Prefrontal cortex eQTLs/mQTLs enriched in genetic variants associated with alcohol use disorder and other diseases

Trans-eQTL hotspots shape complex traits by modulating cellular states

Regulatory genetic variation shapes gene expression, providing an important mechanism connecting DNA variation and complex traits. The causal relationships between gene expression and complex traits remain poorly understood. Here, we integrated transcriptomes and 46 genetically complex growth traits in a large cross between two strains of the yeast Saccharomyces cerevisiae. We discovered thousands of genetic correlations between gene expression and growth, suggesting potential functional connections. Local regulatory variation was a minor source of these genetic correlations. Instead, genetic correlations tended to arise from multiple independent trans-acting regulatory loci. Trans-acting hotspots that affect the expression of numerous genes accounted for particularly large fractions of genetic growth variation and of genetic correlations between gene expression and growth. Genes with genetic correlations were enriched for similar biological processes across traits, but with heterogeneous direction of effect. Our results reveal how trans-acting regulatory hotspots shape complex traits by altering cellular states.

PTGES2 and RNASET2 identified as novel potential biomarkers and therapeutic targets for basal cell carcinoma: insights from proteome-wide mendelian randomization, colocalization, and MR-PheWAS analyses

Introduction

Basal cell carcinoma (BCC) is the most common skin cancer, lacking reliable biomarkers or therapeutic targets for effective treatment. Genome-wide association studies (GWAS) can aid in identifying drug targets, repurposing existing drugs, predicting clinical trial side effects, and reclassifying patients in clinical utility. Hence, the present study investigates the association between plasma proteins and skin cancer to identify effective biomarkers and therapeutic targets for BCC.

Methods

Proteome-wide mendelian randomization was performed using inverse-variance-weight and Wald Ratio methods, leveraging 1 Mb cis protein quantitative trait loci (cis-pQTLs) in the UK Biobank Pharma Proteomics Project (UKB-PPP) and the deCODE Health Study, to determine the causal relationship between plasma proteins and skin cancer and its subtypes in the FinnGen R10 study and the SAIGE database of Lee lab. Significant association with skin cancer and its subtypes was defined as a false discovery rate (FDR) < 0.05. pQTL to GWAS colocalization analysis was executed using a Bayesian model to evaluate five exclusive hypotheses. Strong colocalization evidence was defined as a posterior probability for shared causal variants (PP.H4) of ≥0.85. Mendelian randomization-Phenome-wide association studies (MR-PheWAS) were used to evaluate potential biomarkers and therapeutic targets for skin cancer and its subtypes within a phenome-wide human disease category.

Results

PTGES2, RNASET2, SF3B4, STX8, ENO2, and HS3ST3B1 (besides RNASET2, five other plasma proteins were previously unknown in expression quantitative trait loci (eQTL) and methylation quantitative trait loci (mQTL)) were significantly associated with BCC after FDR correction in the UKB-PPP and deCODE studies. Reverse MR showed no association between BCC and these proteins. PTGES2 and RNASET2 exhibited strong evidence of colocalization with BCC based on a posterior probability PP.H4 >0.92. Furthermore, MR-PheWAS analysis showed that BCC was the most significant phenotype associated with PTGES2 and RNASET2 among 2,408 phenotypes in the FinnGen R10 study. Therefore, PTGES2 and RNASET2 are highlighted as effective biomarkers and therapeutic targets for BCC within the phenome-wide human disease category.

Conclusion

The study identifies PTGES2 and RNASET2 plasma proteins as novel, reliable biomarkers and therapeutic targets for BCC, suggesting more effective clinical application strategies for patients.

Smoking-informed methylation and expression QTLs in human brain and colocalization with smoking-associated genetic loci

Smoking is a leading cause of preventable morbidity and mortality. Smoking is heritable, and genome-wide association studies (GWASs) of smoking behaviors have identified hundreds of significant loci. Most GWAS-identified variants are noncoding with unknown neurobiological effects. We used genome-wide genotype, DNA methylation, and RNA sequencing data in postmortem human nucleus accumbens (NAc) to identify cis-methylation/expression quantitative trait loci (meQTLs/eQTLs), investigate variant-by-cigarette smoking interactions across the genome, and overlay QTL evidence at smoking GWAS-identified loci to evaluate their regulatory potential. Active smokers (N = 52) and nonsmokers (N = 171) were defined based on cotinine biomarker levels and next-of-kin reporting. We simultaneously tested variant and variant-by-smoking interaction effects on methylation and expression, separately, adjusting for biological and technical covariates and correcting for multiple testing using a two-stage procedure. We found >2 million significant meQTL variants (padj < 0.05) corresponding to 41,695 unique CpGs. Results were largely driven by main effects, and five meQTLs, mapping to NUDT12, FAM53B, RNF39, and ADRA1B, showed a significant interaction with smoking. We found 57,683 significant eQTL variants for 958 unique eGenes (padj < 0.05) and no smoking interactions. Colocalization analyses identified loci with smoking-associated GWAS variants that overlapped meQTLs/eQTLs, suggesting that these heritable factors may influence smoking behaviors through functional effects on methylation/expression. One locus containing MUSTN1 and ITIH4 colocalized across all data types (GWAS, meQTL, and eQTL). In this first genome-wide meQTL map in the human NAc, the enriched overlap with smoking GWAS-identified genetic loci provides evidence that gene regulation in the brain helps explain the neurobiology of smoking behaviors.

Potential Molecular Mechanisms of Alcohol Use Disorder with Non-Coding RNAs and Gut Microbiota for the Development of Superior Therapeutic Application

Many investigations have evaluated the expression of noncoding RNAs (ncRNAs) as well as their related molecular functions and biological machineries in individuals with alcohol dependence. Alcohol dependence may be one of the most prevailing psychological disorders globally, and its pathogenesis is intricate and inadequately comprehended. There is substantial evidence indicating significant links between multiple genetic factors and the development of alcohol dependence. In particular, the critical roles of ncRNAs have been emphasized in the pathology of mental illnesses, probably including alcohol dependence. In the comprehension of the action of ncRNAs and their machineries of modification, furthermore, they have emerged as therapeutic targets for a variety of psychiatric illnesses, including alcohol dependence. It is worth mentioning that the dysregulated expression of ncRNAs has been regularly detected in individuals with alcohol dependence. An in-depth knowledge of the roles of ncRNAs and m6A modification may be valuable for the development of a novel treatment against alcohol dependence. In general, a more profound understanding of the practical roles of ncRNAs might make important contributions to the precise diagnosis and/or actual management of alcohol dependence. Here, in this review, we mostly focused on up-to-date knowledge regarding alterations and/or modifications in the expression of ncRNAs in individuals with alcohol dependence. Then, we present prospects for future research and therapeutic applications with a novel concept of the engram system.

Comparing feature selection and machine learning approaches for predicting CYP2D6 methylation from genetic variation

Introduction

Pharmacogenetics currently supports clinical decision-making on the basis of a limited number of variants in a few genes and may benefit paediatric prescribing where there is a need for more precise dosing. Integrating genomic information such as methylation into pharmacogenetic models holds the potential to improve their accuracy and consequently prescribing decisions. Cytochrome P450 2D6 (CYP2D6) is a highly polymorphic gene conventionally associated with the metabolism of commonly used drugs and endogenous substrates. We thus sought to predict epigenetic loci from single nucleotide polymorphisms (SNPs) related to CYP2D6 in children from the GUSTO cohort.

Methods

Buffy coat DNA methylation was quantified using the Illumina Infinium Methylation EPIC beadchip. CpG sites associated with CYP2D6 were used as outcome variables in Linear Regression, Elastic Net and XGBoost models. We compared feature selection of SNPs from GWAS mQTLs, GTEx eQTLs and SNPs within 2 MB of the CYP2D6 gene and the impact of adding demographic data. The samples were split into training (75%) sets and test (25%) sets for validation. In Elastic Net model and XGBoost models, optimal hyperparameter search was done using 10-fold cross validation. Root Mean Square Error and R-squared values were obtained to investigate each models' performance. When GWAS was performed to determine SNPs associated with CpG sites, a total of 15 SNPs were identified where several SNPs appeared to influence multiple CpG sites.

Results

Overall, Elastic Net models of genetic features appeared to perform marginally better than heritability estimates and substantially better than Linear Regression and XGBoost models. The addition of nongenetic features appeared to improve performance for some but not all feature sets and probes. The best feature set and Machine Learning (ML) approach differed substantially between CpG sites and a number of top variables were identified for each model.

Discussion

The development of SNP-based prediction models for CYP2D6 CpG methylation in Singaporean children of varying ethnicities in this study has clinical application. With further validation, they may add to the set of tools available to improve precision medicine and pharmacogenetics-based dosing.

Methylation and expression quantitative trait loci rs1799971 in the OPRM1 gene and rs4654327 in the OPRD1 gene are associated with opioid use disorder

Opioid use disorder (OUD) is a chronic and relapsing brain disease that results in significant mortality worldwide. Genetic factors are estimated to contribute to 40%-60% of the liability, with polymorphisms of opioid receptor genes implicated in this disorder. However, the mechanisms underlying these associations are not yet fully understood. In the present study, we first examined the methylation levels in the promoter region of the OPRM1, OPRD1, and OPRK1 genes in 111 healthy controls (HCs) and 120 patients with OUD, and genotyped three tag SNPs in these genes. Correlations between these SNPs and the methylation levels of the CpG sites and expression levels of the genes were analyzed. After identifying the mQTLs and eQTLs, we determined the associations between the mQTLs/eQTLs and susceptibility to and characteristics of OUD in 930 HCs and 801 patients with OUD. Our results demonstrated that SNPs rs1799971 in the OPRM1 gene and rs4654327 in the OPRD1 gene were both mQTLs and eQTLs. We observed unique correlations between mQTLs and methylation levels of several CpG sites in the OUD group compared to the HC group. Interestingly, both the two mQTLs and eQTLs were associated with the susceptibility to OUD. In conclusion, we suppose that mQTLs and eQTLs in genes may underlie the associations between certain risk genetic polymorphisms and OUD. These mQTLs and eQTLs could potentially serve as promising biomarkers for better management of opioid misuse.

Smoking-informed methylation and expression QTLs in human brain and colocalization with smoking-associated genetic loci

Smoking is a leading cause of preventable morbidity and mortality. Smoking is heritable, and genome-wide association studies (GWAS) of smoking behaviors have identified hundreds of significant loci. Most GWAS-identified variants are noncoding with unknown neurobiological effects. We used genome-wide genotype, DNA methylation, and RNA sequencing data in postmortem human nucleus accumbens (NAc) to identify cis-methylation/expression quantitative trait loci (meQTLs/eQTLs), investigate variant-by-cigarette smoking interactions across the genome, and overlay QTL evidence at smoking GWAS-identified loci to evaluate their regulatory potential. Active smokers (N=52) and nonsmokers (N=171) were defined based on cotinine biomarker levels and next-of-kin reporting. We simultaneously tested variant and variant-by-smoking interaction effects on methylation and expression, separately, adjusting for biological and technical covariates and using a two-stage multiple testing approach with eigenMT and Bonferroni corrections. We found >2 million significant meQTL variants (padj<0.05) corresponding to 41,695 unique CpGs. Results were largely driven by main effects; five meQTLs, mapping to NUDT12, FAM53B, RNF39, and ADRA1B, showed a significant interaction with smoking. We found 57,683 significant eQTLs for 958 unique eGenes (padj<0.05) and no smoking interactions. Colocalization analyses identified loci with smoking-associated GWAS variants that overlapped meQTLs/eQTLs, suggesting that these heritable factors may influence smoking behaviors through functional effects on methylation/expression. One locus containing MUSTIN1 and ITIH4 colocalized across all data types (GWAS + meQTL + eQTL). In this first genome-wide meQTL map in the human NAc, the enriched overlap with smoking GWAS-identified genetic loci provides evidence that gene regulation in the brain helps explain the neurobiology of smoking behaviors.

Hypomethylation of Long Interspersed Nucleotide Elements and Aldehyde Dehydrogenase in Patients of Alcohol Use Disorder with Cirrhosis

Alcohol use disorder (AUD) and cirrhosis are key outcomes of excessive alcohol use, and a genetic influence in these outcomes is increasingly recognized. While 80-90% of heavy alcohol users show evidence of fatty liver, only 10-20% progress to cirrhosis. There is currently no clear understanding of the causes of this difference in progression. The aim of this study is to evaluate genetics and epigenetics at the aldehyde dehydrogenase (ALDH2) locus in patients with AUD and liver complications. Study participants were inpatients from the clinical services of Gastroenterology and Psychiatry at St. John's Medical College Hospital (SJMCH) and the National Institute of Mental Health and Neurosciences (NIMHANS), Bangalore, India. Men diagnosed as having AUD with cirrhosis (AUDC+ve, N = 136) and AUD without cirrhosis (AUDC-ve, N = 107) were assessed. FibroScan/sonographic evidence was used to rule out fibrosis in the AUDC-ve group. Genomic DNA was used for genotyping at the ALDH2 (rs2238151) locus. A subset of 89 samples was used for DNA methylation (AUDC+ve, N = 44; and AUDC-ve, N = 45) analysis at long interspersed nucleotide element 1 (LINE-1) and ALDH2 cytosine-phosphate-guanine (CpG) loci by pyrosequencing. ALDH2 DNA methylation was significantly lower in the AUDC+ve group compared with the AUDC-ve group (p < 0.001). Lower methylation was associated with a risk allele (T) of the ALDH2 locus (rs2238151) (p = 0.01). Global (LINE-1) DNA methylation levels were also significantly lower in the AUDC+ve group compared with the AUDC-ve group (p = 0.01). Compromised global methylation (LINE-1) and hypomethylation at the ALDH2 gene was observed in patients with cirrhosis compared with those without cirrhosis. DNA methylation could be explored as a biomarker for cirrhosis and liver complications.

Integration of Mendelian randomisation and systems biology models to identify novel blood-based biomarkers for stroke

Stroke is the second largest cause of mortality in the world. Genome-wide association studies (GWAS) have identified some genetic variants associated with stroke risk, but their putative functional causal genes are unknown. Hence, we aimed to identify putative functional causal gene biomarkers of stroke risk. We used a summary-based Mendelian randomisation (SMR) approach to identify the pleiotropic associations of genetically regulated traits (i.e., gene expression and DNA methylation) with stroke risk. Using SMR approach, we integrated cis-expression quantitative loci (cis-eQTLs) and cis-methylation quantitative loci (cis-mQTLs) data with GWAS summary statistics of stroke. We also utilised heterogeneity in dependent instruments (HEIDI) test to distinguish pleiotropy from linkage from the observed associations identified through SMR analysis. Our integrative SMR analyses and HEIDI test revealed 45 candidate biomarker genes (FDR < 0.05; P_HEIDI>0.01) that were pleiotropically or potentially causally associated with stroke risk. Of those candidate biomarker genes, 10 genes (HTRA1, PMF1, FBN2, C9orf84, COL4A1, BAG4, NEK6, SH2B3, SH3PXD2A, ACAD10) were differentially expressed in genome-wide blood transcriptomics data from stroke and healthy individuals (FDR<0.05). Functional enrichment analysis of the identified candidate biomarker genes revealed gene ontologies and pathways involved in stroke, including "cell aging", "metal ion binding" and "oxidative damage". Based on the evidence of genetically regulated expression of genes through SMR and directly measured expression of genes in blood, our integrative analysis suggests ten genes as blood biomarkers of stroke risk. Furthermore, our study provides a better understanding of the influence of DNA methylation on the expression of genes linked to stroke risk.

The Genetically Informed Neurobiology of Addiction (GINA) model

Addictions are heritable and unfold dynamically across the lifespan. One prominent neurobiological theory proposes that substance-induced changes in neural circuitry promote the progression of addiction. Genome-wide association studies have begun to characterize the polygenic architecture undergirding addiction liability and revealed that genetic loci associated with risk can be divided into those associated with a general broad-spectrum liability to addiction and those associated with drug-specific addiction risk. In this Perspective, we integrate these genomic findings with our current understanding of the neurobiology of addiction to propose a new Genetically Informed Neurobiology of Addiction (GINA) model.

Methylation and expression quantitative trait locus rs6296 in the HTR1B gene is associated with susceptibility to opioid use disorder

Serotonin (5-HT) is implicated in the reward processes underlying substance use disorder. Epigenetic and transcriptional mechanisms contribute to the development of addictive states. To examine the potential mechanisms of 5-HT receptor genes in opioid use disorder, we first determined the associations between several single-nucleotide polymorphism (SNPs) in three representative 5-HT receptor genes (HTR1B, HTR2A, and HTR3B) and susceptibility to heroin use disorder in 1731 participants. Gene-gene interactions among these genes were analyzed. After identifying the susceptibility genes and SNPs for heroin use disorder, DNA methylation in the promoter region of these susceptibility genes was compared between 111 healthy controls and 120 patients with heroin use disorder. In addition, associations between the susceptibility SNPs and methylation of the CpG sites and gene promoters with differential methylation between groups were examined. Finally, the function of the susceptibility SNPs in the expression of the corresponding genes was screened. Our results demonstrated that rs6296 in the HTR1B gene was correlated with susceptibility to heroin use disorder. Gene-gene interactions between the HTR1B and HTR2A genes were identified. The CpG sites HTR1B_07 and HTR1B_26 and the promoter region of the HTR1B gene were hypermethylated in patients with heroin use disorder compared with healthy controls. Notably, rs6296 correlated in an allele-specific manner with methylation in the HTR1B gene promoter in the blood and gene expression of the HTR1B gene in the frontal cortex and hypothalamus. SNP rs6296 was associated with opioid use disorder by involving mechanisms of DNA methylation and expression of the HTR1B gene.

RNA m6A Modification Changes in Postmortem Nucleus Accumbens of Subjects with Alcohol Use Disorder: A Pilot Study

BACKGROUND

The nucleus accumbens (NAc) is a key brain structure mediating the rewarding effect of alcohol and drug abuse. Chronic alcohol consumption may alter RNA methylome (or epitranscriptome) in the NAc, leading to altered gene expression and thus behavioral neuroadaptation to alcohol.

METHODS

This pilot study profiled the epitranscriptomes of mRNAs, long noncoding RNAs (lncRNAs), and microRNAs (miRNAs) in postmortem NAc of three male Caucasian subjects with alcohol use disorder (AUD) and three matched male Caucasian control subjects using Arraystar's m6A-mRNA&lncRNA Epitranscriptomic Microarray assay. Differentially methylated (DM) RNAs and the function of DM RNAs were analyzed by biostatistics and bioinformatics programs.

RESULTS

26 mRNAs were hypermethylated and three mRNAs were hypomethylated in the NAc of AUD subjects (≥2-fold changes and p ≤ 0.05). Most of these 29 DM mRNAs are involved in immune-related pathways (e.g., IL-17 signaling). Moreover, four lncRNAs were hypermethylated and one lncRNA was hypomethylated in the NAc of AUD subjects (≥2-fold changes and p ≤ 0.05). Additionally, three miRNAs were hypermethylated in the NAc of AUD subjects (≥2-fold changes and p ≤ 0.05).

CONCLUSIONS

This study revealed RNA methylomic changes in the NAc of AUD subjects, suggesting that chronic alcohol consumption may lead to AUD through epitranscriptomic RNA modifications. Our findings need to be replicated in a larger sample.

Dual methylation and hydroxymethylation study of alcohol use disorder

Using an integrative, multi-tissue design, we sought to characterize methylation and hydroxymethylation changes in blood and brain associated with alcohol use disorder (AUD). First, we used epigenomic deconvolution to perform cell-type-specific methylome-wide association studies within subpopulations of granulocytes/T-cells/B-cells/monocytes in 1132 blood samples. Blood findings were then examined for overlap with AUD-related associations with methylation and hydroxymethylation in 50 human post-mortem brain samples. Follow-up analyses investigated if overlapping findings mediated AUD-associated transcription changes in the same brain samples. Lastly, we replicated our blood findings in an independent sample of 412 individuals and aimed to replicate published alcohol methylation findings using our results. Cell-type-specific analyses in blood identified methylome-wide significant associations in monocytes and T-cells. The monocyte findings were significantly enriched for AUD-related methylation and hydroxymethylation in brain. Hydroxymethylation in specific sites mediated AUD-associated transcription in the same brain samples. As part of the most comprehensive methylation study of AUD to date, this work involved the first cell-type-specific methylation study of AUD conducted in blood, identifying and replicating a finding in DLGAP1 that may be a blood-based biomarker of AUD. In this first study to consider the role of hydroxymethylation in AUD, we found evidence for a novel mechanism for cognitive deficits associated with AUD. Our results suggest promising new avenues for AUD research.

Methylation quantitative locus rs3758653 in the DRD4 gene is associated with duration from first heroin exposure to addiction

Opioid addiction is a chronic brain disease with a high heritability. However, the genetic underpinnings remain uncertain. DNA methylation is involved in the adaptive changes in neuroplasticity after prolonged drug use. The dopamine receptor D4 (DRD4) has an essential role in the reward processes associated with addictive drugs. To further elucidate the potential role and mechanism of the DRD4 gene variants in heroin addiction, we detected the methylation level of 46 CpG sites in the promoter region and the genotypes of three SNPs in the DRD4 gene. Correlations between the SNPs and methylation levels of the CpG sites, i.e., the analysis of methylation quantitative trait loci (mQTLs) was conducted. Following the identification of mQTLs that are unique in the heroin addiction group, we performed an association study between the mQTLs and traits of heroin addiction. Our results revealed that there were several correlations of SNPs rs3758653 and rs11246226 with the methylation levels of some CpG sites in the DRD4 gene. Among these SNP-CpG pairs, rs3758653-DRD4_04, rs3758653-DRD4_05, rs3758653-DRD4_13 and rs3758653-DRD4_03 were unique in the heroin addiction group. Moreover, we found that mQTL rs3758653 was associated with duration from first heroin exposure to addiction, and the expression level of the DRD4 gene in human brain regions of the frontal cortex and hippocampus. Our findings suggested that some mQTLs in the genome may be associated with traits of opioid addiction through implicating the processes of DNA methylation and gene expression.

Role of DNA Methylation in Mediating Genetic Risk of Psychiatric Disorders

Psychiatric disorders are common, complex, and heritable conditions estimated to be the leading cause of disability worldwide. The last decade of research in genomics of psychiatry, performed by multinational, and multicenter collaborative efforts on hundreds of thousands of mental disorder cases and controls, provided invaluable insight into the genetic risk variants of these conditions. With increasing cohort sizes, more risk variants are predicted to be identified in the near future, but there appears to be a knowledge gap in understanding how these variants contribute to the pathophysiology of psychiatric disorders. Majority of the identified common risk single-nucleotide polymorphisms (SNPs) are non-coding but are enriched in regulatory regions of the genome. It is therefore of great interest to study the impact of identified psychiatric disorders' risk SNPs on DNA methylation, the best studied epigenetic modification, playing a pivotal role in the regulation of transcriptomic processes, brain development, and functioning. This work outlines the mechanisms through which risk SNPs can impact DNA methylation levels and provides a summary of current evidence on the role of DNA methylation in mediating the genetic risk of psychiatric disorders.