Genome-wide DNA methylation profiling in whole blood reveals epigenetic signatures associated with migraine

Abstract Background Migraine is a common heritable neurovascular disorder typically characterised by episodic attacks of severe pulsating headache and nausea, often accompanied by visual, auditory or other sensory symptoms. Although genome-wide association studies have identified over 40 single nucleotide polymorphisms associated with migraine, there remains uncertainty about the casual genes involved in disease pathogenesis and how their function is regulated. Results We performed an epigenome-wide association study, quantifying genome-wide patterns of DNA methylation in 67 migraine cases and 67 controls with a matching age and sex distribution. Association analyses between migraine and methylation probe expression, after adjustment for cell type proportions, indicated an excess of small P values, but there was no significant single-probe association after correction for multiple testing (P < 1.09 × 10− 7). However, utilising a 1 kb sliding window approach to combine adjacent migraine-methylation association P values, we identified 62 independent differentially methylated regions (DMRs) underlying migraine (false discovery rate < 0.05). Migraine association signals were subtle but consistent in effect direction across the length of each DMR. Subsequent analyses showed that the migraine-associated DMRs were enriched in regulatory elements of the genome and were in close proximity to genes involved in solute transportation and haemostasis. Conclusions This study represents the first genome-wide analysis of DNA methylation in migraine. We have identified DNA methylation in the whole blood of subjects associated with migraine, highlighting novel loci that provide insight into the biological pathways and mechanisms underlying migraine pathogenesis

Genome-wide meta-analyses reveal novel loci for verbal short-term memory and learning

Understanding the genomic basis of memory processes may help in combating neurodegenerative disorders. Hence, we examined the associations of common genetic variants with verbal short-term memory and verbal learning in adults without dementia or stroke (N = 53,637). We identified novel loci in the intronic region of CDH18, and at 13q21 and 3p21.1, as well as an expected signal in the APOE/APOC1/TOMM40 region. These results replicated in an independent sample. Functional and bioinformatic analyses supported many of these loci and further implicated POC1. We showed that polygenic score for verbal learning associated with brain activation in right parieto-occipital region during working memory task. Finally, we showed genetic correlations of these memory traits with several neurocognitive and health outcomes. Our findings suggest a role of several genomic loci in verbal memory processes.Peer reviewe

Integrating genome-wide association and blood genomic profiling data to characterise migraine risk loci

This thesis involved a multi-staged integrated study of gene expression, DNA methylation, and DNA sequence variation data in a large sample of migraine cases and non-migraine controls. The analysis and integration of these data identified molecular perturbations associated with migraine, and prioritised migraine susceptibility genes for further functional characterisation. The use of multiple molecular data to study existing migraine loci has the potential to provide a substantial contribution to understanding the underlying genetic architecture and biological mechanisms of migraine, and may help in the development of diagnostic tests and new targets for drug therapy

A gene co-expression network-based analysis of multiple brain tissues reveals novel genes and molecular pathways underlying major depression

Major depression is a common and severe psychiatric disorder with a highly polygenic genetic architecture. Genome-wide association studies have successfully identified multiple independent genetic loci that harbour variants associated with major depression, but the exact causal genes and biological mechanisms are largely unknown. Tissue-specific network approaches may identify molecular mechanisms underlying major depression and provide a biological substrate for integrative analyses. We provide a framework for the identification of individual risk genes and gene co-expression networks using genome-wide association summary statistics and gene expression information across multiple human brain tissues and whole blood. We developed a novel gene-based method called eMAGMA that leverages tissue-specific eQTL information to identify 99 biologically plausible risk genes associated with major depression, of which 58 are novel. Among these novel associations is Complement Factor 4A (C4A), recently implicated in schizophrenia through its role in synaptic pruning during postnatal development. Major depression risk genes were enriched in gene co-expression modules in multiple brain tissues and the implicated gene modules contained genes involved in synaptic signalling, neuronal development, and cell transport pathways. Modules enriched with major depression signals were strongly preserved across brain tissues, but were weakly preserved in whole blood, highlighting the importance of using disease-relevant tissues in genetic studies of psychiatric traits. We identified tissue-specific genes and gene co-expression networks associated with major depression. Our novel analytical framework can be used to gain fundamental insights into the functioning of the nervous system in major depression and other brain-related traits

Genome-wide DNA methylation profiling in whole blood reveals epigenetic signatures associated with migraine

Abstract Background Migraine is a common heritable neurovascular disorder typically characterised by episodic attacks of severe pulsating headache and nausea, often accompanied by visual, auditory or other sensory symptoms. Although genome-wide association studies have identified over 40 single nucleotide polymorphisms associated with migraine, there remains uncertainty about the casual genes involved in disease pathogenesis and how their function is regulated. Results We performed an epigenome-wide association study, quantifying genome-wide patterns of DNA methylation in 67 migraine cases and 67 controls with a matching age and sex distribution. Association analyses between migraine and methylation probe expression, after adjustment for cell type proportions, indicated an excess of small P values, but there was no significant single-probe association after correction for multiple testing (P < 1.09 × 10− 7). However, utilising a 1 kb sliding window approach to combine adjacent migraine-methylation association P values, we identified 62 independent differentially methylated regions (DMRs) underlying migraine (false discovery rate < 0.05). Migraine association signals were subtle but consistent in effect direction across the length of each DMR. Subsequent analyses showed that the migraine-associated DMRs were enriched in regulatory elements of the genome and were in close proximity to genes involved in solute transportation and haemostasis. Conclusions This study represents the first genome-wide analysis of DNA methylation in migraine. We have identified DNA methylation in the whole blood of subjects associated with migraine, highlighting novel loci that provide insight into the biological pathways and mechanisms underlying migraine pathogenesis

An integrative systems-based analysis of substance use: eQTL-informed gene-based tests, gene networks, and biological mechanisms

Genome-wide association studies have identified multiple genetic risk factors underlying susceptibility to substance use, however, the functional genes and biological mechanisms remain poorly understood. The discovery and characterization of risk genes can be facilitated by the integration of genome-wide association data and gene expression data across biologically relevant tissues and/or cell types to identify genes whose expression is altered by DNA sequence variation (expression quantitative trait loci; eQTLs). The integration of gene expression data can be extended to the study of genetic co-expression, under the biologically valid assumption that genes form co-expression networks to influence the manifestation of a disease or trait. Here, we integrate genome-wide association data with gene expression data from 13 brain tissues to identify candidate risk genes for 8 substance use phenotypes. We then test for the enrichment of candidate risk genes within tissue-specific gene co-expression networks to identify modules (or groups) of functionally related genes whose dysregulation is associated with variation in substance use. We identified eight gene modules in brain that were enriched with gene-based association signals for substance use phenotypes. For example, a single module of 40 co-expressed genes was enriched with gene-based associations for drinks per week and biological pathways involved in GABA synthesis, release, reuptake and degradation. Our study demonstrates the utility of eQTL and gene co-expression analysis to uncover novel biological mechanisms for substance use traits

Genome-wide analysis of blood gene expression in migraine implicates immune-inflammatory pathways

Background Typical migraine is a frequent, debilitating and painful headache disorder with an estimated heritability of about 50%. Although genome-wide association (GWA) studies have identified over 40 single nucleotide polymorphisms associated with migraine, further research is required to determine their biological role in migraine susceptibility. Therefore, we performed a study of genome-wide gene expression in a large sample of 83 migraine cases and 83 non-migraine controls to determine whether altered expression levels of genes and pathways could provide insights into the biological mechanisms underlying migraine. Methods We assessed whole blood gene expression data for 17994 expression probes measured using IlluminaHT-12 v4.0 BeadChips. Differential expression was assessed using multivariable logistic regression. Gene expression probes with a nominal p value < 0.05 were classified as differentially expressed. We identified modules of co-regulated genes and tested them for enrichment of differentially expressed genes and functional pathways using a false discovery rate <0.05. Results Association analyses between migraine and probe expression levels, adjusted for age and gender, revealed an excess of small p values, but there was no significant single-probe association after correction for multiple testing. Network analysis of pooled expression data identified 10 modules of co-expressed genes. One module harboured a significant number of differentially expressed genes and was strongly enriched with immune-inflammatory pathways, including multiple pathways expressed in microglial cells. Conclusions These data suggest immune-inflammatory pathways play an important role in the pathogenesis, manifestation, and/or progression of migraine in some patients. Furthermore, gene-expression associations are measurable in whole blood, suggesting the analysis of blood gene expression can inform our understanding of the biological mechanisms underlying migraine, identify biomarkers, and facilitate the discovery of novel pathways and thus determine new targets for drug therapy

An analysis of genetically regulated gene expression across multiple tissues implicates novel gene candidates in Alzheimer's disease

Introduction: Genome-wide association studies (GWAS) have successfully identified multiple independent genetic loci that harbour variants associated with Alzheimer's disease, but the exact causal genes and biological pathways are largely unknown. Methods: To prioritise likely causal genes associated with Alzheimer's disease, we used S-PrediXcan to integrate expression quantitative trait loci (eQTL) from the Genotype-Tissue Expression (GTEx) study and CommonMind Consortium (CMC) with Alzheimer's disease GWAS summary statistics. We meta-analysed the GTEx results using S-MultiXcan, prioritised disease-implicated loci using a computational fine-mapping approach, and performed a biological pathway analysis on the gene-based results. Results: We identified 126 tissue-specific gene-based associations across 48 GTEx tissues, targeting 50 unique genes. Meta-analysis of the tissue-specific associations identified 73 genes whose expression was associated with Alzheimer's disease. Additional analyses in the dorsolateral prefrontal cortex from the CMC identified 12 significant associations, 8 of which also had a significant association in GTEx tissues. Fine-mapping of causal gene sets prioritised gene candidates in 10 Alzheimer's disease loci with strong evidence for causality. Biological pathway analyses of the meta-analysed GTEx data and CMC data identified a significant enrichment of Alzheimer's disease association signals in plasma lipoprotein clearance, in addition to multiple immune-related pathways. Conclusions: Gene expression data from brain and peripheral tissues can improve power to detect regulatory variation underlying Alzheimer's disease. However, the associations in peripheral tissues may reflect tissue-shared regulatory variation for a gene. Therefore, future functional studies should be performed to validate the biological meaning of these associations and whether they represent new pathogenic tissues

Examining the causal relationship between associated risk factors and Alzheimer's disease using Mendelian randomization

BACKGROUND: Alzheimer 's disease (AD) is highly prevalent in the global population, predicted to affect 132 million people by 2050. A recent report by the Lancet Commission has identified 12 modifiable lifestyle risk factors that are associated with an increased risk of AD, including hypertension, education, exercise and tobacco and alcohol consumption. Targeting these risk factors could potentially prevent a third of all dementia cases by allowing people to reach the end of their life dementia free. However, current evidence is primarily obtained from observational studies which does not distinguish between factors that causally contribute to increased AD risk or factors that are correlated with disease risk through reverse causation or mediating variables. METHODS: We examined the genetic relationships between 10 AD-associated risk factors and AD, as well as the genetic relationships across risk factors, using LD Score regression. Next, we used bivariate Mendelian Randomization (MR) to assess the causal relationship between each risk factor and AD. Finally, we used Multivariable MR to model the combined causal relationships of multiple risk factors while accounting for genetic relationships across risk factors. RESULTS: We observed statistically significant genetic correlations between many of the examined risk factors (ranging from -0.88 to 0.75) and between these risk factors and AD (ranging from -0.33 to 0.45). Eight of the 10 examined risk factors were significantly correlated with AD risk. Notably, educational attainment had a significant genetic correlation with all other examined risk factors. The results of the MR analysis will be presented. As an example, preliminary results suggest that although AD and hearing impairment show a positive genetic correlation, hearing impairment does not causally influence AD risk. CONCLUSION: Modifying risk factors will only reduce AD prevalence if the factors are causally linked to disease risk. We will establish patterns of causality for 10 modifiable risk factors. We show that hearing impairment does not causally influence AD risk, suggesting that reducing hearing impairment in the population will not decrease the prevalence of AD.</p