Epigenome-wide association study of body mass index, and the adverse outcomes of adiposity.

Approximately 1.5 billion people worldwide are overweight or affected by obesity, and are at risk of developing type 2 diabetes, cardiovascular disease and related metabolic and inflammatory disturbances. Although the mechanisms linking adiposity to associated clinical conditions are poorly understood, recent studies suggest that adiposity may influence DNA methylation, a key regulator of gene expression and molecular phenotype. Here we use epigenome-wide association to show that body mass index (BMI; a key measure of adiposity) is associated with widespread changes in DNA methylation (187 genetic loci with P < 1 × 10-7, range P = 9.2 × 10-8 to 6.0 × 10-46; n = 10,261 samples). Genetic association analyses demonstrate that the alterations in DNA methylation are predominantly the consequence of adiposity, rather than the cause. We find that methylation loci are enriched for functional genomic features in multiple tissues (P < 0.05), and show that sentinel methylation markers identify gene expression signatures at 38 loci (P < 9.0 × 10-6, range P = 5.5 × 10-6 to 6.1 × 10-35, n = 1,785 samples). The methylation loci identify genes involved in lipid and lipoprotein metabolism, substrate transport and inflammatory pathways. Finally, we show that the disturbances in DNA methylation predict future development of type 2 diabetes (relative risk per 1 standard deviation increase in methylation risk score: 2.3 (2.07-2.56); P = 1.1 × 10-54). Our results provide new insights into the biologic pathways influenced by adiposity, and may enable development of new strategies for prediction and prevention of type 2 diabetes and other adverse clinical consequences of obesity.

Genome-Wide Association Study Identifies Two Novel Loci Associated with Female Stress and Urgency Urinary Incontinence.

PURPOSE: Genome-wide association studies have not identified replicable genetic risk loci for stress or urgency urinary incontinence. MATERIALS AND METHODS: We carried out a discovery stage, case control, genome-wide association study in 3 independent discovery cohorts of European women (8,979) for stress incontinence, urgency incontinence, and any incontinence phenotypes. We conducted replication in 6 additional studies of European ancestry (4,069). We collected bladder biopsies from women with incontinence (50) to further investigate bladder expression of implicated genes and pathways and used symptom questionnaires for phenotyping. We conducted meta-analyses using inverse variance fixed effects models and whole transcriptome analyses using Affymetrix® arrays with replication with TaqMan® polymerase chain reaction. RESULTS: In the discovery stage, we identified 16 single nucleotide polymorphisms genotyped or imputed at 5 loci that reached genome-wide significance (p <5×10-8). In replication, rs138724718 on chromosome 2 near the macrophage receptor with collagenous structure (MARCO) gene (replication p=0.003) was associated with stress incontinence. In addition, rs34998271 on chromosome 6 near the endothelin 1 (EDN1) gene (replication p=0.0008) was associated with urgency incontinence. In combined meta-analyses of discovery and replication cohorts, associations with genome-wide significance for these 2 single nucleotide polymorphisms were confirmed. Transcriptomics analyses showed differential expression of 7 of 19 genes in the endothelin pathway between stress and urgency incontinence (p <0.0001). CONCLUSIONS: We uncovered 2 new risk loci near the genes endothelin 1 (EDN1), associated with urgency incontinence, and macrophage receptor with collagenous structure (MARCO), associated with stress incontinence. These loci are biologically plausible given their roles in smooth muscle contraction and innate host defense, respectively.

Alcohol consumption is associated with widespread changes in blood DNA methylation: Analysis of cross-sectional and longitudinal data.

DNA methylation may be one of the mechanisms by which alcohol consumption is associated with the risk of disease. We conducted a large-scale, cross-sectional, genome-wide DNA methylation association study of alcohol consumption and a longitudinal analysis of repeated measurements taken several years apart. Using the Illumina HumanMethylation450 BeadChip, DNA methylation was measured in blood samples from 5606 Melbourne Collaborative Cohort Study (MCCS) participants. For 1088 of them, these measures were repeated using blood samples collected a median of 11 years later. Associations between alcohol intake and blood DNA methylation were assessed using linear mixed-effects regression models. Independent data from the London Life Sciences Prospective Population (LOLIPOP) (N = 4042) and Cooperative Health Research in the Augsburg Region (KORA) (N = 1662) cohorts were used to replicate associations discovered in the MCCS. Cross-sectional analyses identified 1414 CpGs associated with alcohol intake at P < 10-7 , 1243 of which had not been reported previously. Of these novel associations, 1078 were replicated (P < .05) using LOLIPOP and KORA data. Using the MCCS data, we also replicated 403 of 518 previously reported associations. Interaction analyses suggested that associations were stronger for women, non-smokers, and participants genetically predisposed to consume less alcohol. Of the 1414 CpGs, 530 were differentially methylated (P < .05) in former compared with current drinkers. Longitudinal associations between the change in alcohol intake and the change in methylation were observed for 513 of the 1414 cross-sectional associations. Our study indicates that alcohol intake is associated with widespread changes in DNA methylation across the genome. Longitudinal analyses showed that the methylation status of alcohol-associated CpGs may change with alcohol consumption changes in adulthood.

Whole-Genome Sequencing Coupled to Imputation Discovers Genetic Signals for Anthropometric Traits.

Deep sequence-based imputation can enhance the discovery power of genome-wide association studies by assessing previously unexplored variation across the common- and low-frequency spectra. We applied a hybrid whole-genome sequencing (WGS) and deep imputation approach to examine the broader allelic architecture of 12 anthropometric traits associated with height, body mass, and fat distribution in up to 267,616 individuals. We report 106 genome-wide significant signals that have not been previously identified, including 9 low-frequency variants pointing to functional candidates. Of the 106 signals, 6 are in genomic regions that have not been implicated with related traits before, 28 are independent signals at previously reported regions, and 72 represent previously reported signals for a different anthropometric trait. 71% of signals reside within genes and fine mapping resolves 23 signals to one or two likely causal variants. We confirm genetic overlap between human monogenic and polygenic anthropometric traits and find signal enrichment in cis expression QTLs in relevant tissues. Our results highlight the potential of WGS strategies to enhance biologically relevant discoveries across the frequency spectrum.

Serum metabolic signatures of coronary and carotid atherosclerosis and subsequent cardiovascular disease.

AIMS: To characterize serum metabolic signatures associated with atherosclerosis in the coronary or carotid arteries and subsequently their association with incident cardiovascular disease (CVD). METHODS AND RESULTS: We used untargeted one-dimensional (1D) serum metabolic profiling by proton nuclear magnetic resonance spectroscopy (1H NMR) among 3867 participants from the Multi-Ethnic Study of Atherosclerosis (MESA), with replication among 3569 participants from the Rotterdam and LOLIPOP studies. Atherosclerosis was assessed by coronary artery calcium (CAC) and carotid intima-media thickness (IMT). We used multivariable linear regression to evaluate associations between NMR features and atherosclerosis accounting for multiplicity of comparisons. We then examined associations between metabolites associated with atherosclerosis and incident CVD available in MESA and Rotterdam and explored molecular networks through bioinformatics analyses. Overall, 30 1H NMR measured metabolites were associated with CAC and/or IMT, P = 1.3 × 10-14 to 1.0 × 10-6 (discovery) and P = 5.6 × 10-10 to 1.1 × 10-2 (replication). These associations were substantially attenuated after adjustment for conventional cardiovascular risk factors. Metabolites associated with atherosclerosis revealed disturbances in lipid and carbohydrate metabolism, branched chain, and aromatic amino acid metabolism, as well as oxidative stress and inflammatory pathways. Analyses of incident CVD events showed inverse associations with creatine, creatinine, and phenylalanine, and direct associations with mannose, acetaminophen-glucuronide, and lactate as well as apolipoprotein B (P < 0.05). CONCLUSION: Metabolites associated with atherosclerosis were largely consistent between the two vascular beds (coronary and carotid arteries) and predominantly tag pathways that overlap with the known cardiovascular risk factors. We present an integrated systems network that highlights a series of inter-connected pathways underlying atherosclerosis.

Vaginal dysbiosis increases risk of preterm fetal membrane rupture, neonatal sepsis and is exacerbated by erythromycin.

BACKGROUND: Preterm prelabour rupture of the fetal membranes (PPROM) precedes 30% of preterm births and is a risk factor for early onset neonatal sepsis. As PPROM is strongly associated with ascending vaginal infection, prophylactic antibiotics are widely used. The evolution of vaginal microbiota compositions associated with PPROM and the impact of antibiotics on bacterial compositions are unknown. METHODS: We prospectively assessed vaginal microbiota prior to and following PPROM using MiSeq-based sequencing of 16S rRNA gene amplicons and examined the impact of erythromycin prophylaxis on bacterial load and community structures. RESULTS: In contrast to pregnancies delivering at term, vaginal dysbiosis characterised by Lactobacillus spp. depletion was present prior to the rupture of fetal membranes in approximately a third of cases (0% vs. 27%, P = 0.026) and persisted following membrane rupture (31%, P = 0.005). Vaginal dysbiosis was exacerbated by erythromycin treatment (47%, P = 0.00009) particularly in women initially colonised by Lactobacillus spp. Lactobacillus depletion and increased relative abundance of Sneathia spp. were associated with subsequent funisitis and early onset neonatal sepsis. CONCLUSIONS: Our data show that vaginal microbiota composition is a risk factor for subsequent PPROM and is associated with adverse short-term maternal and neonatal outcomes. This highlights vaginal microbiota as a potentially modifiable antenatal risk factor for PPROM and suggests that routine use of erythromycin for PPROM be re-examined.

Pooled sequencing of 531 genes in inflammatory bowel disease identifies an associated rare variant in BTNL2 and implicates other immune related genes.

The contribution of rare coding sequence variants to genetic susceptibility in complex disorders is an important but unresolved question. Most studies thus far have investigated a limited number of genes from regions which contain common disease associated variants. Here we investigate this in inflammatory bowel disease by sequencing the exons and proximal promoters of 531 genes selected from both genome-wide association studies and pathway analysis in pooled DNA panels from 474 cases of Crohn's disease and 480 controls. 80 variants with evidence of association in the sequencing experiment or with potential functional significance were selected for follow up genotyping in 6,507 IBD cases and 3,064 population controls. The top 5 disease associated variants were genotyped in an extension panel of 3,662 IBD cases and 3,639 controls, and tested for association in a combined analysis of 10,147 IBD cases and 7,008 controls. A rare coding variant p.G454C in the BTNL2 gene within the major histocompatibility complex was significantly associated with increased risk for IBD (p = 9.65x10-10, OR = 2.3[95% CI = 1.75-3.04]), but was independent of the known common associated CD and UC variants at this locus. Rare (<1%) and low frequency (1-5%) variants in 3 additional genes showed suggestive association (p<0.005) with either an increased risk (ARIH2 c.338-6C>T) or decreased risk (IL12B p.V298F, and NICN p.H191R) of IBD. These results provide additional insights into the involvement of the inhibition of T cell activation in the development of both sub-phenotypes of inflammatory bowel disease. We suggest that although rare coding variants may make a modest overall contribution to complex disease susceptibility, they can inform our understanding of the molecular pathways that contribute to pathogenesis.

NFAT5 and SLC4A10 Loci Associate with Plasma Osmolality.

Disorders of water balance, an excess or deficit of total body water relative to body electrolyte content, are common and ascertained by plasma hypo- or hypernatremia, respectively. We performed a two-stage genome-wide association study meta-analysis on plasma sodium concentration in 45,889 individuals of European descent (stage 1 discovery) and 17,637 additional individuals of European descent (stage 2 replication), and a transethnic meta-analysis of replicated single-nucleotide polymorphisms in 79,506 individuals (63,526 individuals of European descent, 8765 individuals of Asian Indian descent, and 7215 individuals of African descent). In stage 1, we identified eight loci associated with plasma sodium concentration at P<5.0 × 10-6 Of these, rs9980 at NFAT5 replicated in stage 2 meta-analysis (P=3.1 × 10-5), with combined stages 1 and 2 genome-wide significance of P=5.6 × 10-10 Transethnic meta-analysis further supported the association at rs9980 (P=5.9 × 10-12). Additionally, rs16846053 at SLC4A10 showed nominally, but not genome-wide, significant association in combined stages 1 and 2 meta-analysis (P=6.7 × 10-8). NFAT5 encodes a ubiquitously expressed transcription factor that coordinates the intracellular response to hypertonic stress but was not previously implicated in the regulation of systemic water balance. SLC4A10 encodes a sodium bicarbonate transporter with a brain-restricted expression pattern, and variant rs16846053 affects a putative intronic NFAT5 DNA binding motif. The lead variants for NFAT5 and SLC4A10 are cis expression quantitative trait loci in tissues of the central nervous system and relevant to transcriptional regulation. Thus, genetic variation in NFAT5 and SLC4A10 expression and function in the central nervous system may affect the regulation of systemic water balance.

Improving Visualization and Interpretation of Metabolome-Wide Association Studies: An Application in a Population-Based Cohort Using Untargeted 1H NMR Metabolic Profiling.

1H NMR spectroscopy of biofluids generates reproducible data allowing detection and quantification of small molecules in large population cohorts. Statistical models to analyze such data are now well-established, and the use of univariate metabolome wide association studies (MWAS) investigating the spectral features separately has emerged as a computationally efficient and interpretable alternative to multivariate models. The MWAS rely on the accurate estimation of a metabolome wide significance level (MWSL) to be applied to control the family wise error rate. Subsequent interpretation requires efficient visualization and formal feature annotation, which, in-turn, call for efficient prioritization of spectral variables of interest. Using human serum 1H NMR spectroscopic profiles from 3948 participants from the Multi-Ethnic Study of Atherosclerosis (MESA), we have performed a series of MWAS for serum levels of glucose. We first propose an extension of the conventional MWSL that yields stable estimates of the MWSL across the different model parameterizations and distributional features of the outcome. We propose both efficient visualization methods and a strategy based on subsampling and internal validation to prioritize the associations. Our work proposes and illustrates practical and scalable solutions to facilitate the implementation of the MWAS approach and improve interpretation in large cohort studies.

Workflow for Integrated Processing of Multicohort Untargeted 1H NMR Metabolomics Data in Large-Scale Metabolic Epidemiology.

Large-scale metabolomics studies involving thousands of samples present multiple challenges in data analysis, particularly when an untargeted platform is used. Studies with multiple cohorts and analysis platforms exacerbate existing problems such as peak alignment and normalization. Therefore, there is a need for robust processing pipelines that can ensure reliable data for statistical analysis. The COMBI-BIO project incorporates serum from ∼8000 individuals, in three cohorts, profiled by six assays in two phases using both 1H NMR and UPLC-MS. Here we present the COMBI-BIO NMR analysis pipeline and demonstrate its fitness for purpose using representative quality control (QC) samples. NMR spectra were first aligned and normalized. After eliminating interfering signals, outliers identified using Hotelling's T2 were removed and a cohort/phase adjustment was applied, resulting in two NMR data sets (CPMG and NOESY). Alignment of the NMR data was shown to increase the correlation-based alignment quality measure from 0.319 to 0.391 for CPMG and from 0.536 to 0.586 for NOESY, showing that the improvement was present across both large and small peaks. End-to-end quality assessment of the pipeline was achieved using Hotelling's T2 distributions. For CPMG spectra, the interquartile range decreased from 1.425 in raw QC data to 0.679 in processed spectra, while the corresponding change for NOESY spectra was from 0.795 to 0.636, indicating an improvement in precision following processing. PCA indicated that gross phase and cohort differences were no longer present. These results illustrate that the pipeline produces robust and reproducible data, successfully addressing the methodological challenges of this large multifaceted study.

Integrative genomic analyses in adipocytes implicate DNA methylation in human obesity and diabetes.

DNA methylation variations are prevalent in human obesity but evidence of a causative role in disease pathogenesis is limited. Here, we combine epigenome-wide association and integrative genomics to investigate the impact of adipocyte DNA methylation variations in human obesity. We discover extensive DNA methylation changes that are robustly associated with obesity (N = 190 samples, 691 loci in subcutaneous and 173 loci in visceral adipocytes, P < 1 × 10-7). We connect obesity-associated methylation variations to transcriptomic changes at >500 target genes, and identify putative methylation-transcription factor interactions. Through Mendelian Randomisation, we infer causal effects of methylation on obesity and obesity-induced metabolic disturbances at 59 independent loci. Targeted methylation sequencing, CRISPR-activation and gene silencing in adipocytes, further identifies regional methylation variations, underlying regulatory elements and novel cellular metabolic effects. Our results indicate DNA methylation is an important determinant of human obesity and its metabolic complications, and reveal mechanisms through which altered methylation may impact adipocyte functions.

Genetic variation influencing DNA methylation provides insights into molecular mechanisms regulating genomic function.

We determined the relationships between DNA sequence variation and DNA methylation using blood samples from 3,799 Europeans and 3,195 South Asians. We identify 11,165,559 SNP-CpG associations (methylation quantitative trait loci (meQTL), P < 10-14), including 467,915 meQTL that operate in trans. The meQTL are enriched for functionally relevant characteristics, including shared chromatin state, High-throuhgput chromosome conformation interaction, and association with gene expression, metabolic variation and clinical traits. We use molecular interaction and colocalization analyses to identify multiple nuclear regulatory pathways linking meQTL loci to phenotypic variation, including UBASH3B (body mass index), NFKBIE (rheumatoid arthritis), MGA (blood pressure) and COMMD7 (white cell counts). For rs6511961 , chromatin immunoprecipitation followed by sequencing (ChIP-seq) validates zinc finger protein (ZNF)333 as the likely trans acting effector protein. Finally, we used interaction analyses to identify population- and lineage-specific meQTL, including rs174548 in FADS1, with the strongest effect in CD8+ T cells, thus linking fatty acid metabolism with immune dysregulation and asthma. Our study advances understanding of the potential pathways linking genetic variation to human phenotype.

DNA methylation signature of chronic low-grade inflammation and its role in cardio-respiratory diseases.

We performed a multi-ethnic Epigenome Wide Association study on 22,774 individuals to describe the DNA methylation signature of chronic low-grade inflammation as measured by C-Reactive protein (CRP). We find 1,511 independent differentially methylated loci associated with CRP. These CpG sites show correlation structures across chromosomes, and are primarily situated in euchromatin, depleted in CpG islands. These genomic loci are predominantly situated in transcription factor binding sites and genomic enhancer regions. Mendelian randomization analysis suggests altered CpG methylation is a consequence of increased blood CRP levels. Mediation analysis reveals obesity and smoking as important underlying driving factors for changed CpG methylation. Finally, we find that an activated CpG signature significantly increases the risk for cardiometabolic diseases and COPD.

Exome localization of complex disease association signals.

BACKGROUND: Genome-wide association studies (GWAS) of common diseases have had a tremendous impact on genetic research over the last five years; the field is now moving from microarray-based technology towards next-generation sequencing. To evaluate the potential of association studies for complex diseases based on exome sequencing we analysed the distribution of association signal with respect to protein-coding genes based on GWAS data for seven diseases from the Wellcome Trust Case Control Consortium. RESULTS: We find significant concentration of association signal in exons and genes for Crohn's Disease, Type 1 Diabetes and Bipolar Disorder, but also observe enrichment from up to 40 kilobases upstream to 40 kilobases downstream of protein-coding genes for Crohn's Disease and Type 1 Diabetes; the exact extent of the distribution is disease dependent. CONCLUSIONS: Our work suggests that exome sequencing may be a feasible approach to find genetic variation associated with complex disease. Extending the exome sequencing to include flanking regions therefore promises further improvement of covering disease-relevant variants.

Protein-protein interaction databases: keeping up with growing interactomes.

Over the past few years, the number of known protein-protein interactions has increased substantially. To make this information more readily available, a number of publicly available databases have set out to collect and store protein-protein interaction data. Protein-protein interactions have been retrieved from six major databases, integrated and the results compared. The six databases (the Biological General Repository for Interaction Datasets [BioGRID], the Molecular INTeraction database [MINT], the Biomolecular Interaction Network Database [BIND], the Database of Interacting Proteins [DIP], the IntAct molecular interaction database [IntAct] and the Human Protein Reference Database [HPRD]) differ in scope and content; integration of all datasets is non-trivial owing to differences in data annotation. With respect to human protein-protein interaction data, HPRD seems to be the most comprehensive. To obtain a complete dataset, however, interactions from all six databases have to be combined. To overcome this limitation, meta-databases such as the Agile Protein Interaction Database (APID) offer access to integrated protein-protein interaction datasets, although these also currently have certain restrictions.

Epigenetic Link Between Statin Therapy and Type 2 Diabetes.

OBJECTIVE: To investigate the role of epigenetics in statins' diabetogenic effect comparing DNA methylation (DNAm) between statin users and nonusers in an epigenome-wide association study in blood. RESEARCH DESIGN AND METHODS: Five cohort studies' participants (n = 8,270) were classified as statin users when they were on statin therapy at the time of DNAm assessment with Illumina 450K or EPIC array or noncurrent users otherwise. Associations of DNAm with various outcomes like incident type 2 diabetes, plasma glucose, insulin, and insulin resistance (HOMA of insulin resistance [HOMA-IR]) as well as with gene expression were investigated. RESULTS: Discovery (n = 6,820) and replication (n = 1,450) phases associated five DNAm sites with statin use: cg17901584 (1.12 × 10-25 [DHCR24]), cg10177197 (3.94 × 10-08 [DHCR24]), cg06500161 (2.67 × 10-23 [ABCG1]), cg27243685 (6.01 × 10-09 [ABCG1]), and cg05119988 (7.26 × 10-12 [SC4MOL]). Two sites were associated with at least one glycemic trait or type 2 diabetes. Higher cg06500161 methylation was associated with higher fasting glucose, insulin, HOMA-IR, and type 2 diabetes (odds ratio 1.34 [95% CI 1.22, 1.47]). Mediation analyses suggested that ABCG1 methylation partially mediates the effect of statins on high insulin and HOMA-IR. Gene expression analyses showed that statin exposure and ABCG1 methylation were associated with ABCG1 downregulation, suggesting epigenetic regulation of ABCG1 expression. Further, outcomes insulin and HOMA-IR were significantly associated with ABCG1 expression. CONCLUSIONS: This study sheds light on potential mechanisms linking statins with type 2 diabetes risk, providing evidence on DNAm partially mediating statins' effects on insulin traits. Further efforts shall disentangle the molecular mechanisms through which statins may induce DNAm changes, potentially leading to ABCG1 epigenetic regulation.

Epigenome-Wide Association Study of Incident Type 2 Diabetes in a British Population: EPIC-Norfolk Study.

Epigenetic changes may contribute substantially to risks of diseases of aging. Previous studies reported seven methylation variable positions (MVPs) robustly associated with incident type 2 diabetes mellitus (T2DM). However, their causal roles in T2DM are unclear. In an incident T2DM case-cohort study nested within the population-based European Prospective Investigation into Cancer and Nutrition (EPIC)-Norfolk cohort, we used whole blood DNA collected at baseline, up to 11 years before T2DM onset, to investigate the role of methylation in the etiology of T2DM. We identified 15 novel MVPs with robust associations with incident T2DM and robustly confirmed three MVPs identified previously (near to TXNIP, ABCG1, and SREBF1). All 18 MVPs showed directionally consistent associations with incident and prevalent T2DM in independent studies. Further conditional analyses suggested that the identified epigenetic signals appear related to T2DM via glucose and obesity-related pathways acting before the collection of baseline samples. We integrated genome-wide genetic data to identify methylation-associated quantitative trait loci robustly associated with 16 of the 18 MVPs and found one MVP, cg00574958 at CPT1A, with a possible direct causal role in T2DM. None of the implicated genes were previously highlighted by genetic association studies, suggesting that DNA methylation studies may reveal novel biological mechanisms involved in tissue responses to glycemia.

Association of maternal prenatal smoking GFI1-locus and cardio-metabolic phenotypes in 18,212 adults.

BACKGROUND: DNA methylation at the GFI1-locus has been repeatedly associated with exposure to smoking from the foetal period onwards. We explored whether DNA methylation may be a mechanism that links exposure to maternal prenatal smoking with offspring's adult cardio-metabolic health. METHODS: We meta-analysed the association between DNA methylation at GFI1-locus with maternal prenatal smoking, adult own smoking, and cardio-metabolic phenotypes in 22 population-based studies from Europe, Australia, and USA (n = 18,212). DNA methylation at the GFI1-locus was measured in whole-blood. Multivariable regression models were fitted to examine its association with exposure to prenatal and own adult smoking. DNA methylation levels were analysed in relation to body mass index (BMI), waist circumference (WC), fasting glucose (FG), high-density lipoprotein cholesterol (HDL-C), triglycerides (TG), diastolic, and systolic blood pressure (BP). FINDINGS: Lower DNA methylation at three out of eight GFI1-CpGs was associated with exposure to maternal prenatal smoking, whereas, all eight CpGs were associated with adult own smoking. Lower DNA methylation at cg14179389, the strongest maternal prenatal smoking locus, was associated with increased WC and BP when adjusted for sex, age, and adult smoking with Bonferroni-corrected P < 0·012. In contrast, lower DNA methylation at cg09935388, the strongest adult own smoking locus, was associated with decreased BMI, WC, and BP (adjusted 1 × 10-7 < P < 0.01). Similarly, lower DNA methylation at cg12876356, cg18316974, cg09662411, and cg18146737 was associated with decreased BMI and WC (5 × 10-8 < P < 0.001). Lower DNA methylation at all the CpGs was consistently associated with higher TG levels. INTERPRETATION: Epigenetic changes at the GFI1 were linked to smoking exposure in-utero/in-adulthood and robustly associated with cardio-metabolic risk factors. FUND: European Union's Horizon 2020 research and innovation programme under grant agreement no. 633595 DynaHEALTH.

Training a model for estimating leukocyte composition using whole-blood DNA methylation and cell counts as reference.

AIM: Whole-blood DNA methylation depends on the underlying leukocyte composition and confounding hereby is a major concern in epigenome-wide association studies. Cell counts are often missing or may not be feasible. Computational approaches estimate leukocyte composition from DNA methylation based on reference datasets of purified leukocytes. We explored the possibility to train such a model on whole-blood DNA methylation and cell counts without the need for purification. MATERIALS & METHODS: Using whole-blood DNA methylation and corresponding five-part cell counts from 2445 participants from the London Life Sciences Prospective Population Study, a model was trained on a subset of 175 subjects and evaluated on the remaining. RESULTS: Correlations between cell counts and estimated cell proportions were high (neutrophils 0.85, eosinophils 0.88, basophils 0.02, lymphocytes 0.84, monocytes 0.55) and estimated proportions explained more variance in whole-blood DNA methylation levels than counts. CONCLUSION: Our model provided precise estimates for the common cell types.

Investigation of Genetic Variation Underlying Central Obesity amongst South Asians.

South Asians are 1/4 of the world's population and have increased susceptibility to central obesity and related cardiometabolic disease. Knowledge of genetic variants affecting risk of central obesity is largely based on genome-wide association studies of common SNPs in Europeans. To evaluate the contribution of DNA sequence variation to the higher levels of central obesity (defined as waist hip ratio adjusted for body mass index, WHR) among South Asians compared to Europeans we carried out: i) a genome-wide association analysis of >6M genetic variants in 10,318 South Asians with focused analysis of population-specific SNPs; ii) an exome-wide association analysis of ~250K SNPs in protein-coding regions in 2,637 South Asians; iii) a comparison of risk allele frequencies and effect sizes of 48 known WHR SNPs in 12,240 South Asians compared to Europeans. In genome-wide analyses, we found no novel associations between common genetic variants and WHR in South Asians at P<5x10-8; variants showing equivocal association with WHR (P<1x10-5) did not replicate at P<0.05 in an independent cohort of South Asians (N = 1,922) or in published, predominantly European meta-analysis data. In the targeted analyses of 122,391 population-specific SNPs we also found no associations with WHR in South Asians at P<0.05 after multiple testing correction. Exome-wide analyses showed no new associations between genetic variants and WHR in South Asians, either individually at P<1.5x10-6 or grouped by gene locus at P<2.5x10-6. At known WHR loci, risk allele frequencies were not higher in South Asians compared to Europeans (P = 0.77), while effect sizes were unexpectedly smaller in South Asians than Europeans (P<5.0x10-8). Our findings argue against an important contribution for population-specific or cosmopolitan genetic variants underlying the increased risk of central obesity in South Asians compared to Europeans.