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DNA methylation quantitative trait locus (mQTL) analyses on 32,851 participants identify genetic variants associated with DNA methylation at 420,509 sites in blood, resulting in a database of >270,000 independent mQTLs. Characterizing genetic influences on DNA methylation (DNAm) provides an opportunity to understand mechanisms underpinning gene regulation and disease. In the present study, we describe results of DNAm quantitative trait locus (mQTL) analyses on 32,851 participants, identifying genetic variants associated with DNAm at 420,509 DNAm sites in blood. We present a database of >270,000 independent mQTLs, of which 8.5% comprise long-range (trans) associations. Identified mQTL associations explain 15-17% of the additive genetic variance of DNAm. We show that the genetic architecture of DNAm levels is highly polygenic. Using shared genetic control between distal DNAm sites, we constructed networks, identifying 405 discrete genomic communities enriched for genomic annotations and complex traits. Shared genetic variants are associated with both DNAm levels and complex diseases, but only in a minority of cases do these associations reflect causal relationships from DNAm to trait or vice versa, indicating a more complex genotype-phenotype map than previously anticipated.

Characterizing genetic influences on DNA methylation (DNAm) provides an opportunity to understand mechanisms underpinning gene regulation and disease. Here we describe results of DNA methylation-quantitative trait loci (mQTL) analyses on 32,851 participants, identifying genetic variants associated with DNAm at 420,509 DNAm sites in blood. We present a database of >270,000 independent mQTL of which 8.5% comprise long-range (trans) associations. Identified mQTL associations explain 15-17% of the additive genetic variance of DNAm. We reveal that the genetic architecture of DNAm levels is highly polygenic and DNAm exhibits signatures of negative and positive natural selection. Using shared genetic control between distal DNAm sites we construct networks, identifying 405 discrete genomic communities enriched for genomic annotations and complex traits. Shared genetic factors are associated with both blood DNAm levels and complex diseases but in most cases these associations do not reflect causal relationships from DNAm to trait or vice versa indicating a more complex genotype-phenotype map than has previously been hypothesised.

Context Circulating concentrations of free triiodothyronine (fT3), free thyroxine (fT4), and thyrotropin (TSH) are partly heritable traits. Recent studies have advanced knowledge of their genetic architecture. Epigenetic modifications, such as DNA methylation (DNAm), may be important in pituitary-thyroid axis regulation and action, but data are limited. Objective To identify novel associations between fT3, fT4, and TSH and differentially methylated positions (DMPs) in the genome in subjects from 2 Australian cohorts. Method We performed an epigenome-wide association study (EWAS) of thyroid function parameters and DNAm using participants from: Brisbane Systems Genetics Study (median age 14.2 years, n = 563) and the Raine Study (median age 17.0 years, n = 863). Plasma fT3, fT4, and TSH were measured by immunoassay. DNAm levels in blood were assessed using Illumina HumanMethylation450 BeadChip arrays. Analyses employed generalized linear mixed models to test association between DNAm and thyroid function parameters. Data from the 2 cohorts were meta-analyzed. Results We identified 2 DMPs with epigenome-wide significant (P Conclusions This study has demonstrated associations between blood-based DNAm and both fT3 and TSH. This may provide insight into mechanisms underlying thyroid hormone action and/or pituitary-thyroid axis function.

Background Studies evaluating the relationship between chronic inflammation and cognitive functioning have produced heterogeneous results. A potential reason for this is the variability of inflammatory mediators which could lead to misclassifications of individuals’ persisting levels of inflammation. DNA methylation (DNAm) has shown utility in indexing environmental exposures and could be leveraged to provide proxy signatures of chronic inflammation. Method We conducted an elastic net regression of interleukin-6 (IL-6) in a cohort of 875 older adults (Lothian Birth Cohort 1936; mean age: 70 years) to develop a DNAm-based predictor. The predictor was tested in an independent cohort (Generation Scotland; N = 7028 [417 with measured IL-6], mean age: 51 years). Results A weighted score from 35 CpG sites optimally predicted IL-6 in the independent test set (Generation Scotland; R2 = 4.4%, p = 2.1 × 10−5). In the independent test cohort, both measured IL-6 and the DNAm proxy increased with age (serum IL-6: n = 417, β = 0.02, SE = 0.004, p = 1.3 × 10−7; DNAm IL-6 score: N = 7028, β = 0.02, SE = 0.0009, p < 2 × 10−16). Serum IL-6 did not associate with cognitive ability (n = 417, β = −0.06, SE = 0.05, p = .19); however, an inverse association was identified between the DNAm score and cognitive functioning (N = 7028, β = −0.16, SE = 0.02, pFDR < 2 × 10−16). Conclusions These results suggest methylation-based predictors can be used as proxies for inflammatory markers, potentially allowing for further insight into the relationship between inflammation and pertinent health outcomes.

Linking epigenetic marks to clinical outcomes improves insight into molecular processes, disease prediction, and therapeutic target identification. Here, a statistical approach is presented to infer the epigenetic architecture of complex disease, determine the variation captured by epigenetic effects, and estimate phenotype-epigenetic probe associations jointly. Implicitly adjusting for probe correlations, data structure (cell-count or relatedness), and single-nucleotide polymorphism (SNP) marker effects, improves association estimates and in 9,448 individuals, 75.7% (95% CI 71.70–79.3) of body mass index (BMI) variation and 45.6% (95% CI 37.3–51.9) of cigarette consumption variation was captured by whole blood methylation array data. Pathway-linked probes of blood cholesterol, lipid transport and sterol metabolism for BMI, and xenobiotic stimuli response for smoking, showed >1.5 times larger associations with >95% posterior inclusion probability. Prediction accuracy improved by 28.7% for BMI and 10.2% for smoking over a LASSO model, with age-, and tissue-specificity, implying associations are a phenotypic consequence rather than causal., Linking epigenetic marks to clinical outcomes promises insight into the underlying processes. Here, the authors introduce a statistical approach to estimate associations between a phenotype and all epigenetic probes jointly, and to estimate the proportion of variation captured by epigenetic effects.

Promoter-anchored chromatin interactions (PAIs) play a pivotal role in transcriptional regulation. Current high-throughput technologies for detecting PAIs, such as promoter capture Hi-C, are not scalable to large cohorts. Here, we present an analytical approach that uses summary-level data from cohort-based DNA methylation (DNAm) quantitative trait locus (mQTL) studies to predict PAIs. Using mQTL data from human peripheral blood (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$n = 1980$$\end{document}n=1980), we predict 34,797 PAIs which show strong overlap with the chromatin contacts identified by previous experimental assays. The promoter-interacting DNAm sites are enriched in enhancers or near expression QTLs. Genes whose promoters are involved in PAIs are more actively expressed, and gene pairs with promoter-promoter interactions are enriched for co-expression. Integration of the predicted PAIs with GWAS data highlight interactions among 601 DNAm sites associated with 15 complex traits. This study demonstrates the use of mQTL data to predict PAIs and provides insights into the role of PAIs in complex trait variation., Promoter-anchored chromatin interaction (PAI) is a mechanism by which gene expression is regulated, but methods to measure PAIs are costly and currently not scalable. Here, the authors develop an approach by which PAIs can be predicted using summary-level data from methylation QTL studies.

Carriers of large recurrent copy number variants (CNVs) have a higher risk of developing neurodevelopmental disorders. The 16p11.2 distal CNV predisposes carriers to e.g., autism spectrum disorder and schizophrenia. We compared subcortical brain volumes of 12 16p11.2 distal deletion and 12 duplication carriers to 6882 non-carriers from the large-scale brain Magnetic Resonance Imaging collaboration, ENIGMA-CNV. After stringent CNV calling procedures, and standardized FreeSurfer image analysis, we found negative dose-response associations with copy number on intracranial volume and on regional caudate, pallidum and putamen volumes (β = -0.71 to -1.37; P 0.0005). In an independent sample, consistent results were obtained, with significant effects in the pallidum (β = -0.95, P = 0.0042). The two data sets combined showed significant negative dose-response for the accumbens, caudate, pallidum, putamen and ICV (P = 0.0032, 8.9 × 10

The molecular factors which control circulating levels of inflammatory proteins are not well understood. Furthermore, association studies between molecular probes and human traits are often performed by linear model-based methods which may fail to account for complex structure and interrelationships within molecular datasets. Therefore, in this study, we perform genome- and epigenome-wide association studies (GWAS/EWAS) on the levels of 70 plasma-derived inflammatory protein biomarkers in healthy older adults (Lothian Birth Cohort 1936; n = 876; Olink® inflammation panel). We employ a Bayesian framework (BayesR+) which can account for issues pertaining to data structure and unknown confounding variables (with sensitivity analyses using ordinary least squares- (OLS) and mixed model-based approaches). We identified 13 SNPs associated with 13 proteins (n = 1 SNP each) concordant across OLS and Bayesian methods. We identified three CpG sites spread across three proteins (n = 1 CpG each) that were concordant across OLS, mixed-model and Bayesian analyses. Tagged genetic variants accounted for up to 45% of variance in protein levels (for MCP2, 36% of variance alone attributable to one polymorphism). Methylation data accounted for up to 46% of variation in protein levels (for CXCL10). Up to 66% of variation in protein levels (for VEGFA) was explained using genetic and epigenetic data combined. We demonstrated putative causal relationships between CD6 and IL18R1 with inflammatory bowel disease, and between IL12B and Crohn’s disease. Our data may aid understanding of the molecular regulation of the circulating inflammatory proteome as well as causal relationships between inflammatory mediators and disease.

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.

Background Advanced age is associated with cognitive and physical decline and is a major risk factor for a multitude of disorders. There is also a gap in life expectancy between males and females. DNA methylation differences have been shown to be associated with both age and sex. Here, we investigate age-by-sex differences in blood-based DNA methylation in an unrelated cohort of 2586 individuals between the ages of 18 and 87 years, with replication in a further 4450 individuals between the ages of 18 and 93 years. Methods Linear regression models were applied, with stringent genome-wide significance thresholds (p −8) used in both the discovery and replication data. A second, highly conservative mixed linear model method that better controls the false-positive rate was also applied, using the same genome-wide significance thresholds. Results Using the linear regression method, 52 autosomal and 597 X-linked CpG sites, mapping to 251 unique genes, replicated with concordant effect size directions in the age-by-sex interaction analysis. The site with the greatest difference mapped to GAGE10, an X-linked gene. Here, DNA methylation levels remained stable across the male adult age range (DNA methylation by age r = 0.02) but decreased across female adult age range (DNA methylation by age r = − 0.61). One site (cg23722529) with a significant age-by-sex interaction also had a quantitative trait locus (rs17321482) that is a genome-wide significant variant for prostate cancer. The mixed linear model method identified 11 CpG sites associated with the age-by-sex interaction. Conclusion The majority of differences in age-associated DNA methylation trajectories between sexes are present on the X chromosome. Several of these differences occur within genes that have been implicated in sexually dimorphic traits.

DNA methylation, which is modulated by both genetic factors and environmental exposures, may offer a unique opportunity to discover novel biomarkers of disease-related brain phenotypes, even when measured in other tissues than brain, such as blood. A few studies of small sample sizes have revealed associations between blood DNA methylation and neuropsychopathology, however, large-scale epigenome-wide association studies (EWAS) are needed to investigate the utility of DNA methylation profiling as a peripheral marker for the brain. Here, in an analysis of eleven international cohorts, totalling 3337 individuals, we report epigenome-wide meta-analyses of blood DNA methylation with volumes of the hippocampus, thalamus and nucleus accumbens (NAcc)—three subcortical regions selected for their associations with disease and heritability and volumetric variability. Analyses of individual CpGs revealed genome-wide significant associations with hippocampal volume at two loci. No significant associations were found for analyses of thalamus and nucleus accumbens volumes. Cluster-based analyses revealed additional differentially methylated regions (DMRs) associated with hippocampal volume. DNA methylation at these loci affected expression of proximal genes involved in learning and memory, stem cell maintenance and differentiation, fatty acid metabolism and type-2 diabetes. These DNA methylation marks, their interaction with genetic variants and their impact on gene expression offer new insights into the relationship between epigenetic variation and brain structure and may provide the basis for biomarker discovery in neurodegeneration and neuropsychiatric conditions.

The identification of genes and regulatory elements underlying the associations discovered by GWAS is essential to understanding the aetiology of complex traits (including diseases). Here, we demonstrate an analytical paradigm of prioritizing genes and regulatory elements at GWAS loci for follow-up functional studies. We perform an integrative analysis that uses summary-level SNP data from multi-omics studies to detect DNA methylation (DNAm) sites associated with gene expression and phenotype through shared genetic effects (i.e., pleiotropy). We identify pleiotropic associations between 7858 DNAm sites and 2733 genes. These DNAm sites are enriched in enhancers and promoters, and >40% of them are mapped to distal genes. Further pleiotropic association analyses, which link both the methylome and transcriptome to 12 complex traits, identify 149 DNAm sites and 66 genes, indicating a plausible mechanism whereby the effect of a genetic variant on phenotype is mediated by genetic regulation of transcription through DNAm., The identification of the causal gene at a GWAS locus remains to be a challenging task. Here, using the SMR & HEIDI method to integrate GWAS, eQTL and mQTL data, Wu et al. map DNA methylation sites to the transcriptome and thereby prioritize functionally relevant genes for 12 human complex traits.

Importance: Depressive disorders arise from a combination of genetic and environmental risk factors. Epigenetic disruption provides a plausible mechanism through which gene-environment interactions lead to depression. Large-scale, epigenome-wide studies on depression are missing, hampering the identification of potentially modifiable biomarkers.Objective: To identify epigenetic mechanisms underlying depression in middle-aged and elderly persons, using DNA methylation in blood.Design, Setting, and Participants: To date, the first cross-ethnic meta-analysis of epigenome-wide association studies (EWAS) within the framework of the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Consortium was conducted. The discovery EWAS included 7948 individuals of European origin from 9 population-based cohorts. Participants who were assessed for both depressive symptoms and whole-blood DNA methylation were included in the study. Results of EWAS were pooled using sample-size weighted meta-analysis. Replication of the top epigenetic sites was performed in 3308 individuals of African American and European origin from 2 population-based cohorts.Main Outcomes and Measures: Whole-blood DNA methylation levels were assayed with Illumina-Infinium Human Methylation 450K BeadChip and depressive symptoms were assessed by questionnaire.Results: The discovery cohorts consisted of 7948 individuals (4104 [51.6%] women) with a mean (SD) age of 65.4 (5.8) years. The replication cohort consisted of 3308 individuals (2456 [74.2%] women) with a mean (SD) age of 60.3 (6.4) years. The EWAS identified methylation of 3 CpG sites to be significantly associated with increased depressive symptoms: cg04987734 (P = 1.57 × 10−08; n = 11 256; CDC42BPB gene), cg12325605 (P = 5.24 × 10−09; n = 11 256; ARHGEF3 gene), and an intergenic CpG site cg14023999 (P = 5.99 × 10−08; n = 11 256; chromosome = 15q26.1). The predicted expression of the CDC42BPB gene in the brain (basal ganglia) (effect, 0.14; P = 2.7 × 10−03) and of ARHGEF3 in fibroblasts (effect, −0.48; P = 9.8 × 10−04) was associated with major depression.Conclusions and Relevance: This study identifies 3 methylated sites associated with depressive symptoms. All 3 findings point toward axon guidance as the common disrupted pathway in depression. The findings provide new insights into the molecular mechanisms underlying the complex pathophysiology of depression. Further research is warranted to determine the utility of these findings as biomarkers of depression and evaluate any potential role in the pathophysiology of depression and their downstream clinical effects.

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

There is increasing interest in the potential contribution of the gut microbiome to autism spectrum disorder (ASD). However, previous studies have been underpowered and have not been designed to address potential confounding factors in a comprehensive way. We performed a large autism stool metagenomics study (n = 247) based on participants from the Australian Autism Biobank and the Queensland Twin Adolescent Brain project. We found negligible direct associations between ASD diagnosis and the gut microbiome. Instead, our data support a model whereby ASD-related restricted interests are associated with less-diverse diet, and in turn reduced microbial taxonomic diversity and looser stool consistency. In contrast to ASD diagnosis, our dataset was well powered to detect microbiome associations with traits such as age, dietary intake, and stool consistency. Overall, microbiome differences in ASD may reflect dietary preferences that relate to diagnostic features, and we caution against claims that the microbiome has a driving role in ASD.

The evolutionary theories of mutation accumulation (MA) and disposable soma (DS) provide possible explanations for the existence of human aging. To better understand the relative importance of these theories, we devised a test to identify MA- and DS-consistent sites across the genome using familial DNA methylation data. Two key characteristics of DNA methylation allowed us to do so. First, DNA methylation exhibits distinct and widespread changes with age, with numerous age-differentially-methylated sites observed across the genome. Second, many sites show heritable DNA methylation patterns within families. We extended heritability predictions of MA and DS to DNA methylation, predicting that MA-consistent age-differentially-methylated sites will show increasing heritability with age, while DS-consistent sites will show the opposite. Variance components models were used to test for changing heritability of methylation with age at 48,601 age-differentially-methylated sites across the genome in 610 individuals from 176 families. Of these, 102 sites showed significant MA-consistent increases in heritability with age, while 2266 showed significant DS-consistent decreases in heritability. These results suggest that both MA and DS play a role in explaining aging and aging-related changes, and that while the majority of DNA methylation changes observed in aging are consistent with epigenetic drift, targeted changes exist and may mediate effects of aging-related genes.

We show that genotype-by-environment interaction can be inferred from an analysis without environmental data in a large sample., Genotype-by-environment interaction (GEI) is a fundamental component in understanding complex trait variation. However, it remains challenging to identify genetic variants with GEI effects in humans largely because of the small effect sizes and the difficulty of monitoring environmental fluctuations. Here, we demonstrate that GEI can be inferred from genetic variants associated with phenotypic variability in a large sample without the need of measuring environmental factors. We performed a genome-wide variance quantitative trait locus (vQTL) analysis of ~5.6 million variants on 348,501 unrelated individuals of European ancestry for 13 quantitative traits in the UK Biobank and identified 75 significant vQTLs with P < 2.0 × 10−9 for 9 traits, especially for those related to obesity. Direct GEI analysis with five environmental factors showed that the vQTLs were strongly enriched with GEI effects. Our results indicate pervasive GEI effects for obesity-related traits and demonstrate the detection of GEI without environmental data.

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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.

IntroductionAdvanced age is associated with cognitive and physical decline, and is a major risk factor for a multitude of disorders including neurodegenerative diseases such as Alzheimer’s disease. There is also a gap in life-expectancy between males and females. DNA methylation differences have been shown to be associated with both age and sex. Here, we investigate age-by-sex differences in DNA methylation in an unrelated cohort of 2,586 individuals between the ages of 18 and 87 years.MethodsGenome-wide DNA methylation was measured on the Illumina HumanMethylationEPIC beadchip in a subset of unrelated individuals from the Generation Scotland cohort. Mixed linear model-based analyses were performed to investigate the relationship between DNA methylation and an interaction term between age and sex, as well as chronological age.ResultsAt a genome-wide significance level of P < 3.6 × 10−8, 14 loci were associated with the age-by-sex interaction term, the majority of which were X-linked (n = 12). Seven of these loci were annotated to genes. The site with the greatest difference mapped to GAGE10, an X-linked gene. Here, DNA methylation levels remained stable across the male adult age range (DNA methylation x age r = 0.02), but decreased across female adult age range (DNA methylation x age r = −0.61). The seven age-by-sex-associated genes were enriched among differentially-expressed genes in lung, liver, testis and blood.ConclusionThe majority of differences in age-associated DNA methylation trajectories between sexes are present on the X-chromosome. Several of these differences occur within genes which have implicated in multiple cancers, schizophrenia and systemic lupus erythematosus.

Although plasma proteins may serve as important markers of disease risk in neurological conditions, the molecular mechanisms responsible for inter-individual variation in plasma protein levels are poorly understood. In this study, we conducted genome- and epigenome-wide association studies on the levels of 92 neurological proteins to identify genetic and epigenetic loci associated with their plasma concentrations (n = 750). We identified 62 independent genome-wide significant loci for 37 proteins (P < 5.4 × 10−10) and 68 epigenome-wide significant sites associated with the levels of 7 proteins (P < 3.9 × 10−10). Using this information, we identified biological pathways in which putative neurological biomarkers are implicated as well as molecular mechanisms through which genetic variation may perturb plasma protein levels. Additionally, we found evidence that poliovirus receptor is causally associated with Alzheimer’s disease. In conclusion, we identified many novel genetic and epigenetic factors that are associated with neurological protein levels which may inform disease biology and establish causal relationships between biomarkers and neurological diseases.

As cloud computing has become the de facto standard for big data processing, there is interest in using a multi-cloud environment that combines public cloud resources with private on-premise infrastructure. However, by decentralizing the infrastructure, a uniform storage solution is required to provide data movement between different clouds to assist on-demand computing. This paper presents a solution based on our earlier work, the MeDiCI (Metropolitan Data Caching Infrastructure) architecture. Specially, we extend MeDiCI to simplify the movement of data between different clouds and a centralized storage site. It uses a hierarchical caching system and supports most popular infrastructure-as-a-service (IaaS) interfaces, including Amazon AWS and OpenStack. As a result, our system allows the existing parallel data intensive application to be offloaded into IaaS clouds directly. The solution is illustrated using a large bioinformatics application, a Genome Wide Association Study (GWAS), with Amazons AWS, HUAWEI Cloud, and a private centralized storage system. The system is evaluated on Amazon AWS and the Australian national cloud.

Contains fulltext : 157507.pdf (Publisher’s version ) (Closed access) Spontaneous dizygotic (DZ) twinning occurs in 1%–4% of women, with familial clustering and unknown physiological pathways and genetic origin. DZ twinning might index increased fertility and has distinct health implications for mother and child. We performed a GWAS in 1,980 mothers of spontaneous DZ twins and 12,953 control subjects. Findings were replicated in a large Icelandic cohort and tested for association across a broad range of fertility traits in women. Two SNPs were identified (rs11031006 near FSHB, p = 1.54 × 10−9, and rs17293443 in SMAD3, p = 1.57 × 10−8) and replicated (p = 3 × 10−3 and p = 1.44 × 10−4, respectively). Based on ∼90,000 births in Iceland, the risk of a mother delivering twins increased by 18% for each copy of allele rs11031006-G and 9% for rs17293443-C. A higher polygenic risk score (PRS) for DZ twinning, calculated based on the results of the DZ twinning GWAS, was significantly associated with DZ twinning in Iceland (p = 0.001). A higher PRS was also associated with having children (p = 0.01), greater lifetime parity (p = 0.03), and earlier age at first child (p = 0.02). Allele rs11031006-G was associated with higher serum FSH levels, earlier age at menarche, earlier age at first child, higher lifetime parity, lower PCOS risk, and earlier age at menopause. Conversely, rs17293443-C was associated with later age at last child. We identified robust genetic risk variants for DZ twinning: one near FSHB and a second within SMAD3, the product of which plays an important role in gonadal responsiveness to FSH. These loci contribute to crucial aspects of reproductive capacity and health. 11 p.

Monozygotic (MZ) twins provide a natural system for investigating developmental plasticity and the potential epigenetic origins of disease. A major difference in the intrauterine environment between MZ pairs is whether they share a common placenta or have separate placentas. Using DNA methylation measured at >400,000 points in the genome on the Illumina HumanMethylation450 array, we demonstrate that the co-twins of MZ pairs (average age of 14) that shared a common placenta (n = 18 pairs) have more similar DNA methylation levels in blood throughout the genome relative to those with separate placentas (n = 16 pairs). Functional annotation of the genomic regions that show significantly different correlation between monochorionic (MC) and dichorionic (DC) MZ pairs found an over-representation of genes involved in the regulation of transcription, neuronal development, and cellular differentiation. These results support the idea that prenatal environmental exposures may have a lasting effect on an individual's epigenetic landscape, and the potential for these changes to have functional consequences.

Linking epigenetic marks to clinical outcomes improves insight into molecular processes, disease prediction, and therapeutic target identification. Here, a statistical approach is presented to infer the epigenetic architecture of complex disease, determine the variation captured by epigenetic effects, and estimate phenotype-epigenetic probe associations jointly. Implicitly adjusting for probe correlations, data structure (cell-count or relatedness), and single-nucleotide polymorphism (SNP) marker effects, improves association estimates and in 9,448 individuals, 75.7% (95% CI 71.70-79.3) of body mass index (BMI) variation and 45.6% (95% CI 37.3-51.9) of cigarette consumption variation was captured by whole blood methylation array data. Pathway-linked probes of blood cholesterol, lipid transport and sterol metabolism for BMI, and xenobiotic stimuli response for smoking, showed1.5 times larger associations with95% posterior inclusion probability. Prediction accuracy improved by 28.7% for BMI and 10.2% for smoking over a LASSO model, with age-, and tissue-specificity, implying associations are a phenotypic consequence rather than causal.

DNA methylation, which is modulated by both genetic factors and environmental exposures, may offer a unique opportunity to discover novel biomarkers of disease-related brain phenotypes, even when measured in other tissues than brain, such as blood. A few studies of small sample sizes have revealed associations between blood DNA methylation and neuropsychopathology, however, large-scale epigenome-wide association studies (EWAS) are needed to investigate the utility of DNA methylation profiling as a peripheral marker for the brain. Here, in an analysis of eleven international cohorts, totalling 3,337 individuals, we report epigenome-wide meta-analyses of blood DNA methylation with volumes of the hippocampus, thalamus and nucleus accumbens (NAcc) –three subcortical regions selected for their associations with disease and heritability and volumetric variability. Analyses of individual CpGs revealed genome-wide significant associations with hippocampal volume at two loci. No significant associations were found for analyses of thalamus and nucleus accumbens volumes. CpG sites associated with hippocampus volume were significantly enriched within cancer-related genes and within regulatory elements containing the transcriptionally repressive histone H3K27 tri-methylation mark that is vital for stem cell fate specification. Cluster-based analyses revealed additional differentially methylated regions (DMRs) associated with hippocampal volume. DNA methylation at these loci affected expression of proximal genes involved in in learning and memory, stem cell maintenance and differentiation, fatty acid metabolism and type-2 diabetes. These DNA methylation marks, their interaction with genetic variants and their impact on gene expression offer new insights into the relationship between epigenetic variation and brain structure and may provide the basis for biomarker discovery in neurodegeneration and neuropsychiatric conditions.

Background Major challenges in understanding the functional consequences of genetic risk factors for human disease are which tissues and cell types are affected and the limited availability of suitable tissue. The aim of this study was to evaluate tissue-specific genotype-epigenetic characteristics in DNA samples from both endometrium and blood collected from women at different stages of the menstrual cycle and relate results to genetic risk factors for reproductive traits and diseases. Results We analysed DNA methylation (DNAm) data from endometrium and blood samples from 66 European women. Methylation profiles were compared between stages of the menstrual cycle, and changes in methylation overlaid with changes in transcription and genotypes. We observed large changes in methylation (27,262 DNAm probes) across the menstrual cycle in endometrium that were not observed in blood. Individual genotype data was tested for association with methylation at 443,016 and 443,101 DNAm probes in endometrium and blood respectively to identify methylation quantitative trait loci (mQTLs). A total of 4546 sentinel cis-mQTLs (P

1AbstractEpigenetic DNA modification is partly under genetic control, and occurs in response to a wide range of environmental exposures. Linking epigenetic marks to clinical outcomes may provide greater insight into underlying molecular processes of disease, assist in the identification of therapeutic targets, and improve risk prediction. Here, we present a statistical approach, based on Bayesian inference, that estimates associations between disease risk and all measured epigenetic probes jointly, automatically controlling for both data structure (including cell-count effects, relatedness, and experimental batch effects) and correlations among probes. We benchmark our approach in simulation study, finding improved estimation of probe associations across a wide range of scenarios over existing approaches. Our method estimates the total proportion of disease risk captured by epigenetic probe variation, and when we applied it to measures of body mass index (BMI) and cigarette consumption behaviour in 5,101 individuals, we find that 66.7% (95% CI 60.0-72.8) of the variation in BMI and 67.7% (95% CI 58.4-76.9) of the variation in cigarette consumption can be captured by methylation array data from whole blood, independent of the variation explained by single nucleotide polymorphism markers. We find novel associations, with smoking behaviour associated with a methylation probe at the MNDA gene with >95% posterior inclusion probability, which is a myeloid cell nuclear differentiation antigen gene previously implicated as a biomarker for inflammation and non-Hodgkin lymphoma risk. We conduct unique genome-wide enrichment analyses, identifying blood cholesterol, lipid transport and sterol metabolism pathways for BMI, and response to xenobiotic stimulus and negative regulation of RNA polymerase II promoter transcription for smoking, all with >95% posterior inclusion probability of having methylation probes with associations >1.5 times larger than the average. Finally, we improve phenotypic prediction in two independent cohorts by 28.7% and 10.2% for BMI and smoking respectively over a LASSO model. These results imply that probe measures may capture large amounts of variance because they are likely a consequence of the phenotype rather than a cause. As a result, ‘omics’ data may enable accurate characterization of disease progression and identification of individuals who are on a path to disease. Our approach facilitates better understanding of the underlying epigenetic architecture of complex common disease and is applicable to any kind of genomics data.

Quantitative genetics theory predicts that X-chromosome dosage compensation (DC) will have a detectable effect on the amount of genetic and therefore phenotypic trait variances at associated loci in males and females. Here, we systematically examine the role of DC in humans in 20 complex traits in a sample of more than 450,000 individuals from the UK Biobank and 1600 gene expression traits from a sample of 2000 individuals as well as across-tissue gene expression from the GTEx resource. We find approximately twice as much X-linked genetic variation across the UK Biobank traits in males (mean h2SNP = 0.63%) compared to females (mean h2SNP = 0.30%), confirming the predicted DC effect. Our DC estimates for complex traits and gene expression are consistent with a small proportion of genes escaping X-inactivation in a trait- and tissue-dependent manner. Finally, we highlight examples of biologically relevant X-linked heterogeneity between the sexes that bias DC estimates if unaccounted for., Dosage compensation (DC) on the X chromosome has predictable effects on genetic and phenotypic trait variance. Here, the authors use information for 20 quantitative traits in the UK Biobank and across-tissue gene expression to compare X-linked heritability and the effects of trait-associated SNPs between the sexes.

Background DNA methylation levels change along with age, but few studies have examined the variation in the rate of such changes between individuals. Methods We performed a longitudinal analysis to quantify the variation in the rate of change of DNA methylation between individuals using whole blood DNA methylation array profiles collected at 2–4 time points (N = 2894) in 954 individuals (67–90 years). Results After stringent quality control, we identified 1507 DNA methylation CpG sites (rsCpGs) with statistically significant variation in the rate of change (random slope) of DNA methylation among individuals in a mixed linear model analysis. Genes in the vicinity of these rsCpGs were found to be enriched in Homeobox transcription factors and the Wnt signalling pathway, both of which are related to ageing processes. Furthermore, we investigated the SNP effect on the random slope. We found that 4 out of 1507 rsCpGs had one significant (P

Background Genome-wide DNA methylation (DNAm) profiling has allowed for the development of molecular predictors for a multitude of traits and diseases. Such predictors may be more accurate than the self-reported phenotypes and could have clinical applications. Results Here, penalized regression models are used to develop DNAm predictors for ten modifiable health and lifestyle factors in a cohort of 5087 individuals. Using an independent test cohort comprising 895 individuals, the proportion of phenotypic variance explained in each trait is examined for DNAm-based and genetic predictors. Receiver operator characteristic curves are generated to investigate the predictive performance of DNAm-based predictors, using dichotomized phenotypes. The relationship between DNAm scores and all-cause mortality (n = 212 events) is assessed via Cox proportional hazards models. DNAm predictors for smoking, alcohol, education, and waist-to-hip ratio are shown to predict mortality in multivariate models. The predictors show moderate discrimination of obesity, alcohol consumption, and HDL cholesterol. There is excellent discrimination of current smoking status, poorer discrimination of college-educated individuals and those with high total cholesterol, LDL with remnant cholesterol, and total:HDL cholesterol ratios. Conclusions DNAm predictors correlate with lifestyle factors that are associated with health and mortality. They may supplement DNAm-based predictors of age to identify the lifestyle profiles of individuals and predict disease risk. Electronic supplementary material The online version of this article (10.1186/s13059-018-1514-1) contains supplementary material, which is available to authorized users.

BACKGROUNDEpigenetic age acceleration (an older methylation age compared to chronological age) correlates strongly with various age-related morbidities and mortality. Chronic systemic inflammation is thought to be a hallmark of ageing but the relationship between an increased epigenetic age and this likely key phenotype of ageing has not yet been extensively investigated.METHODSWe modelled the trajectories of the inflammatory biomarkers C-reactive protein (CRP; measured using both a high- and low-sensitivity assay), and interleukin-6 (IL-6) over the 8th decade in the Lothian Birth Cohort 1936. We additionally investigated the association between CRP and imputed leukocyte counts. Using linear mixed models we examined the cross-sectional and longitudinal association between the inflammatory biomarkers and two measures of epigenetic age acceleration, derived from the Horvath and Hannum epigenetic clocks.RESULTSLow-sensitivity CRP declined, high-sensitivity CRP did not change, and IL-6 increased over time. CRP levels inversely associated with total counts of CD8+T cells and CD4+T cells, and positively associated with senescent CD8+T cells, plasmablasts and granulocytes. Cross-sectionally, the Hannum, but not the Horvath, measure of age acceleration was positively associated with low-sensitivity CRP, high-sensitivity CRP, IL-6 and a restricted measure of CRP (≤10mg/L) likely reflecting levels relevant to chronic inflammation.CONCLUSIONSWe found a divergent relationship between inflammation and immune system parameters in older age. We additionally report the Hannum measure of epigenetic age acceleration associated with an elevated inflammatory profile cross-sectionally, but not longitudinally.

Gene expression varies markedly across the menstrual cycle and expression levels for many genes are under genetic control. We analyzed gene expression and mapped expression quantitative trait loci (eQTLs) in endometrial tissue samples from 229 women and then analyzed the overlap of endometrial eQTL signals with genomic regions associated with endometriosis and other reproductive traits. We observed a total of 45,923 cis-eQTLs for 417 unique genes and 2,968 trans-eQTLs affecting 82 unique genes. Two eQTLs were located in known risk regions for endometriosis including LINC00339 on chromosome 1 and VEZT on chromosome 12 and there was evidence for eQTLs that may be target genes in genomic regions associated with other reproductive diseases. Dynamic changes in expression of individual genes across cycle include alterations in both mean expression and transcriptional silencing. Significant effects of cycle stage on mean expression levels were observed for (2,427/15,262) probes with detectable expression in at least 90% of samples and for (2,877/9,626) probes expressed in some, but not all samples. Pathway analysis supports similar biological control of both altered expression levels and transcriptional silencing. Taken together, these data identify strong genetic effects on genes with diverse functions in human endometrium and provide a platform for better understanding genetic effects on endometrial-related pathologies.

DNA methylation is associated with age. The deviation of age predicted from DNA methylation from actual age has been proposed as a biomarker for ageing. However, a better prediction of chronological age implies less opportunity for biological age. Here we used 13,661 samples (from blood and saliva) in the age range of 2 to 104 years from 14 cohorts measured on Illumina HumanMethylation450/EPIC arrays to perform prediction analyses. We show that increasing the sample size achieves a smaller prediction error and higher correlations in test datasets. We demonstrate that smaller prediction errors provide a limit to how much variation in biological ageing can be captured by methylation and provide evidence that age predictors from small samples are prone to confounding by cell composition. Our predictor shows a similar or better performance in non-blood tissues including saliva, endometrium, breast, liver, adipose and muscle, compared with Horvath’s across-tissue age predictor.

BackgroundGenome-wide DNA methylation (DNAm) profiling has allowed for the development of molecular predictors for a multitude of traits and diseases. Such predictors may be more accurate than the self-reported phenotypes, and could have clinical applications. Here, penalised regression models were used to develop DNAm predictors for body mass index (BMI), smoking status, alcohol consumption, and educational attainment in a cohort of 5,100 individuals. Using an independent test cohort comprising 906 individuals, the proportion of phenotypic variance explained in each trait was examined for DNAm-based and genetic predictors. Receiver operator characteristic curves were generated to investigate the predictive performance of DNAm-based predictors, using dichotomised phenotypes. The relationship between DNAm scores and all-cause mortality (n = 214 events) was assessed via Cox proportional-hazards models.ResultsThe DNAm-based predictors explained different proportions of the phenotypic variance for BMI (12%), smoking (60%), alcohol consumption (12%) and education (3%). The combined genetic and DNAm predictors explained 20% of the variance in BMI, 61% in smoking, 13% in alcohol consumption, and 6% in education. DNAm predictors for smoking, alcohol, and education but not BMI predicted mortality in univariate models. The predictors showed moderate discrimination of obesity (AUC=0.67) and alcohol consumption (AUC=0.75), and excellent discrimination of current smoking status (AUC=0.98). There was poorer discrimination of college-educated individuals (AUC=0.59).ConclusionsDNAm predictors correlate with lifestyle factors that are associated with health and mortality. They may supplement DNAm-based predictors of age to identify the lifestyle profiles of individuals and predict disease risk.List of abbreviationsDNAmDNA methylationBMIBody mass indexAUCArea under the curveCpGCytosine phosphate Guanine dinucleotideEWASEpigenome-wide association studyGS:SFHSGeneration Scotland: The Scottish family health studyLBC1936Lothian birth cohort 1936LASSOLeast absolute shrinkage and selector operatorHRHazard ratioCIConfidence intervalSTRADLStratifying resilience and depression longitudinally

BackgroundThe relationship between obesity and adverse health is well established, but little is known about the contribution of DNA methylation to obesity-related health outcomes. Additionally, it is of interest whether such contributions are independent of those attributed by the most widely used clinical measure of body mass – the Body Mass Index (BMI).MethodWe tested whether an epigenetic BMI score accounts for inter-individual variation in health-related, cognitive, psychosocial and lifestyle outcomes in the Lothian Birth Cohort 1936 (n=903). Weights for the epigenetic BMI score were derived using penalised regression on methylation data from unrelated Generation Scotland participants (n=2566).ResultsThe Epigenetic BMI score was associated with variables related to poor physical health (R2 ranges from 0.02-0.10), metabolic syndrome (R2 ranges from 0.01-0.09), lower crystallised intelligence (R2=0.01), lower health-related quality of life (R2=0.02), physical inactivity (R2=0.02), and social deprivation (R2=0.02). The epigenetic BMI score (per SD) was also associated with self-reported type 2 diabetes (OR 2.25, 95 % CI 1.74, 2.94), cardiovascular disease (OR 1.44, 95 % CI 1.23, 1.69) and high blood pressure (OR 1.21, 95% CI 1.13, 1.48; all at pConclusionsOur results show that regression models with epigenetic and phenotypic BMI scores as predictors account for a greater proportion of all outcome variables than either predictor alone, demonstrating independent and additive effects of epigenetic and phenotypic BMI scores.

Background Investigating the genetic and environmental causes of variation in genome-wide average DNA methylation (GWAM), a global methylation measure from the HumanMethylation450 array, might give a better understanding of genetic and environmental influences on methylation. Methods We measured GWAM for 2299 individuals aged 0 to 90 years from seven twin and/or family studies. We estimated familial correlations, modelled correlations with cohabitation history and fitted variance components models for GWAM. Results The correlation in GWAM for twin pairs was ∼0.8 at birth, decreased with age during adolescence and was constant at ∼0.4 throughout adulthood, with no evidence that twin pair correlations differed by zygosity. Non-twin first-degree relatives were correlated, from 0.17 [95% confidence interval (CI): 0.05–0.30] to 0.28 (95% CI: 0.08–0.48), except for middle-aged siblings (0.01, 95% CI: −0.10–0.12), and the correlation increased with time living together and decreased with time living apart. Spouse pairs were correlated in all studies, from 0.23 (95% CI: 0.3–0.43) to 0.31 (95% CI: 0.05–0.52), and the correlation increased with time living together. The variance explained by environmental factors shared by twins alone was 90% (95% CI: 74–95%) at birth, decreased in early life and plateaued at 28% (95% CI: 17–39%) in middle age and beyond. There was a cohabitation-related environmental component of variance. Conclusions GWAM is determined in utero by prenatal environmental factors, the effects of which persist throughout life. The variation of GWAM is also influenced by environmental factors shared by family members, as well as by individual-specific environmental factors.

Alzheimer’s disease (AD) is a public health priority for the 21st century. Risk reduction currently revolves around lifestyle changes with much research trying to elucidate the biological underpinnings. Using self-report of parental history of Alzheimer’s dementia for case ascertainment in a genome-wide association study of over 300,000 participants from UK Biobank (32,222 maternal cases, 16,613 paternal cases) and meta-analysing with published consortium data (n=74,046 with 25,580 cases across the discovery and replication analyses), six new AD-associated loci (P−8) are identified. Three contain genes relevant for AD and neurodegeneration: ADAM10, ADAMTS4, and ACE. Suggestive loci include drug targets such as VKORC1 (warfarin dose) and BZRAP1 (benzodiazepine receptor). We report evidence that association of SNPs and AD at the PVR gene is potentially mediated by both gene expression and DNA methylation in the prefrontal cortex. Our discovered loci may help to elucidate the biological mechanisms underlying AD and, given that many are existing drug targets for other diseases and disorders, warrant further exploration for potential precision medicine applications.

Age-related changes in DNA methylation have been observed in many cross-sectional studies, but longitudinal evidence is still very limited. Here, we aimed to characterize longitudinal age-related methylation patterns (Illumina HumanMethylation450 array) using 1011 blood samples collected from 385 old Swedish twins (mean age of 69 at baseline) up to five times over 20 years. We identified 1316 age-associated methylation sites (p−7) using a longitudinal epigenome-wide association study design. We measured how estimated cellular compositions changed with age and how much they confounded the age effect. We validated the results in two independent longitudinal cohorts, where 118 CpGs were replicated in PIVUS (p−5) and 594 were replicated in LBC (p−5). Functional annotation of age-associated CpGs showed enrichment in CCCTC-binding factor (CTCF) and other unannotated transcription factor binding sites. We further investigated genetic influences on methylation (methylation quantitative trait loci) and found no interaction between age and genetic effects in the 1316 age-associated CpGs. Moreover, in the same CpGs, methylation differences within twin pairs increased over time, where monozygotic twins had smaller intra-pair differences than dizygotic twins. We show that age-related methylation changes persist in a longitudinal perspective, and are fairly stable across cohorts. Moreover, the changes are under genetic influence, although this effect is independent of age. In addition, inter-individual methylation variations increase over time, especially in age-associated CpGs, indicating the increase of environmental contributions on DNA methylation with age.

Genome-wide association studies have identified hundreds of genetic variants associated with blood pressure (BP), but sequence variation accounts for a small fraction of the phenotypic variance. Epigenetic changes may alter the expression of genes involved in BP regulation and explain part of the missing heritability. We therefore conducted a two-stage meta-analysis of the cross-sectional associations of systolic and diastolic BP with blood-derived genome-wide DNA methylation measured on the Infinium HumanMethylation450 BeadChip in 17,010 individuals of European, African American, and Hispanic ancestry. Of 31 discovery-stage cytosine-phosphate-guanine (CpG) dinucleotides, 13 replicated after Bonferroni correction (discovery: N = 9,828, p < 1.0× 10-7; replication: N = 7,182, p 30%) and independent of known BP genetic variants, explaining an additional 1.4% and 2.0% of the interindividual variation in systolic and diastolic BP, respectively. Bidirectional Mendelian randomization among up to 4,513 individuals of European ancestry from 4 cohorts suggested that methylation at cg08035323 (TAF1B-YWHAQ) influences BP, while BP influences methylation at cg00533891 (ZMIZ1), cg00574958 (CPT1A), and cg02711608 (SLC1A5). Gene expression analyses further identified six genes (TSPAN2, SLC7A11, UNC93B1, CPT1A, PTMS, and LPCAT3) with evidence of triangular associations between methylation, gene expression, and BP. Additional integrative Mendelian randomization analyses of gene expression and DNA methylation suggested that the expression of TSPAN2 is a putative mediator of association between DNA methylation at cg23999170 and BP. These findings suggest that heritable DNA methylation plays a role in regulating BP independently of previously known genetic variants.

DNA methylation age is an accurate biomarker of chronological age and predicts lifespan, but its underlying molecular mechanisms are unknown. In this genome-wide association study of 9907 individuals, we find gene variants mapping to five loci associated with intrinsic epigenetic age acceleration (IEAA) and gene variants in three loci associated with extrinsic epigenetic age acceleration (EEAA). Mendelian randomization analysis suggests causal influences of menarche and menopause on IEAA and lipoproteins on IEAA and EEAA. Variants associated with longer leukocyte telomere length (LTL) in the telomerase reverse transcriptase gene (TERT) paradoxically confer higher IEAA (P, Epigenetic clocks based on DNA methylation levels are estimators of chronological age. Here, the authors perform a GWAS of epigenetic aging rates in blood and find SNP variants in the TERT locus associated with increased intrinsic epigenetic age are also associated with longer telomeres.

DNA methylation age is an accurate biomarker of chronological age and predicts lifespan, but its underlying molecular mechanisms are unknown. In this genome-wide association study of 9,907 individuals, we found gene variants mapping to five loci associated with intrinsic epigenetic age acceleration (IEAA) and gene variants in 3 loci associated extrinsic epigenetic age acceleration (EEAA). Mendelian randomization analysis suggested causal influences of menarche and menopause on IEAA and lipid levels on IEAA and EEAA. Variants associated with longer leukocyte telomere length (LTL) in the telomerase reverse transcriptase gene (TERT) locus at 5p15.33 confer higher IEAA (P-11). Causal modelling indicatesTERT-specific and independent effects on LTL and IEAA. Experimental hTERT expression in primary human fibroblasts engenders a linear increase in DNA methylation age with cell population doubling number. Together, these findings indicate a critical role for hTERT in regulating the DNA methylation clock, in addition to its established role of compensating for cell replication-dependent telomere shortening.

Estimation of the joint distribution of effect size and minor allele frequency (MAF) for genetic variants is important for understanding the genetic basis of complex trait variation and can be used to detect signature of natural selection. We develop a Bayesian mixed linear model that simultaneously estimates SNP-based heritability, polygenicity (i.e. the proportion of SNPs with nonzero effects) and the relationship between effect size and MAF for complex traits in conventionally unrelated individuals using genome-wide SNP data. We apply the method to 28 complex traits in the UK Biobank data (N = 126,752), and show that on average across 28 traits, 6% of SNPs have nonzero effects, which in total explain 22% of phenotypic variance. We detect significant (p < 0.05/28 =1.8×10−3) signatures of natural selection for 23 out of 28 traits including reproductive, cardiovascular, and anthropometric traits, as well as educational attainment. We further apply the method to 27,869 gene expression traits (N = 1,748), and identify 30 genes that show significant (p < 2.3×10−6) evidence of natural selection. All the significant estimates of the relationship between effect size and MAF in either complex traits or gene expression traits are consistent with a model of negative selection, as confirmed by forward simulation. We conclude that natural selection acts pervasively on human complex traits shaping genetic variation in the form of negative selection.

Several studies have investigated the relationship between genetic variation and DNA methylation with respect to type 2 diabetes, but it is unknown if DNA methylation is a mediator in the disease pathway or if it is altered in response to disease state. This study uses genotypic information as a causal anchor to help decipher the likely role of DNA methylation measured in peripheral blood in the etiology of type 2 diabetes. Illumina HumanMethylation450 BeadChip data were generated on 1,018 young individuals from the Avon Longitudinal Study of Parents and Children (ALSPAC) cohort. In stage 1, 118 unique associations between published type 2 diabetes single nucleotide polymorphisms (SNPs) and genome-wide methylation (methylation quantitative trait loci [mQTLs]) were identified. In stage 2, a further 226 mQTLs were identified between 202 additional independent non–type 2 diabetes SNPs and CpGs identified in stage 1. Where possible, associations were replicated in independent cohorts of similar age. We discovered that around half of known type 2 diabetes SNPs are associated with variation in DNA methylation and postulated that methylation could either be on a causal pathway to future disease or could be a noncausal biomarker. For one locus (KCNQ1), we were able to provide further evidence that methylation is likely to be on the causal pathway to disease in later life.

We develop a Bayesian mixed linear model that simultaneously estimates single-nucleotide polymorphism (SNP)-based heritability, polygenicity (proportion of SNPs with nonzero effects), and the relationship between SNP effect size and minor allele frequency for complex traits in conventionally unrelated individuals using genome-wide SNP data. We apply the method to 28 complex traits in the UK Biobank data (N = 126,752) and show that on average, 6% of SNPs have nonzero effects, which in total explain 22% of phenotypic variance. We detect significant (P 0.05/28) signatures of natural selection in the genetic architecture of 23 traits, including reproductive, cardiovascular, and anthropometric traits, as well as educational attainment. The significant estimates of the relationship between effect size and minor allele frequency in complex traits are consistent with a model of negative (or purifying) selection, as confirmed by forward simulation. We conclude that negative selection acts pervasively on the genetic variants associated with human complex traits.

Background: Gene discovery has provided remarkable biological insights into amyotrophic lateral sclerosis (ALS). One challenge for clinical application of genetic testing is critical evaluation of the significance of reported variants. Methods: We use whole exome sequencing (WES) to develop a clinically relevant approach to identify a subset of ALS patients harboring likely pathogenic mutations. In parallel, we assess if DNA methylation can be used to screen for pathogenicity of novel variants since a methylation signature has been shown to associate with the pathogenic C9orf72 expansion, but has not been explored for other ALS mutations. Australian patients identified with ALS-relevant variants were cross-checked with population databases and case reports to critically assess whether they were “likely causal,” “uncertain significance,” or “unlikely causal.”. Results: Published ALS variants were identified in >10% of patients; however, in only 3% of patients (4/120) could these be confidently considered pathogenic (in SOD1 and TARDBP). We found no evidence for a differential DNA methylation signature in these mutation carriers. Conclusions: The use of WES in a typical ALS clinic demonstrates a critical approach to variant assessment with the capability to combine cohorts to enhance the largely unknown genetic basis of ALS Refereed/Peer-reviewed

The epigenome has been shown to be influenced by biological factors, such as disease status, and environmental factors, such as smoking, alcohol consumption, and body mass index. Although there is a widespread perception that environmental influences on the epigenome are pervasive and profound, there has been little evidence to date in humans with respect to environmental factors that are biologically distal. Here, we provide evidence on the associations between epigenetic modifications—in our case, CpG methylation—and educational attainment (EA), a biologically distal environmental factor that is arguably among of the most important life-shaping experiences for individuals. Specifically, we report the results of an epigenome-wide association study meta-analysis of EA based on data from 27 cohort studies with a total of 10,767 individuals. While we find that 9 CpG probes are significantly associated with EA, only two remain associated when we restrict the sample to never-smokers. These two are known to be strongly associated with maternal smoking during pregnancy, and thus their association with EA could be due to correlation between EA and maternal smoking. Moreover, their effect sizes on EA are far smaller than the known associations between CpG probes and biologically proximal environmental factors. Two analyses that combine the effects of many probes—polygenic methylation score and epigenetic-clock analyses—both suggest small associations with EA. If our findings regarding EA can be generalized to other biologically distal environmental factors, then they cast doubt on the hypothesis that such factors have large effects on the epigenome.

Background The link between DNA methylation, obesity, and adiposity-related diseases in the general population remains uncertain. Methods and Findings We conducted an association study of body mass index (BMI) and differential methylation for over 400,000 CpGs assayed by microarray in whole-blood-derived DNA from 3,743 participants in the Framingham Heart Study and the Lothian Birth Cohorts, with independent replication in three external cohorts of 4,055 participants. We examined variations in whole blood gene expression and conducted Mendelian randomization analyses to investigate the functional and clinical relevance of the findings. We identified novel and previously reported BMI-related differential methylation at 83 CpGs that replicated across cohorts; BMI-related differential methylation was associated with concurrent changes in the expression of genes in lipid metabolism pathways. Genetic instrumental variable analysis of alterations in methylation at one of the 83 replicated CpGs, cg11024682 (intronic to sterol regulatory element binding transcription factor 1 [SREBF1]), demonstrated links to BMI, adiposity-related traits, and coronary artery disease. Independent genetic instruments for expression of SREBF1 supported the findings linking methylation to adiposity and cardiometabolic disease. Methylation at a substantial proportion (16 of 83) of the identified loci was found to be secondary to differences in BMI. However, the cross-sectional nature of the data limits definitive causal determination. Conclusions We present robust associations of BMI with differential DNA methylation at numerous loci in blood cells. BMI-related DNA methylation and gene expression provide mechanistic insights into the relationship between DNA methylation, obesity, and adiposity-related diseases., Daniel Levy and colleagues examine mechanisms that could link body mass index to cardiovascular diseases via gene expression., Author Summary Why Was This Study Done? Genetic sequence variants explain only a modest proportion of the variation in body mass index (BMI) and cardiometabolic disease in the general population. There is limited understanding of the link of DNA methylation—a well-characterized epigenetic modification—with BMI and cardiometabolic disease in the general population. What Did the Researchers Do and Find? We conducted a cross-sectional analysis of the association of BMI with leukocyte DNA methylation at over 400,000 sites in the genome among 7,798 community-dwelling adults. We identified associations between BMI and methylation at 83 replicated sites (including 50 novel sites) and concurrent differences in expression in whole blood of genes overrepresented in lipid metabolism pathways. Using genetic sequence variants to model exposure to differential DNA methylation and tissue-specific gene expression, we found differential methylation and expression of SREBF1 to be implicated in BMI, adiposity-related traits, and coronary artery disease. Using genetic sequence variants to model exposure to differences in BMI, we found a substantial proportion of the differentially methylated sites (16 of 83) to be downstream of BMI. What Do These Findings Mean? Evidence is accumulating that epigenetic modifications, such as DNA methylation, are related to obesity-related diseases in the general population. We provide support for a role of genomic regulation of a lipid metabolism transcription factor, SREBF1, in adiposity and coronary artery disease. Mendelian randomization approaches can help prioritize relevant loci for future functional studies, but the cross-sectional observational nature of our study limits definitive causal inference.