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Neutrophil-to-lymphocyte ratio (NLR) and platelet-to-lymphocyte ratio (PLR) are important biomarkers for disease development and progression. To gain insight into the genetic causes of variance in NLR and PLR in the general population, we conducted genome-wide association (GWA) analyses and estimated SNP heritability in a sample of 5901 related healthy Dutch individuals. GWA analyses identified a new genome-wide significant locus on the HBS1L-MYB intergenic region for PLR, which replicated in a sample of 2538 British twins. For platelet count, we replicated three known genome-wide significant loci in our cohort (at CCDC71L-PIK3CG, BAK1 and ARHGEF3). For neutrophil count, we replicated the PSMD3 locus. For the identified top SNPs, we found significant cis and trans expression quantitative trait loci effects for several loci involved in hematological and immunological pathways. Linkage Disequilibrium score (LD) regression analyses for PLR and NLR confirmed that both traits are heritable, with a polygenetic SNP heritability for PLR of 14.1%, and for NLR of 2.4%. Genetic correlations were present between ratios and the constituent counts, with the genetic correlation (r=0.45) of PLR with platelet count reaching statistical significance. In conclusion, we established that two important biomarkers have a significant heritable SNP component, and identified the first genome-wide locus for PLR.

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.

Supplemental Digital Content is Available in the Text. We analysed epigenome-wide methylation in blood DNA in Caucasian female twins. Bioinformatics analyses of the associated methylation signals showed enrichment for neurological pathways in CWP., Chronic widespread musculoskeletal pain (CWP), has a considerable heritable component, which remains to be explained. Epigenetic factors may contribute to and account for some of the heritability estimate. We analysed epigenome-wide methylation using MeDIPseq in whole blood DNA from 1708 monozygotic and dizygotic Caucasian twins having CWP prevalence of 19.9%. Longitudinally stable methylation bins (lsBINs), were established by testing repeated measurements conducted ≥3 years apart, n = 292. DNA methylation variation at lsBINs was tested for association with CWP in a discovery set of 50 monozygotic twin pairs discordant for CWP, and in an independent dataset (n = 1608 twins), and the results from the 2 samples were combined using Fisher method. Functional interpretation of the most associated signals was based on functional genomic annotations, gene ontology, and pathway analyses. Of 723,029 signals identified as lsBINs, 26,399 lsBINs demonstrated the same direction of association in both discovery and replication datasets at nominal significance (P ≤ 0.05). In the combined analysis across 1708 individuals, whereas no lsBINs showed genome-wide significance (P < 10-8), 24 signals reached p≤9E-5, and these included association signals mapping in or near to IL17A, ADIPOR2, and TNFRSF13B. Bioinformatics analyses of the associated methylation bins showed enrichment for neurological pathways in CWP. We estimate that the variance explained by epigenetic factors in CWP is 6%. This, the largest study to date of DNA methylation in CWP, points towards epigenetic modification of neurological pathways in CWP and provides proof of principle of this method in teasing apart the complex risk factors for CWP.

Monozygotic (MZ) twins share the same STR profile, demonstrating a practical problem in forensic casework. DNA methylation has provided a suitable resource for MZ twin differentiation; however, studies addressing the forensic feasibility are lacking. Here, we investigated epigenetic MZ twin differentiation from blood under the forensic scenario comprising i) the discovery of candidate markers in reference-type blood DNA via genome-wide analysis, ii) the technical validation of candidate markers in reference-type blood DNA using a suitable targeted method, and iii) the analysis of the validated markers in trace-type DNA. Genome-wide methylation analysis in blood DNA from 10 MZ twin pairs resulted in 19 to 111 twin-differentially methylated sites (tDMSs) per pair with >0.3 twin-to-twin differences. Considering all top three candidate tDMSs across all pairs in the technical validation based on methylation-specific qPCR, 67.85% generated >0.1 twin-to-twin differences. Of the validated tDMSs, 68.4% showed >0.1 twin-to-twin differences with qPCR in trace-type DNA across 8 pairs. Using an updated marker selection strategy, 8 additional candidate tDMSs were obtained for an example MZ pair, of which 7 showed >0.1 twin-to-twin differences in both reference- and trace-type DNA. Lastly, we introduce a high-resolution melting curve analysis of the entire fragment that can complement the proposed approach. Overall, our study demonstrates the general feasibility of epigenetic twin differentiation in the forensic context and highlights that the number of informative tDMSs in the final trace DNA analysis is crucial, as some candidate markers identified in reference DNA were shown not informative in the trace DNA due to various, including technical, reasons. Future studies will need to address the optimal number of epigenetic markers required for reliable identification of MZ twin individuals including statistical considerations.

SummaryHost genetics and the gut microbiome can both influence metabolic phenotypes. However, whether host genetic variation shapes the gut microbiome and interacts with it to affect host phenotype is unclear. Here, we compared microbiotas across >1,000 fecal samples obtained from the TwinsUK population, including 416 twin pairs. We identified many microbial taxa whose abundances were influenced by host genetics. The most heritable taxon, the family Christensenellaceae, formed a co-occurrence network with other heritable Bacteria and with methanogenic Archaea. Furthermore, Christensenellaceae and its partners were enriched in individuals with low body mass index (BMI). An obese-associated microbiome was amended with Christensenella minuta, a cultured member of the Christensenellaceae, and transplanted to germ-free mice. C. minuta amendment reduced weight gain and altered the microbiome of recipient mice. Our findings indicate that host genetics influence the composition of the human gut microbiome and can do so in ways that impact host metabolism.

Using a nontargeted metabolomics approach of 447 fasting plasma metabolites, we searched for novel molecular markers that arise before and after hyperglycemia in a large population-based cohort of 2,204 females (115 type 2 diabetic [T2D] case subjects, 192 individuals with impaired fasting glucose [IFG], and 1,897 control subjects) from TwinsUK. Forty-two metabolites from three major fuel sources (carbohydrates, lipids, and proteins) were found to significantly correlate with T2D after adjusting for multiple testing; of these, 22 were previously reported as associated with T2D or insulin resistance. Fourteen metabolites were found to be associated with IFG. Among the metabolites identified, the branched-chain keto-acid metabolite 3-methyl-2-oxovalerate was the strongest predictive biomarker for IFG after glucose (odds ratio [OR] 1.65 [95% CI 1.39–1.95], P = 8.46 × 10−9) and was moderately heritable (h2 = 0.20). The association was replicated in an independent population (n = 720, OR 1.68 [ 1.34–2.11], P = 6.52 × 10−6) and validated in 189 twins with urine metabolomics taken at the same time as plasma (OR 1.87 [1.27–2.75], P = 1 × 10−3). Results confirm an important role for catabolism of branched-chain amino acids in T2D and IFG. In conclusion, this T2D-IFG biomarker study has surveyed the broadest panel of nontargeted metabolites to date, revealing both novel and known associated metabolites and providing potential novel targets for clinical prediction and a deeper understanding of causal mechanisms.

Epigenetic modifications such as DNA methylation play a key role in gene regulation and disease susceptibility. However, little is known about the genome-wide frequency, localization, and function of methylation variation and how it is regulated by genetic and environmental factors.We utilized the Multiple Tissue Human Expression Resource (MuTHER) and generated Illumina 450K adipose methylome data from 648 twins.We found that individual CpGs had low variance and that variability was suppressed in promoters.We noted that DNA methylation variation was highly heritable (2median = 0.34) and that shared environmental effects correlated with metabolic phenotype-associated CpGs. Analysis of methylation quantitative-trait loci (metQTL) revealed that 28% of CpGs were associated with nearby SNPs, and when overlapping them with adipose expression quantitative-trait loci (eQTL) from the same individuals, we found that 6% of the loci played a role in regulating both gene expression and DNA methylation. These associations were bidirectional, but there were pronounced negative associations for promoter CpGs. Integration of metQTL with adipose reference epigenomes and disease associations revealed significant enrichment of metQTL overlapping metabolic-trait or disease loci in enhancers (the strongesteffects were for high-density lipoprotein cholesterol and body mass index [BMI]). We followed up with the BMI SNP rs713586, a cg01884057 metQTL that overlaps an enhancer upstream of ADCY3, and used bisulphite sequencing to refine this region. Our results showed widespread population invariability yet sequence dependence on adipose DNA methylation but that incorporating maps of regulatory elements aid in linking CpG variation to gene regulation and disease risk in a tissue-dependent manner. © 2013 by The American Society of Human Genetics. All rights reserved.

Objective: Genome-wide association studies (GWAS) have been successful at identifying associations with stroke and stroke subtypes, but have not yet identified any associations solely with small vessel stroke (SVS). SVS comprises one quarter of all ischemic stroke and is a major manifestation of cerebral small vessel disease, the primary cause of vascular cognitive impairment. Studies across neurological traits have shown that younger-onset cases have an increased genetic burden. We leveraged this increased genetic burden by performing an age-at-onset informed GWAS meta-analysis, including a large younger-onset SVS population, to identify novel associations with stroke. Methods: We used a three-stage age-at-onset informed GWAS to identify novel genetic variants associated with stroke. On identifying a novel locus associated with SVS, we assessed its influence on other small vessel disease phenotypes, as well as on messenger RNA (mRNA) expression of nearby genes, and on DNA methylation of nearby CpG sites in whole blood and in the fetal brain. Results: We identified an association with SVS in 4,203 cases and 50,728 controls on chromosome 16q24.2 (odds ratio [OR; 95% confidence interval {CI}] = 1.16 [1.10–1.22]; p = 3.2 × 10−9). The lead single-nucleotide polymorphism (rs12445022) was also associated with cerebral white matter hyperintensities (OR [95% CI] = 1.10 [1.05–1.16]; p = 5.3 × 10−5; N = 3,670), but not intracerebral hemorrhage (OR [95% CI] = 0.97 [0.84–1.12]; p = 0.71; 1,545 cases, 1,481 controls). rs12445022 is associated with mRNA expression of ZCCHC14 in arterial tissues (p = 9.4 × 10−7) and DNA methylation at probe cg16596957 in whole blood (p = 5.3 × 10−6). Interpretation: 16q24.2 is associated with SVS. Associations of the locus with expression of ZCCHC14 and DNA methylation suggest the locus acts through changes to regulatory elements. Ann Neurol 2017;81:383–394.

High nevus count is the strongest risk factor for melanoma, and although gene variants have been discovered for both traits, epigenetic variation is unexplored. We investigated 322 healthy human skin DNA methylomes associated with total body nevi count, incorporating genetic and transcriptomic variation. DNA methylation changes were identified at genes involved in melanocyte biology, such as RAF1 ( P = 1.2 × 10 −6 ) and CTC1 (region: P = 6.3 × 10 −4 ), and other genes including ARRDC1 ( P = 3.1 × 10 −7 ). A subset exhibited coordinated methylation and transcription changes within the same biopsy. The total analysis was also enriched for melanoma-associated DNA methylation variation ( P = 6.33 × 10 −6 ). In addition, we show that skin DNA methylation is associated in cis with known genome-wide association study single nucleotide polymorphisms for nevus count, at PLA2G6 ( P = 1.7 × 10 −49 ) and NID1 ( P = 6.4 × 10 −14 ), as well as melanoma risk, including in or near MC1R , MX2, and TERT / CLPTM1L ( P 1 × 10 −10 ) . Our analysis using a uniquely large dataset comprising healthy skin DNA methylomes identified known and additional regulatory loci and pathways in nevi and melanoma biology. This integrative study improves our understanding of predisposition to nevi and their potential contribution to melanoma pathogenesis.

Although association studies have unveiled numerous correlations of biochemical markers with age and age-related diseases, we still lack an understanding of their mutual dependencies. To find molecular pathways that underlie age-related diseases as well as their comorbidities, we integrated aging markers from four different high-throughput omics datasets, namely epigenomics, transcriptomics, glycomics and metabolomics, with a comprehensive set of disease phenotypes from 510 participants of the TwinsUK cohort. We used graphical random forests to assess conditional dependencies between omics markers and phenotypes while eliminating mediated associations. Applying this novel approach for multi-omics data integration yields a model consisting of seven modules that represent distinct aspects of aging. These modules are connected by hubs that potentially trigger comorbidities of age-related diseases. As an example, we identified urate as one of these key players mediating the comorbidity of renal disease with body composition and obesity. Body composition variables are in turn associated with inflammatory IgG markers, mediated by the expression of the hormone oxytocin. Thus, oxytocin potentially contributes to the development of chronic low-grade inflammation, which often accompanies obesity. Our multi-omics graphical model demonstrates the interconnectivity of age-related diseases and highlights molecular markers of the aging process that might drive disease comorbidities.

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.

Background Advancing age progressively impacts on risk and severity of chronic disease. It also modifies the epigenome, with changes in DNA methylation, due to both random drift and variation within specific functional loci. Results In a discovery set of 2238 peripheral-blood genome-wide DNA methylomes aged 19–82 years, we identify 71 age-associated differentially methylated regions within the linkage disequilibrium blocks of the single nucleotide polymorphisms from the NIH genome-wide association study catalogue. This included 52 novel regions, 29 within loci not covered by 450 k or 27 k Illumina array, and with enrichment for DNase-I Hypersensitivity sites across the full range of tissues. These age-associated differentially methylated regions also show marked enrichment for enhancers and poised promoters across multiple cell types. In a replication set of 2084 DNA methylomes, 95.7 % of the age-associated differentially methylated regions showed the same direction of ageing effect, with 80.3 % and 53.5 % replicated to p

Multiple sclerosis (MS) is a complex trait in which HLA-DRB1*15 bearing MHC haplotypes increase risk of MS in people of Northern European descent. In this investigation of 7,334 individuals from 1,515 MS families, the largest cohort used to study the HLA-DRB1 locus to date, we analysed the transmission of HLA-DRB1*15 haplotypes stratified by sex of transmitting parent. A significant over transmission of HLA-DRB1*15 from mothers was observed (chi (2) = 7.73, P = 0.0054), suggesting that parent of origin effects at the MHC determine susceptibility to MS.

Correction We showed in our study [1] that SNP rs10876043 in the disco-interacting protein 2 homolog B gene (DIP2B) was associated with the first principal component of methylation. Although the analyses and result remain unchanged, it appears that this observation is likely due to a genotyping artifact. That is, the reported rs10876043 genotypes differ according to HapMap Phase (cell lines genotyped in Phase 1/2 have reported genotypes AG and GG, while Phase 3 cell lines have genotype AA). The 1000 Genomes data suggest the correct genotype is probably AA for all of these YRI individuals. These genotype differences between different phases of the HapMap Project, coupled with a small difference in mean methylation between Phase 1/2 vs 3 cell lines appear to have produced an artifactual association. Other analyses in the paper controlled for the top principal components and should therefore be robust to this type of effect.

While there have been studies exploring regulatory variation in one or more tissues, the complexity of tissue-specificity in multiple primary tissues is not yet well understood. We explore in depth the role of cis-regulatory variation in three human tissues: lymphoblastoid cell lines (LCL), skin, and fat. The samples (156 LCL, 160 skin, 166 fat) were derived simultaneously from a subset of well-phenotyped healthy female twins of the MuTHER resource. We discover an abundance of cis-eQTLs in each tissue similar to previous estimates (858 or 4.7% of genes). In addition, we apply factor analysis (FA) to remove effects of latent variables, thus more than doubling the number of our discoveries (1,822 eQTL genes). The unique study design (Matched Co-Twin Analysis—MCTA) permits immediate replication of eQTLs using co-twins (93%–98%) and validation of the considerable gain in eQTL discovery after FA correction. We highlight the challenges of comparing eQTLs between tissues. After verifying previous significance threshold-based estimates of tissue-specificity, we show their limitations given their dependency on statistical power. We propose that continuous estimates of the proportion of tissue-shared signals and direct comparison of the magnitude of effect on the fold change in expression are essential properties that jointly provide a biologically realistic view of tissue-specificity. Under this framework we demonstrate that 30% of eQTLs are shared among the three tissues studied, while another 29% appear exclusively tissue-specific. However, even among the shared eQTLs, a substantial proportion (10%–20%) have significant differences in the magnitude of fold change between genotypic classes across tissues. Our results underline the need to account for the complexity of eQTL tissue-specificity in an effort to assess consequences of such variants for complex traits., Author Summary Regulation of gene expression is a fundamental cellular process determining a large proportion of the phenotypic variance. Previous studies have identified genetic loci influencing gene expression levels (eQTLs), but the complexity of their tissue-specific properties has not yet been well-characterized. In this study, we perform cis-eQTL analysis in a unique matched co-twin design for three human tissues derived simultaneously from the same set of individuals. The study design allows validation of the substantial discoveries we make in each tissue. We explore in depth the tissue-dependent features of regulatory variants and estimate the proportions of shared and specific effects. We use continuous measures of eQTL sharing to circumvent the statistical power limitations of comparing direct overlap of eQTLs in multiple tissues. In this framework, we demonstrate that 30% of eQTLs are shared among tissues, while 29% are exclusively tissue-specific. Furthermore, we show that the fold change in expression between eQTL genotypic classes differs between tissues. Even among shared eQTLs, we report a substantial proportion (10%–20%) of significant tissue differences in magnitude of these effects. The complexities we highlight here are essential for understanding the impact of regulatory variants on complex traits.

BACKGROUND: DNA methylation is an essential epigenetic mechanism involved in gene regulation and disease, but little is known about the mechanisms underlying inter-individual variation in methylation profiles. Here we measured methylation levels at 22,290 CpG dinucleotides in lymphoblastoid cell lines from 77 HapMap Yoruba individuals, for which genome-wide gene expression and genotype data were also available. RESULTS: Association analyses of methylation levels with more than three million common single nucleotide polymorphisms (SNPs) identified 180 CpG-sites in 173 genes that were associated with nearby SNPs (putatively in cis, usually within 5 kb) at a false discovery rate of 10%. The most intriguing trans signal was obtained for SNP rs10876043 in the disco-interacting protein 2 homolog B gene (DIP2B, previously postulated to play a role in DNA methylation), that had a genome-wide significant association with the first principal component of patterns of methylation; however, we found only modest signal of trans-acting associations overall. As expected, we found significant negative correlations between promoter methylation and gene expression levels measured by RNA-sequencing across genes. Finally, there was a significant overlap of SNPs that were associated with both methylation and gene expression levels. CONCLUSIONS: Our results demonstrate a strong genetic component to inter-individual variation in DNA methylation profiles. Furthermore, there was an enrichment of SNPs that affect both methylation and gene expression, providing evidence for shared mechanisms in a fraction of genes.

We agree with Petkov and colleagues that including the BXD lines (“new BXD”), which were derived from advanced intercross lines (AIL), increases the map length in the recombinant inbred (RI) lines. As we note in our paper “One BXD subset was developed using advanced intercross populations and therefore contains approximately 1.7 times more recombinants per line”[1]. If the “new BXD” set were analyzed by itself, an AIL-specific map expansion factor should be applied [2]. However since our motivation was to obtain an accurate estimation of the relative rates of recombination in different regions of the genome, we analyzed all of the RIs together to increase the number of recombination events and the overall density of polymorphic markers. This increased our sensitivity, for example, to identify regions of extremely low and high recombination rates. There are a number of explanations as to why the map length should differ from that expected on the basis of the Haldane-Waddington equation [3,4]. The estimated map distance of the RIs depends also on the marker density [2] and on the number of inbred generations [5], factors that act to reduce the map length relative to the Haldane-Waddington theoretical equation (which assumes a fully inbred line). It has been hypothesized that “the apparent genetic interference of the accumulated generations (e.g., RIs) differs from the genetic interference acting in each meiosis and that commonly used map functions lead to reduced map distance estimates in the advanced designs” [5]. Consequently, it is not clear that the observed can be explained solely in the way suggested by Petkov et al.

Estimates of biological age based on DNA methylation patterns, often referred to as "epigenetic age", "DNAm age", have been shown to be robust biomarkers of age in humans. We previously demonstrated that independent of chronological age, epigenetic age assessed in blood predicted all-cause mortality in four human cohorts. Here, we expanded our original observation to 13 different cohorts for a total sample size of 13,089 individuals, including three racial/ethnic groups. In addition, we examined whether incorporating information on blood cell composition into the epigenetic age metrics improves their predictive power for mortality. All considered measures of epigenetic age acceleration were predictive of mortality (p ≤ 8.2 x 10-9), independent of chronological age, even after adjusting for additional risk factors (p -4), and within the racial/ethnic groups that we examined (non-Hispanic whites, Hispanics, African Americans). Epigenetic age estimates that incorporated information on blood cell composition led to the smallest p-values for time to death (p≤ 7.5 x 10-43). Overall, this study a) strengthens the evidence that epigenetic age predicts all-cause mortality above and beyond chronological age and traditional risk factors, and b) demonstrates that epigenetic age estimates that incorporate information on blood cell counts lead to highly significant associations with all-cause mortality.

Both the health burden and genetic heritability of blood pressure are well-established, yet only a fraction of blood pressure genes have been identified. Epigenetic changes in or near genes related to blood pressure may explain part of its variance and provide new insights into the biological mechanisms involved in blood pressure regulation. DNA methylation measured on the Infinium HumanMethylation450 BeadChip was tested for association with systolic and diastolic blood pressure in individuals of European (EA) and African ancestry (AA) in the ARIC, CHS, FHS, GENOA, GOLDN, NAS, LBC1936, RS-III, and TwinsUK cohorts (N-total=9,828; N-EA=6,650, N-AA=3,178). In each cohort, linear mixed models were used to estimate associations adjusting for age, sex, blood cell counts, BMI, smoking, and ancestry, as well as surrogate variables and technical covariates to control for batch effects. Effect estimates from all cohorts were combined using inverse variance fixed effects meta-analysis; heterogeneity of effects in race- and sex-stratified analyses was not observed. Thirty-one methylation probes were found to be significantly associated with blood pressure after Bonferroni correction (p Our findings suggest a novel genetic regulation of known blood pressure systems through heritable DNA methylation.

Using whole blood from 15 twin pairs discordant for breast cancer and high-resolution (450K) DNA methylation analysis, we identified 403 differentially methylated CpG sites including known and novel potential breast cancer genes. Confirming the results in an independent validation cohort of 21 twin pairs determined the docking protein DOK7 as a candidate for blood-based cancer diagnosis. DNA hypermethylation of the promoter region was also seen in primary breast cancer tissues and cancer cell lines. Hypermethylation of DOK7 occurs years before tumor diagnosis, suggesting a role as a powerful epigenetic blood-based biomarker as well as providing insights into breast cancer pathogenesis.

In the presence of epistasis multilocus association tests of human complex traits can provide powerful methods to detect susceptibility variants. We undertook multilocus analyses in 1924 type 2 diabetes cases and 2938 controls from the Wellcome Trust Case Control Consortium (WTCCC). We performed a two-dimensional genome-wide association (GWA) scan using joint two-locus tests of association including main and epistatic effects in 70,236 markers tagging common variants. We found two-locus association at 79 SNP-pairs at a Bonferroni-corrected P-value = 0.05 (uncorrected P-value = 2.14 × 10⁻¹¹). The 79 pair-wise results always contained rs11196205 in TCF7L2 paired with 79 variants including confirmed variants in FTO, TSPAN8, and CDKAL1, which are associated in the absence of epistasis. However, the majority (82%) of the 79 variants did not have compelling single-locus association signals (P-value = 5 × 10⁻⁴). Analyses conditional on the single-locus effects at TCF7L2 established that the joint two-locus results could be attributed to single-locus association at TCF7L2 alone. Interaction analyses among the peak 80 regions and among 23 previously established diabetes candidate genes identified five SNP-pairs with case-control and case-only epistatic signals. Our results demonstrate the feasibility of systematic scans in GWA data, but confirm that single-locus association can underlie and obscure multilocus findings.

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.

Monozygotic (MZ) twins are typically indistinguishable via forensic DNA profiling. Recently, we demonstrated that epigenetic differentiation of MZ twins is feasible; however, proportions of twin differentially methylated CpG sites (tDMSs) identified in reference-type blood DNA were not replicated in trace-type blood DNA. Here we investigated buccal swabs as typical forensic reference material, and saliva and cigarette butts as commonly encountered forensic trace materials. As an analog to a forensic case, we analyzed one MZ twin pair. Epigenome-wide microarray analysis in reference-type buccal DNA revealed 25 candidate tDMSs with >0.5 twin-to-twin differences. MethyLight quantitative PCR (qPCR) of 22 selected tDMSs in trace-type DNA revealed in saliva DNA that six tDMSs (27.3%) had >0.1 twin-to-twin differences, seven (31.8%) had smaller (

Background: A key focus in cancer research is the discovery of biomarkers that accurately diagnose early lesions in non-invasive tissues. Several studies have identified malignancy-associated DNA methylation changes in blood, yet no general cancer biomarker has been identified to date. Here, we explore the potential of blood DNA methylation as a biomarker of pan-cancer (cancer of multiple different origins) in 41 female cancer discordant monozygotic (MZ) twin-pairs sampled before or after diagnosis using the Illumina HumanMethylation450 BeadChip.Results: We analysed epigenome-wide DNA methylation profiles in 41 cancer discordant MZ twin-pairs with affected individuals diagnosed with tumours at different single primary sites: the breast, cervix, colon, endometrium, thyroid gland, skin (melanoma), ovary, and pancreas. No significant global differences in whole blood DNA methylation profiles were observed. Epigenome-wide analyses identified one novel pan-cancer differentially methylated position at false discovery rate (FDR) threshold of 10 % (cg02444695, P = 1.8 × 10−7) in an intergenic region 70 kb upstream of the SASH1 tumour suppressor gene, and three suggestive signals in COL11A2, AXL, and LINC00340. Replication of the four top-ranked signals in an independent sample of nine cancer-discordant MZ twin-pairs showed a similar direction of association at COL11A2, AXL, and LINC00340, and significantly greater methylation discordance at AXL compared to 480 healthy concordant MZ twin-pairs. The effects at cg02444695 (near SASH1), COL11A2, and LINC00340 were the most promising in biomarker potential because the DNA methylation differences were found to pre-exist in samples obtained prior to diagnosis and were limited to a 5-year period before diagnosis. Gene expression follow-up at the top-ranked signals in 283 healthy individuals showed correlation between blood methylation and gene expression in lymphoblastoid cell lines at PRL, and in the skin tissue at AXL. A significant enrichment of differential DNA methylation was observed in enhancer regions (P = 0.03).Conclusions: We identified DNA methylation signatures in blood associated with pan-cancer, at or near SASH1, COL11A2, AXL, and LINC00340. Three of these signals were present up to 5 years prior to cancer diagnosis, highlighting the potential clinical utility of whole blood DNA methylation analysis in cancer surveillance.

Background: The prevalence of type 2 diabetes (T2D) and obesity has dramatically increased within a few generations, reaching epidemic levels. In addition to genetic risk factors, epigenetic mechanisms triggered by changing environment are investigated for their role in the pathogenesis of these complex diseases. Epigenome-wide association studies (EWASs) have revealed significant associations of T2D, obesity, and BMI with DNA methylation. However, populations from the Middle East, where T2D and obesity rates are highest worldwide, have not been investigated so far.Methods: We performed the first EWAS in an Arab population with T2D and BMI and attempted to replicate 47 EWAS associations previously reported in Caucasians. We used the Illumina Infinium HumanMethylation450 BeadChip to quantify DNA methylation in whole blood DNA from 123 subjects of 15 multigenerational families from Qatar. To investigate the effect of differing genetic background and environment on the epigenetic associations, we further assessed the effect of replicated loci in 810 twins from UK.Results: Our EWAS suggested a novel association between T2D and cg06721411 (DQX1; p value = 1.18 × 10−9). We replicated in the Qatari population seven CpG associations with BMI (SOCS3, p value = 3.99 × 10−6; SREBF1, p value = 4.33 × 10−5; SBNO2, p value = 5.87 × 10−5; CPT1A, p value = 7.99 × 10−5; PRR5L, p value = 1.85 × 10−4; cg03078551, intergenic region on chromosome 17; p value = 1.00 × 10−3; LY6G6E, p value = 1.10 × 10−3) and one with T2D (TXNIP, p value = 2.46 × 10−5). All the associations were further confirmed in the UK cohort for both BMI and T2D. Meta-analysis increased the significance of the observed associations and revealed strong heterogeneity of the effect sizes (apart from CPT1A), although associations at these loci showed concordant direction in the two populations.Conclusions: Our study replicated eight known CpG associations with T2D or BMI in an Arab population. Heterogeneity of the effects at all loci except CPT1A between the Qatari and UK studies suggests that the underlying mechanisms might depend on genetic background and environmental pressure. Our EWAS results provide a basis for comparison with other ethnicities.

Interest in mapping susceptibility alleles for complex diseases, which do not follow a classic single-gene segregation pattern, has driven interest in methods that account for, or use information from one locus when mapping another. Our discussion group examined methods related to epistasis or gene x gene interaction. The goal of modeling gene x gene interaction varied across groups; some papers tried to detect gene x gene interaction while others tried to exploit it to map genes. Most of the 10 papers summarized here applied newly created or newly modified statistical methods related to gene x gene interaction, while two groups primarily examined computational issues. As is often the case, comparisons are complicated by little overlap in the data used across the papers, and further complicated by the fact that the available data may not have been ideal for some gene x gene interaction methods. However, the main difficulty in comparing and contrasting methods across the papers is the lack of a consistent statistical definition of gene x gene interaction. But despite these issues, two clear trends emerged across the analyses: First, the methods for quantitative trait gene x gene interaction appeared to perform very well, even in families initially ascertained as affected sib pairs; and second, dichotomous trait gene x gene interaction methods failed to produce consistent results. The difficulty of using (primarily) affected sib pair data in a gene x gene interaction analysis is explored.

Epigenetics describes the study of cellular modifications that can modify the expression of genes without changing the DNA sequence. DNA methylation is one of the most stable and prevalent epigenetic mechanisms. Twin studies have been a valuable model for unraveling the genetic and epigenetic epidemiology of complex traits, and now offer a potential to dissect the factors that impact DNA methylation variability and its biomedical significance. The twin design specifically allows for the study of genetic, environmental and lifestyle factors, and their potential interactions, on epigenetic profiles. Furthermore, genetically identical twins offer a unique opportunity to assess nongenetic impacts on epigenetic profiles. Here, we summarize recent findings from twin studies of DNA methylation profiles across tissues, to define current knowledge regarding the genetic and nongenetic factors that influence epigenetic variation.

Background CpG methylation variation is involved in human trait formation and disease susceptibility. Analyses within populations have been biased towards CpG-dense regions through the application of targeted arrays. We generate whole-genome bisulfite sequencing data for approximately 30 adipose and blood samples from monozygotic and dizygotic twins for the characterization of non-genetic and genetic effects at single-site resolution. Results Purely invariable CpGs display a bimodal distribution with enrichment of unmethylated CpGs and depletion of fully methylated CpGs in promoter and enhancer regions. Population-variable CpGs account for approximately 15–20 % of total CpGs per tissue, are enriched in enhancer-associated regions and depleted in promoters, and single nucleotide polymorphisms at CpGs are a frequent confounder of extreme methylation variation. Differential methylation is primarily non-genetic in origin, with non-shared environment accounting for most of the variance. These non-genetic effects are mainly tissue-specific. Tobacco smoking is associated with differential methylation in blood with no evidence of this exposure impacting cell counts. Opposite to non-genetic effects, genetic effects of CpG methylation are shared across tissues and thus limit inter-tissue epigenetic drift. CpH methylation is rare, and shows similar characteristics of variation patterns as CpGs. Conclusions Our study highlights the utility of low pass whole-genome bisulfite sequencing in identifying methylome variation beyond promoter regions, and suggests that targeting the population dynamic methylome of tissues requires assessment of understudied intergenic CpGs distal to gene promoters to reveal the full extent of inter-individual variation. Electronic supplementary material The online version of this article (doi:10.1186/s13059-015-0856-1) contains supplementary material, which is available to authorized users.

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: Epigenome-wide association scans (EWAS) are under way for many complex human traits, but EWAS power has not been fully assessed. We investigate power of EWAS to detect differential methylation using case-control and disease-discordant monozygotic (MZ) twin designs with genome-wide DNA methylation arrays. Methods and Results: We performed simulations to estimate power under the case-control and discordant MZ twin EWAS study designs, under a range of epigenetic risk effect sizes and conditions. For example, to detect a 10% mean methylation difference between affected and unaffected subjects at a genome-wide significance threshold of P = 1 × 10−6, 98 MZ twin pairs were required to reach 80% EWAS power, and 112 cases and 112 controls pairs were needed in the case-control design. We also estimated the minimum sample size required to reach 80% EWAS power under both study designs. Our analyses highlighted several factors that significantly influenced EWAS power, including sample size, epigenetic risk effect size, the variance of DNA methylation at the locus of interest and the correlation in DNA methylation patterns within the twin sample. Conclusions: We provide power estimates for array-based DNA methylation EWAS under case-control and disease-discordant MZ twin designs, and explore multiple factors that impact on EWAS power. Our results can help guide EWAS experimental design and interpretation for future epigenetic studies.

In a study of mainly paucibacillary leprosy-affected sib-pair families from South India, in addition to the expected associations with the HLA-DRB1 locus, we have identified significant association with a functional variant of the MICA gene as well as a microsatellite in the flanking region of the MICB gene. The associations with MICA and MICB cannot be accounted for by linkage disequilibrium with the HLA class II locus indicating a role in genetic susceptibility to leprosy that is independent of HLA-DRB1. Previous studies have shown that MICA and MICB are expressed on the surface of cells in response to infection, where they are recognized by the NKG2D receptor on gammadelta T cells, CD8+ alphabeta T cells and natural killer cells, all of which contribute to defense against mycobacteria. The MICA*5A5.1 allele, associated here with leprosy susceptibility, encodes a protein lacking a cytoplasmic tail providing a possible mechanism for defective immune surveillance against mycobacteria.

It is well established that gene interactions influence common human diseases, but to date linkage studies have been constrained to searching for single genes across the genome. We applied a novel approach to uncover significant gene-gene interactions in a systematic two-dimensional (2D) genome-scan of essential hypertension. The study cohort comprised 2076 affected sib-pairs and 66 affected half-sib-pairs of the British Genetics of HyperTension study. Extensive simulations were used to establish significance thresholds in the context of 2D genome-scans. Our analyses found significant and suggestive evidence for loci on chromosomes 5, 9,11, 15, 16 and 19, which influence hypertension when gene-gene interactions are taken into account (5q13.1 and 11q22.1, two-locus lod score = 5.72; 5q13.1 and 19q12, two-locus lod score = 5.35; 9q22.3 and 15q12, two-locus lod score = 4.80; 16p12.3 and 16q23.1, two-locus lod score= 4.50). For each significant and suggestive pairwise interaction, the two-locus genetic model that best fitted the data was determined. Regions that were not detected using single-locus linkage analysis were identified in the 2D scan as contributing significant epistatic effects. This approach has discovered novel loci for hypertension and offers a unique potential to use existing data to uncover novel regions involved in complex human diseases.

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.

Low birth weight (LBW) can have an impact on health outcomes in later life, especially in relation to pre-disposition to metabolic disease. Several studies suggest that LBW resulting from restricted intrauterine growth leaves a footprint on DNA methylation in utero, and this influence likely persists into adulthood. To investigate this further, we performed epigenome-wide association analyses of blood DNA methylation using Infinium HumanMethylation450 BeadChip profiles in 71 adult monozygotic (MZ) twin pairs who were extremely discordant for birth weight. A signal mapping to the IGF1R gene (cg12562232, p = 2.62 × 10-8), was significantly associated with birth weight discordance at a genome-wide false-discovery rate (FDR) of 0.05. We pursued replication in three additional independent datasets of birth weight discordant MZ pairs and observed the same direction of association, but the results were not significant. However, a meta-analysis across the four independent samples, in total 216 birth-weight discordant MZ twin pairs, showed a significant positive association between birth weight and DNA methylation differences at IGF1R (random-effects meta-analysis p = .04), and the effect was particularly pronounced in older twins (random-effects meta-analysis p = .008, 98 older birth-weight discordant MZ twin pairs). The results suggest that severe intra-uterine growth differences (birth weight discordance >20%) are associated with methylation changes in the IGF1R gene in adulthood, independent of genetic effects. Low birth weight (LBW) can have an impact on health outcomes in later life, especially in relation to pre-disposition to metabolic disease. Several studies suggest that LBW resulting from restricted intrauterine growth leaves a footprint on DNA methylation in utero, and this influence likely persists into adulthood. To investigate this further, we performed epigenome-wide association analyses of blood DNA methylation using Infinium HumanMethylation450 BeadChip profiles in 71 adult monozygotic (MZ) twin pairs who were extremely discordant for birth weight. A signal mapping to the IGF1R gene (cg12562232, p = 2.62 × 10-8), was significantly associated with birth weight discordance at a genome-wide false-discovery rate (FDR) of 0.05. We pursued replication in three additional independent datasets of birth weight discordant MZ pairs and observed the same direction of association, but the results were not significant. However, a meta-analysis across the four independent samples, in total 216 birth-weight discordant MZ twin pairs, showed a significant positive association between birth weight and DNA methylation differences at IGF1R (random-effects meta-analysis p = .04), and the effect was particularly pronounced in older twins (random-effects meta-analysis p = .008, 98 older birth-weight discordant MZ twin pairs). The results suggest that severe intra-uterine growth differences (birth weight discordance >20%) are associated with methylation changes in the IGF1R gene in adulthood, independent of genetic effects.

Metabolites are small molecules involved in cellular metabolism, which can be detected in biological samples using metabolomic techniques. Here we present the results of genome-wide association and meta-analyses for variation in the blood serum levels of 129 metabolites as measured by the Biocrates metabolomic platform. In a discovery sample of 7,478 individuals of European descent, we find 4,068 genome- and metabolome-wide significant (Z-test, P-9) associations between single-nucleotide polymorphisms (SNPs) and metabolites, involving 59 independent SNPs and 85 metabolites. Five of the fifty-nine independent SNPs are new for serum metabolite levels, and were followed-up for replication in an independent sample (N=1,182). The novel SNPs are located in or near genes encoding metabolite transporter proteins or enzymes (SLC22A16, ARG1, AGPS and ACSL1) that have demonstrated biomedical or pharmaceutical importance. The further characterization of genetic influences on metabolic phenotypes is important for progress in biological and medical research.

DNA methylation has a great potential for understanding the aetiology of common complex traits such as Type 2 diabetes (T2D). Here we perform genome-wide methylated DNA immunoprecipitation sequencing (MeDIP-seq) in whole-blood-derived DNA from 27 monozygotic twin pairs and follow up results with replication and integrated omics analyses. We identify predominately hypermethylated T2D-related differentially methylated regions (DMRs) and replicate the top signals in 42 unrelated T2D cases and 221 controls. The strongest signal is in the promoter of the MALT1 gene, involved in insulin and glycaemic pathways, and related to taurocholate levels in blood. Integrating the DNA methylome findings with T2D GWAS meta-analysis results reveals a strong enrichment for DMRs in T2D-susceptibility loci. We also detect signals specific to T2D-discordant twins in the GPR61 and PRKCB genes. These replicated T2D associations reflect both likely causal and consequential pathways of the disease. The analysis indicates how an integrated genomics and epigenomics approach, utilizing an MZ twin design, can provide pathogenic insights as well as potential drug targets and biomarkers for T2D and other complex traits., Type 2 diabetes (T2D) is a highly heterogeneous disease with a strong genetic component. Here the authors examine genome-wide methylation patterns in T2D-discordant, T2D-concordant and healthy concordant monozygotic twin pairs, and identify DNA methylation signals that may represent new biomarkers or drug targets for T2D.

Background Epigenome-wide association scans (EWAS) are an increasingly powerful and widely-used approach to assess the role of epigenetic variation in human complex traits. However, this rapidly emerging field lacks dedicated visualisation tools that can display features specific to epigenetic datasets. Result We developed coMET, an R package and online tool for visualisation of EWAS results in a genomic region of interest. coMET generates a regional plot of epigenetic-phenotype association results and the estimated DNA methylation correlation between CpG sites (co-methylation), with further options to visualise genomic annotations based on ENCODE data, gene tracks, reference CpG-sites, and user-defined features. The tool can be used to display phenotype association signals and correlation patterns of microarray or sequencing-based DNA methylation data, such as Illumina Infinium 450k, WGBS, or MeDIP-seq, as well as other types of genomic data, such as gene expression profiles. The software is available as a user-friendly online tool from http://epigen.kcl.ac.uk/cometand as an R Bioconductor package. Source code, examples, and full documentation are also available from GitHub. Conclusion Our new software allows visualisation of EWAS results with functional genomic annotations and with estimation of co-methylation patterns. coMET is available to a wide audience as an online tool and R package, and can be a valuable resource to interpret results in the fast growing field of epigenetics. The software is designed for epigenetic data, but can also be applied to genomic and functional genomic datasets in any species.

Because of the dearth of biomarkers of aging, it has been difficult to test the hypothesis that obesity increases tissue age. Here we use a novel epigenetic biomarker of aging (referred to as an "epigenetic clock") to study the relationship between high body mass index (BMI) and the DNA methylation ages of human blood, liver, muscle, and adipose tissue. A significant correlation between BMI and epigenetic age acceleration could only be observed for liver (r = 0.42, P = 6.8 × 10(-4) in dataset 1 and r = 0.42, P = 1.2 × 10(-4) in dataset 2). On average, epigenetic age increased by 3.3 y for each 10 BMI units. The detected age acceleration in liver is not associated with the Nonalcoholic Fatty Liver Disease Activity Score or any of its component traits after adjustment for BMI. The 279 genes that are underexpressed in older liver samples are highly enriched (1.2 × 10(-9)) with nuclear mitochondrial genes that play a role in oxidative phosphorylation and electron transport. The epigenetic age acceleration, which is not reversible in the short term after rapid weight loss induced by bariatric surgery, may play a role in liver-related comorbidities of obesity, such as insulin resistance and liver cancer.

Monozygotic (MZ) twins share nearly all of their genetic variants and many similar environments before and after birth. However, they can also show phenotypic discordance for a wide range of traits. Differences at the epigenetic level may account for such discordances. It is well established that epigenetic states can contribute to phenotypic variation, including disease. Epigenetic states are dynamic and potentially reversible marks involved in gene regulation, which can be influenced by genetics, environment, and stochastic events. Here, we review advances in epigenetic studies of discordant MZ twins, focusing on disease. The study of epigenetics and disease using discordant MZ twins offers the opportunity to control for many potential confounders encountered in general population studies, such as differences in genetic background, early-life environmental exposure, age, gender, and cohort effects. Recently, analysis of disease-discordant MZ twins has been successfully used to study epigenetic mechanisms in aging, cancer, autoimmune disease, psychiatric, neurological, and multiple other traits. Epigenetic aberrations have been found in a range of phenotypes, and challenges have been identified, including sampling time, tissue specificity, validation, and replication. The results have relevance for personalized medicine approaches, including the identification of prognostic, diagnostic, and therapeutic targets. The findings also help to identify epigenetic markers of environmental risk and molecular mechanisms involved in disease and disease progression, which have implications both for understanding disease and for future medical research.

Background: Although genetic variation is believed to contribute to an individual’s susceptibility to major depressive disorder, genome-wide association studies have not yet identified associations that could explain the full etiology of the disease. Epigenetics is increasingly believed to play a major role in the development of common clinical phenotypes, including major depressive disorder. Results: Genome-wide MeDIP-Sequencing was carried out on a total of 50 monozygotic twin pairs from the UK and Australia that are discordant for depression. We show that major depressive disorder is associated with significant hypermethylation within the coding region of ZBTB20, and is replicated in an independent cohort of 356 unrelated case-control individuals. The twins with major depressive disorder also show increased global variation in methylation in comparison with their unaffected co-twins. ZBTB20 plays an essential role in the specification of the Cornu Ammonis-1 field identity in the developing hippocampus, a region previously implicated in the development of major depressive disorder. Conclusions: Our results suggest that aberrant methylation profiles affecting the hippocampus are associated with major depressive disorder and show the potential of the epigenetic twin model in neuro-psychiatric disease.

Epigenetic regulation of gene expression has been shown to change over time and may be associated with environmental exposures in common complex traits. Age-related hearing impairment is a complex disorder, known to be heritable, with heritability estimates of 57-70%. Epigenetic regulation might explain the observed difference in age of onset and magnitude of hearing impairment with age. Epigenetic epidemiology studies using unrelated samples can be limited in their ability to detect small effects, and recent epigenetic findings in twins underscore the power of this well matched study design. We investigated the association between venous blood DNA methylation epigenome-wide and hearing ability. Pure-tone audiometry (PTA) and Illumina HumanMethylation array data were obtained from female twin volunteers enrolled in the TwinsUK register. Two study groups were explored: first, an epigenome-wide association scan (EWAS) was performed in a discovery sample (n=115 subjects, age range: 47-83 years, Illumina 27 k array), then replication of the top ten associated probes from the discovery EWAS was attempted in a second unrelated sample (n=203, age range: 41-86 years, Illumina 450 k array). Finally, a set of monozygotic (MZ) twin pairs (n = 21 pairs) within the discovery sample (Illumina 27 k array) was investigated in more detail in an MZ discordance analysis. Hearing ability was strongly associated with DNA methylation levels in the promoter regions of several genes, including TCF25 (cg01161216, p = 6.6 × 10(-6)), FGFR1 (cg15791248, p = 5.7 × 10(-5) and POLE (cg18877514, p= 6.3 × 10(-5)). Replication of these results in a second sample confirmed the presence of differential methylation at TCF25 (p(replication)=6 × 10(-5)) and POLE (p(replication) =0.016). In the MZ discordance analysis, twins' intrapair difference in hearing ability correlated with DNA methylation differences at ACP6 (cg01377755, r= -0.75, p=1.2 × 10(-4)) and MEF2D (cg08156349, r= -0.75, p=1.4 × 10(-4)). Examination of gene expression in skin, suggests an influence of differential methylation on expression, which may account for the variation in hearing ability with age.

Smoking is a major risk factor in many diseases. Genome wide association studies have linked genes for nicotine dependence and smoking behavior to increased risk of cardiovascular, pulmonary, and malignant diseases. We conducted an epigenome wide association study in peripheral-blood DNA in 464 individuals (22 current smokers and 263 ex-smokers), using the Human Methylation 450 K array. Upon replication in an independent sample of 356 twins (41 current and 104 ex-smokers), we identified 30 probes in 15 distinct loci, all of which reached genome-wide significance in the combined analysis P < 5 × 10(-8). All but one probe (cg17024919) remained significant after adjusting for blood cell counts. We replicated all 9 known loci and found an independent signal at CPOX near GPR15. In addition, we found 6 new loci at PRSS23, AVPR1B, PSEN2, LINC00299, RPS6KA2, and KIAA0087. Most of the lead probes (13 out of 15) associated with cigarette smoking, overlapped regions of open chromatin (FAIRE and DNaseI hypersensitive sites) or/and H3K27Ac peaks (ENCODE data set), which mark regulatory elements. The effect of smoking on DNA methylation was partially reversible upon smoking cessation for longer than 3 months. We report the first statistically significant interaction between a SNP (rs2697768) and cigarette smoking on DNA methylation (cg03329539). We provide evidence that the metSNP for cg03329539 regulates expression of the CHRND gene located circa 95 Kb downstream of the methylation site. Our findings suggest the existence of dynamic, reversible site-specific methylation changes in response to cigarette smoking , which may contribute to the extended health risks associated with cigarette smoking.

Cyanobacteria were responsible for the oxygenation of the ancient atmosphere; however, the evolution of this phylum is enigmatic, as relatives have not been characterized. Here we use whole genome reconstruction of human fecal and subsurface aquifer metagenomic samples to obtain complete genomes for members of a new candidate phylum sibling to Cyanobacteria, for which we propose the designation ‘Melainabacteria’. Metabolic analysis suggests that the ancestors to both lineages were non-photosynthetic, anaerobic, motile, and obligately fermentative. Cyanobacterial light sensing may have been facilitated by regulators present in the ancestor of these lineages. The subsurface organism has the capacity for nitrogen fixation using a nitrogenase distinct from that in Cyanobacteria, suggesting nitrogen fixation evolved separately in the two lineages. We hypothesize that Cyanobacteria split from Melainabacteria prior or due to the acquisition of oxygenic photosynthesis. Melainabacteria remained in anoxic zones and differentiated by niche adaptation, including for symbiosis in the mammalian gut. DOI: http://dx.doi.org/10.7554/eLife.01102.001, eLife digest Microbes are ubiquitous in the world and exist in complex communities called microbiomes that have colonized many environments, including the human gut. Until modern techniques for sequencing nucleic acids became available, many of the organisms found in these microbiomes could not be studied because they could not be cultured in the laboratory. However, advances in sequencing technology have made it possible to study the evolution and properties of these microbes, including their impact on human health. Bacteria belonging to the phylum Cyanobacteria had a significant effect on the prehistoric Earth because they were the first organisms to produce gaseous oxygen as a byproduct of photosynthesis, and thus shaped the Earth’s oxygen-rich atmosphere. Early plants took up these bacteria in a symbiotic relationship, and plastids—the organelles in plant cells that perform photosynthesis and produce oxygen–are the descendants of Cyanobacteria. Organisms evolutionarily related to Cyanobacteria have been found in the human gut and in various aquatic sources, but these bacteria have not been studied because it has not been possible to isolate or culture them. Now, Di Rienzi, Sharon et al. have used modern sequencing techniques to obtain complete genomes for some of these bacteria, which they assign to a new phylum called Melainabacteria. By analyzing these genomes, Di Rienzi, Sharon et al. were able to make predictions about the cell structure and metabolic abilities of Melainabacteria. Like Cyanobacteria, they have two membranes surrounding the bacterial cell; unlike Cyanobacteria, however, they have flagella that propel them through liquid or across surfaces. Most interestingly, Melainabacteria are not able to perform photosynthesis, but instead produce energy through fermentation and release hydrogen gas that can be consumed by other microorganisms. The genome of the bacteria isolated from water reveals that it has the capacity to fix nitrogen. Cyanobacteria can also fix atmospheric nitrogen, but the protein complexes used by the two phyla are not related, which suggests that nitrogen fixation evolved after the evolutionary divergence of Cyanobacteria and Melainabacteria. By exploring previously published datasets of bacterial communities, Di Rienzi, Sharon et al. found that Melainabacteria are common in aquatic habitats. They are also prevalent in the guts of herbivorous mammals and humans with a predominantly vegetarian diet. Melainabacteria from the human gut also synthesize several B and K vitamins, which suggests that these bacteria are beneficial to their host because in addition to aiding with the digestion of plant fibers, they are also a source of vitamins. DOI: http://dx.doi.org/10.7554/eLife.01102.002

BACKGROUND: Human ageing is a complex, multifactorial process and early developmental factors affect health outcomes in old age. METHODS: Metabolomic profiling on fasting blood was carried out in 6055 individuals from the UK. Stepwise regression was performed to identify a panel of independent metabolites which could be used as a surrogate for age. We also investigated the association with birthweight overall and within identical discordant twins and with genome-wide methylation levels. RESULTS: We identified a panel of 22 metabolites which combined are strongly correlated with age (R(2) = 59%) and with age-related clinical traits independently of age. One particular metabolite, C-glycosyl tryptophan (C-glyTrp), correlated strongly with age (beta = 0.03, SE = 0.001, P = 7.0 × 10(-157)) and lung function (FEV1 beta = -0.04, SE = 0.008, P = 1.8 × 10(-8) adjusted for age and confounders) and was replicated in an independent population (n = 887). C-glyTrp was also associated with bone mineral density (beta = -0.01, SE = 0.002, P = 1.9 × 10(-6)) and birthweight (beta = -0.06, SE = 0.01, P = 2.5 × 10(-9)). The difference in C-glyTrp levels explained 9.4% of the variance in the difference in birthweight between monozygotic twins. An epigenome-wide association study in 172 individuals identified three CpG-sites, associated with levels of C-glyTrp (P < 2 × 10(-6)). We replicated one CpG site in the promoter of the WDR85 gene in an independent sample of 350 individuals (beta = -0.20, SE = 0.04, P = 2.9 × 10(-8)). WDR85 is a regulator of translation elongation factor 2, essential for protein synthesis in eukaryotes. CONCLUSIONS: Our data illustrate how metabolomic profiling linked with epigenetic studies can identify some key molecular mechanisms potentially determined in early development that produce long-term physiological changes influencing human health and ageing.

ObjectiveLumbar disc degeneration (LDD) is an important cause of low back pain, which is a common and costly problem. LDD is characterised by disc space narrowing and osteophyte growth at the circumference of the disc. To date, the agnostic search of the genome by genome-wide association (GWA) to identify common variants associated with LDD has not been fruitful. This study is the first GWA meta-analysis of LDD.MethodsWe have developed a continuous trait based on disc space narrowing and osteophytes growth which is measurable on all forms of imaging (plain radiograph, CT scan and MRI) and performed a meta-analysis of five cohorts of Northern European extraction each having GWA data imputed to HapMap V.2.ResultsThis study of 4600 individuals identified four single nucleotide polymorphisms with p−8, the threshold set for genome-wide significance. We identified a variant in the PARK2 gene (p=2.8×10−8) associated with LDD. Differential methylation at one CpG island of the PARK2 promoter was observed in a small subset of subjects (β=8.74×10−4, p=0.006).ConclusionsLDD accounts for a considerable proportion of low back pain and the pathogenesis of LDD is poorly understood. This work provides evidence of association of the PARK2 gene and suggests that methylation of the PARK2 promoter may influence degeneration of the intervertebral disc. This gene has not previously been considered a candidate in LDD and further functional work is needed on this hitherto unsuspected pathway.

Recent advances in molecular methods and next-generation sequencing technologies have allowed for the detection of epigenetic variation at an unprecedented level of resolution. With the availability of new genome-wide epigenetic assays, particularly for DNA methylation, epigenetic studies of human complex traits have moved towards epigenome-wide association scans (EWAS). Similar to genome-wide association scans (GWAS), EWAS aim to perform a genome-wide search for epigenetic variants that associate with complex phenotypes and have potential to identify novel genes and molecular pathways in common disease. However, unlike genetic variation, epigenetic variation can be dynamic, which has implications for EWAS methodology and design. This chapter discusses the analytical aspects of performing EWAS of DNA methylation changes in complex traits, as well as the potential to integrate genetic and epigenetic variation in the analysis of molecular mechanisms underlying complex phenotypes.