Search

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 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.

Background In the first study of its kind, we examine the association between growth and development in early life and DNAm age biomarkers in mid-life. Methods Participants were from the Medical Research Council National Survey of Health and Development (n = 1376). Four DNAm age acceleration (AgeAccel) biomarkers were measured when participants were aged 53 years: AgeAccelHannum; AgeAccelHorvath; AgeAccelLevine; and AgeAccelGrim. Exposure variables included: relative weight gain (standardised residuals from models of current weight z-score on current height, and previous weight and height z-scores); and linear growth (standardised residuals from models of current height z-score on previous height and weight z-scores) during infancy (0–2 years, weight gain only), early childhood (2–4 years), middle childhood (4–7 years) and late childhood to adolescence (7–15 years); age at menarche; and pubertal stage for men at 14–15 years. The relationship between relative weight gain and linear growth and AgeAccel was investigated using conditional growth models. We replicated analyses from the late childhood to adolescence period and pubertal timing among 240 participants from The National Child and Development Study (NCDS). Results A 1SD increase in relative weight gain in late childhood to adolescence was associated with 0.50 years (95% CI 0.20, 0.79) higher AgeAccelGrim. Although the CI includes the null, the estimate was similar in NCDS [0.57 years (95% CI − 0.01, 1.16)] There was no strong evidence that relative weight gain and linear growth in childhood was associated with any other AgeAccel biomarker. There was no relationship between pubertal timing in men and AgeAccel biomarkers. Women who reached menarche ≥ 12 years had 1.20 years (95% CI 0.15, 2.24) higher AgeAccelGrim on average than women who reached menarche Conclusions Our findings generally do not support an association between growth and AgeAccel biomarkers in mid-life. However, we found rapid weight gain during pubertal development, previously related to higher cardiovascular disease risk, to be associated with older AgeAccelGrim. Given this is an exploratory study, this finding requires replication.

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.

Individual risk of type 2 diabetes (T2D) is modified by perturbations of adipose mass, distribution and function. To investigate mechanisms responsible, we explored the molecular, cellular, and whole-body effects of T2D-associated alleles near KLF14. We show that KLF14 diabetes-risk alleles act in adipose tissue to reduce KLF14 expression, and modulate, in trans, expression of >400 genes. We demonstrate that, in human cellular studies, reduced KLF14 expression increases pre-adipocyte proliferation but disrupts lipogenesis, and, in mice, adipose-specific deletion of Klf14 partially recapitulates the human phenotype of insulin resistance, dyslipidemia and T2D. We show that KLF14 T2D risk-allele carriers shift body fat from gynoid to abdominal stores, and display a marked increase in adipocyte cell size: these effects on fat distribution, and the T2D-association, are female-specific. Metabolic risk associated with variation at this imprinted locus depends on both the sex of the subject, and of the parent from whom the risk-allele derives The replicated genome-wide significant T2D association signal at chr7q32.3 maps to a 45kb recombination interval, extending from 3kb to 48kb upstream of KLF141,2 (Figure 1a-c). In previous work based on microarray-derived RNA expression data, KLF14, which encodes an imprinted transcription factor, was exposed as the likely cis-effector gene for this locus in subcutaneous adipose tissue1 and revealed to be a trans-regulator of a programme of adipose tissue expression3. The KLF family of zinc-finger binding proteins have wide-ranging regulatory roles in biological processes such as proliferation, differentiation and growth4,5. However, little is known about KLF14, a single exon gene whose transcription is limited to the maternally inherited chromosome in embryonic, extra-embryonic, and adult tissue in humans and mice6.

Female somatic X-chromosome inactivation (XCI) balances the X-linked transcriptional dosages between the sexes. Skewed XCI toward one parental X has been observed in several complex human traits, but the extent to which genetics and environment influence skewed XCI is largely unexplored. To address this, we quantify XCI-skew in multiple tissues and immune cell types in a twin cohort. Within an individual, XCI-skew differs between blood, fat and skin tissue, but is shared across immune cell types. XCI skew increases with age in blood, but not other tissues, and is associated with smoking. XCI-skew is increased in twins with Rheumatoid Arthritis compared to unaffected identical co-twins. XCI-skew is heritable in blood of females >55 years old (h2 = 0.34), but not in younger individuals or other tissues. This results in a Gene x Age interaction that shifts the functional dosage of all X-linked heterozygous loci in a tissue-restricted manner., Skewing of X chromosome inactivation (XCI) occurs when the silencing of one parental X chromosome is non-random. Here, Zito et al. report XCI patterns in lymphoblastoid cell lines, blood, subcutaneous adipose tissue samples and skin samples of monozygotic and dizygotic twins and find XCI skew to associate with tissue and age.

Genome-wide DNA methylation has been implicated in complex human diseases. Here, we identified epigenetic biomarkers for type 2 diabetes (T2D) underlying obesogenic environments. In a blood-based DNA methylation analysis of 11 monozygotic twins (MZTW) discordant for T2D, we discovered genetically independent candidate methylation sites. In a follow-up replication study (17 MZTW pairs) for external validation, we replicated the T2D-association at a novel CpG signal in the ELOVL fatty acid elongase 5 (ELOVL5) gene specific to T2D-discordant MZTW. For concordant DNA methylation signatures in tissues, we further confirmed that a CpG site (cg18681426) was associated with adipogenic differentiation between human preadipocytes and adipocytes isolated from the same biopsy sample. In addition, the ELOVL5 gene was significantly differentially expressed in adipose tissues from unrelated T2D patients and in human pancreatic islets. Our results demonstrate that blood-derived DNA methylation is associated with T2D risk as a proxy for cumulative epigenetic status in human adipose and pancreatic tissues. Moreover, ELOVL5 expression was increased in cellular and mouse models of induced obesity-related diabetes. These findings may provide new insights into epigenetic architecture by uncovering methylation-based biomarkers.

Background Variation in the human fecal microbiota has previously been associated with body mass index (BMI). Although obesity is a global health burden, the accumulation of abdominal visceral fat is the specific cardio-metabolic disease risk factor. Here, we explore links between the fecal microbiota and abdominal adiposity using body composition as measured by dual-energy X-ray absorptiometry in a large sample of twins from the TwinsUK cohort, comparing fecal 16S rRNA diversity profiles with six adiposity measures. Results We profile six adiposity measures in 3666 twins and estimate their heritability, finding novel evidence for strong genetic effects underlying visceral fat and android/gynoid ratio. We confirm the association of lower diversity of the fecal microbiome with obesity and adiposity measures, and then compare the association between fecal microbial composition and the adiposity phenotypes in a discovery subsample of twins. We identify associations between the relative abundances of fecal microbial operational taxonomic units (OTUs) and abdominal adiposity measures. Most of these results involve visceral fat associations, with the strongest associations between visceral fat and Oscillospira members. Using BMI as a surrogate phenotype, we pursue replication in independent samples from three population-based cohorts including American Gut, Flemish Gut Flora Project and the extended TwinsUK cohort. Meta-analyses across the replication samples indicate that 8 OTUs replicate at a stringent threshold across all cohorts, while 49 OTUs achieve nominal significance in at least one replication sample. Heritability analysis of the adiposity-associated microbial OTUs prompted us to assess host genetic-microbe interactions at obesity-associated human candidate loci. We observe significant associations of adiposity-OTU abundances with host genetic variants in the FHIT, TDRG1 and ELAVL4 genes, suggesting a potential role for host genes to mediate the link between the fecal microbiome and obesity. Conclusions Our results provide novel insights into the role of the fecal microbiota in cardio-metabolic disease with clear potential for prevention and novel therapies. Electronic supplementary material The online version of this article (doi:10.1186/s13059-016-1052-7) contains supplementary material, which is available to authorized users.

Individual risk of type 2 diabetes (T2D) is modified by perturbations of adipose mass, distribution and function. To investigate mechanisms responsible, we explored the molecular, cellular, and whole-body effects of T2D-associated alleles near KLF14. We show that KLF14 diabetes-risk alleles act in adipose tissue to reduce KLF14 expression, and modulate, in trans, expression of 385 genes. We demonstrate that, in human cellular studies, reduced KLF14 expression increases pre-adipocyte proliferation but disrupts lipogenesis, and, in mice, adipose-specific deletion of Klf14 partially recapitulates the human phenotype of insulin resistance, dyslipidemia and T2D. We show that KLF14 T2D risk-allele carriers shift body fat from gynoid to abdominal stores, and display a marked increase in adipocyte cell size: these effects on fat distribution, and the T2D-association, are female-specific. Metabolic risk associated with variation at this imprinted locus depends on both the sex of the subject, and of the parent from whom the risk-allele derives.

Normal thyroid function is essential for health, but its genetic architecture remains poorly understood. Here, for the heritable thyroid traits thyrotropin (TSH) and free thyroxine (FT4), we analyse whole-genome sequence data from the UK10K project (N=2,287). Using additional whole-genome sequence and deeply imputed data sets, we report meta-analysis results for common variants (MAF≥1%) associated with TSH and FT4 (N=16,335). For TSH, we identify a novel variant in SYN2 (MAF=23.5%, P=6.15 × 10(-9)) and a new independent variant in PDE8B (MAF=10.4%, P=5.94 × 10(-14)). For FT4, we report a low-frequency variant near B4GALT6/SLC25A52 (MAF=3.2%, P=1.27 × 10(-9)) tagging a rare TTR variant (MAF=0.4%, P=2.14 × 10(-11)). All common variants explain ≥20% of the variance in TSH and FT4. Analysis of rare variants (MAF1%) using sequence kernel association testing reveals a novel association with FT4 in NRG1. Our results demonstrate that increased coverage in whole-genome sequence association studies identifies novel variants associated with thyroid function.

Background Recent assays for individual-specific genome-wide DNA methylation profiles have enabled epigenome-wide association studies to identify specific CpG sites associated with a phenotype. Computational prediction of CpG site-specific methylation levels is critical to enable genome-wide analyses, but current approaches tackle average methylation within a locus and are often limited to specific genomic regions. Results We characterize genome-wide DNA methylation patterns, and show that correlation among CpG sites decays rapidly, making predictions solely based on neighboring sites challenging. We built a random forest classifier to predict methylation levels at CpG site resolution using features including neighboring CpG site methylation levels and genomic distance, co-localization with coding regions, CpG islands (CGIs), and regulatory elements from the ENCODE project. Our approach achieves 92% prediction accuracy of genome-wide methylation levels at single-CpG-site precision. The accuracy increases to 98% when restricted to CpG sites within CGIs and is robust across platform and cell-type heterogeneity. Our classifier outperforms other types of classifiers and identifies features that contribute to prediction accuracy: neighboring CpG site methylation, CGIs, co-localized DNase I hypersensitive sites, transcription factor binding sites, and histone modifications were found to be most predictive of methylation levels. Conclusions Our observations of DNA methylation patterns led us to develop a classifier to predict DNA methylation levels at CpG site resolution with high accuracy. Furthermore, our method identified genomic features that interact with DNA methylation, suggesting mechanisms involved in DNA methylation modification and regulation, and linking diverse epigenetic processes. Electronic supplementary material The online version of this article (doi:10.1186/s13059-015-0581-9) contains supplementary material, which is available to authorized users.

Recent studies have identified both heritable DNA methylation effects and differential methylation in disease-discordant identical twins. Larger sample sizes, replication, genetic-epigenetic analyses and longitudinal assays are now needed to establish the role of epigenetic variants in disease.