Aims/hypothesis
Tobacco smoking, a risk factor for diabetes, is an established modifier of DNA methylation. We hypothesised that tobacco smoking modifies DNA methylation of genes previously ...identified for diabetes.
Methods
We annotated CpG sites available on the Illumina Human Methylation 450K array to diabetes genes previously identified by genome-wide association studies (GWAS), and investigated them for an association with smoking by comparing current to never smokers. The discovery study consisted of 630 individuals (Bonferroni-corrected
p
= 1.4 × 10
−5
), and we sought replication in an independent sample of 674 individuals. The replicated sites were tested for association with nearby genetic variants and gene expression and fasting glucose and insulin levels.
Results
We annotated 3,620 CpG sites to the genes identified in the GWAS on type 2 diabetes. Comparing current smokers to never smokers, we found 12 differentially methylated CpG sites, of which five replicated: cg23161492 within
ANPEP
(
p
= 1.3 × 10
−12
); cg26963277 (
p
= 1.2 × 10
−9
), cg01744331 (
p
= 8.0 × 10
−6
) and cg16556677 (
p
= 1.2 × 10
−5
) within
KCNQ1
and cg03450842 (
p
= 3.1 × 10
−8
) within
ZMIZ1
. The effect of smoking on DNA methylation at the replicated CpG sites attenuated after smoking cessation. Increased DNA methylation at cg23161492 was associated with decreased gene expression levels of
ANPEP
(
p
= 8.9 × 10
−5
). rs231356-T, which was associated with hypomethylation of cg26963277 (
KCNQ1
), was associated with a higher odds of diabetes (OR 1.06,
p
= 1.3 × 10
−5
). Additionally, hypomethylation of cg26963277 was associated with lower fasting insulin levels (
p
= 0.04).
Conclusions/interpretation
Tobacco smoking is associated with differential DNA methylation of the diabetes risk genes
ANPEP
,
KCNQ1
and
ZMIZ1
. Our study highlights potential biological mechanisms connecting tobacco smoking to excess risk of type 2 diabetes.
Background:
Smoking-associated DNA methylation levels identified through epigenome-wide association studies (EWASs) are generally ascribed to smoking-reactive mechanisms, but the contribution of a ...shared genetic predisposition to smoking and DNA methylation levels is typically not accounted for.
Methods:
We exploited a strong within-family design, that is, the discordant monozygotic twin design, to study reactiveness of DNA methylation in blood cells to smoking and reversibility of methylation patterns upon quitting smoking. Illumina HumanMethylation450 BeadChip data were available for 769 monozygotic twin pairs (mean age = 36 years, range = 18–78, 70% female), including pairs discordant or concordant for current or former smoking.
Results:
In pairs discordant for current smoking, 13 differentially methylated CpGs were found between current smoking twins and their genetically identical co-twin who never smoked. Top sites include multiple CpGs in
CACNA1D
and
GNG12
, which encode subunits of a calcium voltage-gated channel and G protein, respectively. These proteins interact with the nicotinic acetylcholine receptor, suggesting that methylation levels at these CpGs might be reactive to nicotine exposure. All 13 CpGs have been previously associated with smoking in unrelated individuals and data from monozygotic pairs discordant for former smoking indicated that methylation patterns are to a large extent reversible upon smoking cessation. We further showed that differences in smoking level exposure for monozygotic twins who are both current smokers but differ in the number of cigarettes they smoke are reflected in their DNA methylation profiles.
Conclusions:
In conclusion, by analysing data from monozygotic twins, we robustly demonstrate that DNA methylation level in human blood cells is reactive to cigarette smoking.
Funding:
We acknowledge funding from the National Institute on Drug Abuse grant DA049867, the Netherlands Organization for Scientific Research (NWO): Biobanking and Biomolecular Research Infrastructure (BBMRI-NL, NWO 184.033.111) and the BBRMI-NL-financed BIOS Consortium (NWO 184.021.007), NWO Large Scale infrastructures X-Omics (184.034.019), Genotype/phenotype database for behaviour genetic and genetic epidemiological studies (ZonMw Middelgroot 911-09-032); Netherlands Twin Registry Repository: researching the interplay between genome and environment (NWO-Groot 480-15-001/674); the Avera Institute, Sioux Falls (USA), and the National Institutes of Health (NIH R01 HD042157-01A1, MH081802, Grand Opportunity grants 1RC2 MH089951 and 1RC2 MH089995); epigenetic data were generated at the Human Genomics Facility (HuGe-F) at ErasmusMC Rotterdam. Cotinine assaying was sponsored by the Neuroscience Campus Amsterdam. DIB acknowledges the Royal Netherlands Academy of Science Professor Award (PAH/6635).
The genetic information of people who smoke present distinctive characteristics. In particular, previous research has revealed differences in patterns of DNA methylation, a type of chemical modification that helps cells switch certain genes on or off. However, most of these studies could not establish for sure whether these changes were caused by smoking, predisposed individuals to smoke, or were driven by underlying genetic variation in the DNA sequence itself.
To investigate this question, van Dongen et al. examined DNA methylation data from the blood cells of over 700 pairs of identical twins. These individuals share the exact same genetic information, making it possible to better evaluate the impact of lifestyle on DNA modifications.
The analyses identified differences in methylation at 13 DNA locations in pairs of twins where one was a current smoker and their sibling had never smoked. Two of the genes code for proteins involved in the response to nicotine, the primary addictive chemical in cigarette smoke. The differences were smaller if one of the twins had stopped smoking, suggesting that quitting can help to reverse some of these changes.
These findings confirm that DNA methylation in blood cells is influenced by cigarette smoke, which could help to better understand smoking-associated diseases. They also demonstrate how useful identical twins studies can be to identify methylation changes that are markers of lifestyle.
X-chromosome inactivation (XCI), i.e., the inactivation of one of the female X chromosomes, restores equal expression of X-chromosomal genes between females and males. However, ~10% of genes show ...variable degrees of escape from XCI between females, although little is known about the causes of variable XCI. Using a discovery data-set of 1867 females and 1398 males and a replication sample of 3351 females, we show that genetic variation at three autosomal loci is associated with female-specific changes in X-chromosome methylation. Through cis-eQTL expression analysis, we map these loci to the genes SMCHD1/METTL4, TRIM6/HBG2, and ZSCAN9. Low-expression alleles of the loci are predominantly associated with mild hypomethylation of CpG islands near genes known to variably escape XCI, implicating the autosomal genes in variable XCI. Together, these results suggest a genetic basis for variable escape from XCI and highlight the potential of a population genomics approach to identify genes involved in XCI.
We show that epigenome- and transcriptome-wide association studies (EWAS and TWAS) are prone to significant inflation and bias of test statistics, an unrecognized phenomenon introducing spurious ...findings if left unaddressed. Neither GWAS-based methodology nor state-of-the-art confounder adjustment methods completely remove bias and inflation. We propose a Bayesian method to control bias and inflation in EWAS and TWAS based on estimation of the empirical null distribution. Using simulations and real data, we demonstrate that our method maximizes power while properly controlling the false positive rate. We illustrate the utility of our method in large-scale EWAS and TWAS meta-analyses of age and smoking.
Most disease-associated genetic variants are noncoding, making it challenging to design experiments to understand their functional consequences. Identification of expression quantitative trait loci ...(eQTLs) has been a powerful approach to infer the downstream effects of disease-associated variants, but most of these variants remain unexplained. The analysis of DNA methylation, a key component of the epigenome, offers highly complementary data on the regulatory potential of genomic regions. Here we show that disease-associated variants have widespread effects on DNA methylation in trans that likely reflect differential occupancy of trans binding sites by cis-regulated transcription factors. Using multiple omics data sets from 3,841 Dutch individuals, we identified 1,907 established trait-associated SNPs that affect the methylation levels of 10,141 different CpG sites in trans (false discovery rate (FDR) < 0.05). These included SNPs that affect both the expression of a nearby transcription factor (such as NFKB1, CTCF and NKX2-3) and methylation of its respective binding site across the genome. Trans methylation QTLs effectively expose the downstream effects of disease-associated variants.
Identification of causal drivers behind regulatory gene networks is crucial in understanding gene function. Here, we develop a method for the large-scale inference of gene-gene interactions in ...observational population genomics data that are both directed (using local genetic instruments as causal anchors, akin to Mendelian Randomization) and specific (by controlling for linkage disequilibrium and pleiotropy). Analysis of genotype and whole-blood RNA-sequencing data from 3072 individuals identified 49 genes as drivers of downstream transcriptional changes (Wald P < 7 × 10
), among which transcription factors were overrepresented (Fisher's P = 3.3 × 10
). Our analysis suggests new gene functions and targets, including for SENP7 (zinc-finger genes involved in retroviral repression) and BCL2A1 (target genes possibly involved in auditory dysfunction). Our work highlights the utility of population genomics data in deriving directed gene expression networks. A resource of trans-effects for all 6600 genes with a genetic instrument can be explored individually using a web-based browser.
Genetic risk factors often localize to noncoding regions of the genome with unknown effects on disease etiology. Expression quantitative trait loci (eQTLs) help to explain the regulatory mechanisms ...underlying these genetic associations. Knowledge of the context that determines the nature and strength of eQTLs may help identify cell types relevant to pathophysiology and the regulatory networks underlying disease. Here we generated peripheral blood RNA-seq data from 2,116 unrelated individuals and systematically identified context-dependent eQTLs using a hypothesis-free strategy that does not require previous knowledge of the identity of the modifiers. Of the 23,060 significant cis-regulated genes (false discovery rate (FDR) ≤ 0.05), 2,743 (12%) showed context-dependent eQTL effects. The majority of these effects were influenced by cell type composition. A set of 145 cis-eQTLs depended on type I interferon signaling. Others were modulated by specific transcription factors binding to the eQTL SNPs.
There is considerable divergence in the sequences of steroid receptor response elements, including the vitamin D response elements (VDREs). Two major VDRE-containing and thus 1,25-dihydroxyvitamin ...D3(1,25-(OH)2D3)-regulated genes are the two non-collagenous, osteoblast-derived bone matrix proteins osteocalcin and osteopontin. We observed a stronger induction of osteopontin than osteocalcin mRNA expression by 1,25-(OH)2D3. Subsequently, we have shown that vitamin D receptor/retinoid X receptor α (VDR/RXRα) heterodimers bind more tightly to the osteopontin VDRE than to the osteocalcin VDRE. Studies using point mutants revealed that the internal dinucleotide at positions 3 and 4 of the proximal steroid half-element are most important for modulating the strength of receptor binding. In addition, studies with VDRE-driven luciferase reporter gene constructs revealed that the central dinucleotide influences the transactivation potential of VDR/RXRα with the same order of magnitude as that observed in the DNA binding studies. The synthetic vitamin D analog KH1060 is a more potent stimulator of transcription and inducer of VDRE binding of VDR/RXR in the presence of nuclear factors isolated from ROS 17/2.8 osteoblast-like cells than the natural ligand 1,25-(OH)2D3. Interestingly, however, KH1060 is comparable or even less potent than 1,25-(OH)2D3 in stimulating VDRE binding ofin vitro synthesized VDR/RXRα. Thus, the extent of 1,25-(OH)2D3- and KH1060-dependent binding of VDR/RXRα is specified by a central dinucleotide in the VDRE, and the ligand-induced effects on DNA binding are in part controlled by the cellular context of nuclear proteins.
The methylome is subject to genetic and environmental effects. Their impact may depend on sex and age, resulting in sex- and age-related physiological variation and disease susceptibility. Here we ...estimate the total heritability of DNA methylation levels in whole blood and estimate the variance explained by common single nucleotide polymorphisms at 411,169 sites in 2,603 individuals from twin families, to establish a catalogue of between-individual variation in DNA methylation. Heritability estimates vary across the genome (mean=19%) and interaction analyses reveal thousands of sites with sex-specific heritability as well as sites where the environmental variance increases with age. Integration with previously published data illustrates the impact of genome and environment across the lifespan at methylation sites associated with metabolic traits, smoking and ageing. These findings demonstrate that our catalogue holds valuable information on locations in the genome where methylation variation between people may reflect disease-relevant environmental exposures or genetic variation.
Recent genome-wide (GW) scans have identified several independent loci affecting human stature, but their contribution through the different skeletal components of height is still poorly understood. ...We carried out a genome-wide scan in 12,611 participants, followed by replication in an additional 7,187 individuals, and identified 17 genomic regions with GW-significant association with height. Of these, two are entirely novel (rs11809207 in CATSPER4, combined P-value = 6.1x10(-8) and rs910316 in TMED10, P-value = 1.4x10(-7)) and two had previously been described with weak statistical support (rs10472828 in NPR3, P-value = 3x10(-7) and rs849141 in JAZF1, P-value = 3.2x10(-11)). One locus (rs1182188 at GNA12) identifies the first height eQTL. We also assessed the contribution of height loci to the upper- (trunk) and lower-body (hip axis and femur) skeletal components of height. We find evidence for several loci associated with trunk length (including rs6570507 in GPR126, P-value = 4x10(-5) and rs6817306 in LCORL, P-value = 4x10(-4)), hip axis length (including rs6830062 at LCORL, P-value = 4.8x10(-4) and rs4911494 at UQCC, P-value = 1.9x10(-4)), and femur length (including rs710841 at PRKG2, P-value = 2.4x10(-5) and rs10946808 at HIST1H1D, P-value = 6.4x10(-6)). Finally, we used conditional analyses to explore a possible differential contribution of the height loci to these different skeletal size measurements. In addition to validating four novel loci controlling adult stature, our study represents the first effort to assess the contribution of genetic loci to three skeletal components of height. Further statistical tests in larger numbers of individuals will be required to verify if the height loci affect height preferentially through these subcomponents of height.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
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