS (AUCcontinuous [95% CI]: 0.540 [0.526 to 0.553]) was found to be lower than that of the GWAS PRS (AUCcontinuous [95% CI]: 0.609 [0.596 to 0.622]). Even when utilizing 457 distinct variants from both the functional and GWAS PRS, the combined discriminatory performance remained below that of the GWAS PRS (AUCcontinuous, combined [95% CI]: 0.561 [0.548 to 0.575]). A binary high/low-risk classification based on the 80th centile PRS in controls revealed a 6% increase in cases using the GWAS PRS compared to the functional PRS. The functional PRS identified an additional 12% of high-risk cases but also led to a 13% increase in high-risk classification among controls. Similar findings were observed in the SCHS prospective cohort, where the GWAS PRS outperformed the functional PRS, and the highest-performing PRS, a combined model, did not significantly improve over the GWAS PRS.

CONCLUSIONS

While this study identified potentially functional variants associated with breast cancer risk, their inclusion did not substantially enhance the predictive accuracy of the GWAS PRS.

Genome-Wide DNA Methylation in Early-Onset-Dementia Patients Brain Tissue and Lymphoblastoid Cell Lines

Epigenetics, a potential underlying pathogenic mechanism of neurodegenerative diseases, has been in the scope of several studies performed so far. However, there is a gap in regard to analyzing different forms of early-onset dementia and the use of Lymphoblastoid cell lines (LCLs). We performed a genome-wide DNA methylation analysis on sixty-four samples (from the prefrontal cortex and LCLs) including those taken from patients with early-onset forms of Alzheimer's disease (AD) and frontotemporal dementia (FTD) and healthy controls. A beta regression model and adjusted p-values were used to obtain differentially methylated positions (DMPs) via pairwise comparisons. A correlation analysis of DMP levels with Clariom D array gene expression data from the same cohort was also performed. The results showed hypermethylation as the most frequent finding in both tissues studied in the patient groups. Biological significance analysis revealed common pathways altered in AD and FTD patients, affecting neuron development, metabolism, signal transduction, and immune system pathways. These alterations were also found in LCL samples, suggesting the epigenetic changes might not be limited to the central nervous system. In the brain, CpG methylation presented an inverse correlation with gene expression, while in LCLs, we observed mainly a positive correlation. This study enhances our understanding of the biological pathways that are associated with neurodegeneration, describes differential methylation patterns, and suggests LCLs are a potential cell model for studying neurodegenerative diseases in earlier clinical phases than brain tissue.

xWAS analysis in neuropsychiatric disorders by integrating multi-molecular phenotype quantitative trait loci and GWAS summary data

BACKGROUND

Integrating quantitative trait loci (QTL) data related to molecular phenotypes with genome-wide association study (GWAS) data is an important post-GWAS strategic approach employed to identify disease-associated molecular features. Various types of molecular phenotypes have been investigated in neuropsychiatric disorders. However, these findings pertaining to distinct molecular features are often independent of each other, posing challenges for having an overview of the mapped genes.

METHODS

In this study, we comprehensively summarized published analyses focusing on four types of risk-related molecular features (gene expression, splicing transcriptome, protein abundance, and DNA methylation) across five common neuropsychiatric disorders. Subsequently, we conducted supplementary analyses with the latest GWAS dataset and corresponding deficient molecular phenotypes using Functional Summary-based Imputation (FUSION) and summary data-based Mendelian randomization (SMR). Based on the curated and supplemented results, novel reliable genes and their functions were explored.

RESULTS

Our findings revealed that eQTL exhibited superior ability in prioritizing risk genes compared to the other QTL, followed by sQTL. Approximately half of the genes associated with splicing transcriptome, protein abundance, and DNA methylation were successfully replicated by eQTL-associated genes across all five disorders. Furthermore, we identified 436 novel reliable genes, which enriched in pathways related with neurotransmitter transportation such as synaptic, dendrite, vesicles, axon along with correlations with other neuropsychiatric disorders. Finally, we identified ten multiple molecular involved regulation patterns (MMRP), which may provide valuable insights into understanding the contribution of molecular regulation network targeting these disease-associated genes.

CONCLUSIONS

The analyses prioritized novel and reliable gene sets related with five molecular features based on published and supplementary results for five common neuropsychiatric disorders, which were missed in the original GWAS analysis. Besides, the involved MMRP behind these genes could be given priority for further investigation to elucidate the pathogenic molecular mechanisms underlying neuropsychiatric disorders in future studies.

Stem Cell Models for Context-Specific Modeling in Psychiatric Disorders

Genome-wide association studies reveal the complex polygenic architecture underlying psychiatric disorder risk, but there is an unmet need to validate causal variants, resolve their target genes(s), and explore their functional impacts on disorder-related mechanisms. Disorder-associated loci regulate transcription of target genes in a cell type- and context-specific manner, which can be measured through expression quantitative trait loci. In this review, we discuss methods and insights from context-specific modeling of genetically and environmentally regulated expression. Human induced pluripotent stem cell-derived cell type and organoid models have uncovered context-specific psychiatric disorder associations by investigating tissue-, cell type-, sex-, age-, and stressor-specific genetic regulation of expression. Techniques such as massively parallel reporter assays and pooled CRISPR (clustered regularly interspaced short palindromic repeats) screens make it possible to functionally fine-map genome-wide association study loci and validate their target genes at scale. Integration of disorder-associated contexts with these patient-specific human induced pluripotent stem cell models makes it possible to uncover gene by environment interactions that mediate disorder risk, which will ultimately improve our ability to diagnose and treat psychiatric disorders.

Gene-environment interactions in Alzheimer disease: the emerging role of epigenetics

With the exception of a few monogenic forms, Alzheimer disease (AD) has a complex aetiology that is likely to involve multiple susceptibility genes and environmental factors. The role of environmental factors is difficult to determine and, until a few years ago, the molecular mechanisms underlying gene-environment (G × E) interactions in AD were largely unknown. Here, we review evidence that has emerged over the past two decades to explain how environmental factors, such as diet, lifestyle, alcohol, smoking and pollutants, might interact with the human genome. In particular, we discuss how various environmental AD risk factors can induce epigenetic modifications of key AD-related genes and pathways and consider how epigenetic mechanisms could contribute to the effects of oxidative stress on AD onset. Studies on early-life exposures are helping to uncover critical time windows of sensitivity to epigenetic influences from environmental factors, thereby laying the foundations for future primary preventative approaches. We conclude that epigenetic modifications need to be considered when assessing G × E interactions in AD.

Exploring Potential Causal Genes for Uterine Leiomyomas: A Summary Data-Based Mendelian Randomization and FUMA Analysis

Objective: To explore potential causal genetic variants and genes underlying the pathogenesis of uterine leiomyomas (ULs). Methods: We conducted the summary data-based Mendelian randomization (SMR) analyses and performed functional mapping and annotation using FUMA to examine genetic variants and genes that are potentially involved in the pathogenies of ULs. Both analyses used summarized data of a recent genome-wide association study (GWAS) on ULs, which has a total sample size of 244,324 (20,406 cases and 223,918 controls). We performed separate SMR analysis using CAGE and GTEx eQTL data. Results: Using the CAGE eQTL data, our SMR analysis identified 13 probes tagging 10 unique genes that were pleiotropically/potentially causally associated with ULs, with the top three probes being ILMN_1675156 (tagging CDC42, PSMR = 8.03 × 10-9), ILMN_1705330 (tagging CDC42, PSMR = 1.02 × 10-7) and ILMN_2343048 (tagging ABCB9, PSMR = 9.37 × 10-7). Using GTEx eQTL data, our SMR analysis did not identify any significant genes after correction for multiple testing. FUMA analysis identified 106 independent SNPs, 24 genomic loci and 137 genes that are potentially involved in the pathogenesis of ULs, seven of which were also identified by the SMR analysis. Conclusions: We identified many genetic variants, genes, and genomic loci that are potentially involved in the pathogenesis of ULs. More studies are needed to explore the exact underlying mechanisms in the etiology of ULs.

From pharmacogenetics to pharmaco-omics: Milestones and future directions

The origins of pharmacogenetics date back to the 1950s, when it was established that inter-individual differences in drug response are partially determined by genetic factors. Since then, pharmacogenetics has grown into its own field, motivated by the translation of identified gene-drug interactions into therapeutic applications. Despite numerous challenges ahead, our understanding of the human pharmacogenetic landscape has greatly improved thanks to the integration of tools originating from disciplines as diverse as biochemistry, molecular biology, statistics, and computer sciences. In this review, we discuss past, present, and future developments of pharmacogenetics methodology, focusing on three milestones: how early research established the genetic basis of drug responses, how technological progress made it possible to assess the full extent of pharmacological variants, and how multi-dimensional omics datasets can improve the identification, functional validation, and mechanistic understanding of the interplay between genes and drugs. We outline novel strategies to repurpose and integrate molecular and clinical data originating from biobanks to gain insights analogous to those obtained from randomized controlled trials. Emphasizing the importance of increased diversity, we envision future directions for the field that should pave the way to the clinical implementation of pharmacogenetics.

An Entropy Approach to Multiple Sclerosis Identification

Multiple sclerosis (MS) is a relatively common neurodegenerative illness that frequently causes a large level of disability in patients. While its cause is not fully understood, it is likely due to a combination of genetic and environmental factors. Diagnosis of multiple sclerosis through a simple clinical examination might be challenging as the evolution of the illness varies significantly from patient to patient, with some patients experiencing long periods of remission. In this regard, having a quick and inexpensive tool to help identify the illness, such as DNA CpG (cytosine-phosphate-guanine) methylation, might be useful. In this paper, a technique is presented, based on the concept of Shannon Entropy, to select CpGs as inputs for non-linear classification algorithms. It will be shown that this approach generates accurate classifications that are a statistically significant improvement over using all the data available or randomly selecting the same number of CpGs. The analysis controlled for factors such as age, gender and smoking status of the patient. This approach managed to reduce the number of CpGs used while at the same time significantly increasing the accuracy.

Janus kinase 2 (JAK2) methylation and obesity: A Mendelian randomization study

BACKGROUND AND AIMS

Janus kinase 2 (JAK2) play an important role in the energy metabolism. Whether there is a causal relationship between JAK2 methylation levels and obesity remains unclear. Based on the instrumental variables of 5 SNP sites, this study was aimed to explore the causal relationship between JAK2 methylation levels and obesity by Mendelian randomization analysis.

METHODS AND RESULTS

A total of 1021 participants (511 cases and 510 controls defined by body mass index (BMI) ≥ 28.0 kg/m2) was conducted from the Henan Rural Cohort study. SNPscan® was performed to test the SNP genotyping and MethylTarget™ was applied to detect the DNA methylation level. The logistic regression model was used to evaluate the associations between SNP or methylation of JAK2 and obesity (according to BMI). Mendelian randomization analysis was used to assess the potential causal association between JAK2 methylation and obesity. According to the logistic regression model, 1 CpG sit in the promotor was related to an increased risk of obesity (P < 0.05). 10 CpG sites in the exon were associated with decreased risk of obesity (P < 0.05). Mendelian randomization analysis showed a causal association between the methylated level of JAK2 and obesity, based on the instrumental variables of 5 SNPs (P < 0.05).

CONCLUSIONS

This study supported that the methylation degree of JAK2 has a complex relationship with obesity, which might be related to the region of methylation. A causal relationship exists between the methylated level of JAK2 and obesity.

Novel DNA methylation loci and genes showing pleiotropic association with Alzheimer's dementia: a network Mendelian randomization analysis

Previous genome-wide association studies (GWAS) have identified potential genetic variants involved in the risk of Alzheimer's dementia, but their underlying biological interpretation remains largely unclear. In addition, the effects of DNA methylation and gene expression on Alzheimer's dementia are not well understood. A network summary data-based Mendelian randomization (SMR) analysis was performed integrating cis- DNA methylation quantitative trait loci (mQTL) /cis- gene expression QTL (eQTL) data in the brain and blood, as well as GWAS summarized data for Alzheimer's dementia to evaluate the pleiotropic associations of DNA methylation and gene expression with Alzheimer's dementia and to explore the complex mechanisms underpinning Alzheimer's dementia. After correction for multiple testing (false discovery rate [FDR] P < 0.05) and filtering using the heterogeneity in dependent instruments (HEIDI) test (P_HEIDI>0.01), we identified dozens of DNA methylation sites and genes showing pleiotropic associations with Alzheimer's dementia. We found 22 and 16 potentially causal pathways of Alzheimer's dementia (i.e., SNP→DNA methylation→Gene expression→Alzheimer's dementia) in the brain and blood, respectively. Approximately two-thirds of the identified DNA methylation sites had an influence on gene expression and the expression of almost all the identified genes was regulated by DNA methylation. Our network SMR analysis provided evidence supporting the pleiotropic association of some novel DNA methylation sites and genes with Alzheimer's dementia and revealed possible causal pathways underlying the pathogenesis of Alzheimer's dementia. Our findings shed light on the role of DNA methylation in gene expression and in the development of Alzheimer's dementia.

Mendelian randomization analysis identified genes pleiotropically associated with central corneal thickness

OBJECTIVE

To prioritize genes that were pleiotropically or potentially causally associated with central corneal thickness (CCT).

METHODS

We applied the summary data-based Mendelian randomization (SMR) method integrating summarized data of genome-wide association study (GWAS) on CCT and expression quantitative trait loci (eQTL) data to identify genes that were pleiotropically associated with CCT. We performed separate SMR analysis using CAGE eQTL data and GTEx eQTL data. SMR analyses were done for participants of European and East Asian ancestries, separately.

RESULTS

We identified multiple genes showing pleiotropic association with CCT in the participants of European ancestry. CLIC3 (ILMN_1796423; PSMR = 4.15 × 10- 12), PTGDS (ILMN_1664464; PSMR = 6.88 × 10- 9) and C9orf142 (ILMN_1761138; PSMR = 8.09 × 10- 9) were the top three genes using the CAGE eQTL data, and RP11-458F8.4 (ENSG00000273142.1; PSMR = 5.89 × 10- 9), LCNL1 (ENSG00000214402.6; PSMR = 5.67 × 10- 8), and PTGDS (ENSG00000107317.7; PSMR = 1.92 × 10- 7) were the top three genes using the GTEx eQTL data. No genes showed significantly pleiotropic association with CCT in the participants of East Asian ancestry after correction for multiple testing.

CONCLUSION

We identified several genes pleiotropically associated with CCT, some of which represented novel genes influencing CCT. Our findings provided important leads to a better understanding of the genetic factors influencing CCT, and revealed potential therapeutic targets for the treatment of primary open-angle glaucoma and keratoconus.

Mendelian randomization analysis identified genes potentially pleiotropically associated with periodontitis

OBJECTIVE

To prioritize genes that were pleiotropically or potentially causally associated with periodontitis.

METHODS

We applied the summary data-based Mendelian randomization (SMR) method integrating genome-wide association study (GWAS) for periodontitis and expression quantitative trait loci (eQTL) data to identify genes that were pleiotropically associated with periodontitis. We performed separate SMR analysis using CAGE eQTL data and GTEx eQTL data. SMR analysis were done for participants of European and East Asian ancestries, separately.

RESULTS

We identified multiple genes showing pleiotropic association with periodontitis in participants of European ancestry and participants of East Asian ancestry. PDCD2 (corresponding probe: ILMN_1758915) was the top hit showing pleotropic association with periodontitis in the participants of European ancestry using CAGE eQTL data, and BX093763 (corresponding probe: ILMN_1899903) and AC104135.3 (corresponding probe: ENSG00000204792.2) were the top hits in the participants of East Asian ancestry using CAGE eQTL data and GTEx eQTL data, respectively.

CONCLUSION

We identified multiple genes that may be involved in the pathogenesis of periodontitis in participants of European ancestry and participants of East Asian ancestry. Our findings provided important leads to a better understanding of the mechanisms underlying periodontitis and revealed potential therapeutic targets for the effective treatment of periodontitis.