{"database":"EGA","file_versions":[],"scores":null,"additional":{"omics_type":["Genomics","Multiomics"],"dataset_type":["N/A"],"full_dataset_link":["https://ega-archive.org/datasets/EGAD00010000604"],"sample_count":["2195"],"description":["EGA dataset EGAD00010000604"],"repository":["EGA"],"title":["Title not provided"],"pubmed_abstract":["We tested whether DNA-methylation profiles account for inter-individual variation in body mass index (BMI) and height and whether they predict these phenotypes over and above genetic factors. Genetic predictors were derived from published summary results from the largest genome-wide association studies on BMI (n ∼ 350,000) and height (n ∼ 250,000) to date. We derived methylation predictors by estimating probe-trait effects in discovery samples and tested them in external samples. Methylation profiles associated with BMI in older individuals from the Lothian Birth Cohorts (LBCs, n = 1,366) explained 4.9% of the variation in BMI in Dutch adults from the LifeLines DEEP study (n = 750) but did not account for any BMI variation in adolescents from the Brisbane Systems Genetic Study (BSGS, n = 403). Methylation profiles based on the Dutch sample explained 4.9% and 3.6% of the variation in BMI in the LBCs and BSGS, respectively. Methylation profiles predicted BMI independently of genetic profiles in an additive manner: 7%, 8%, and 14% of variance of BMI in the LBCs were explained by the methylation predictor, the genetic predictor, and a model containing both, respectively. The corresponding percentages for LifeLines DEEP were 5%, 9%, and 13%, respectively, suggesting that the methylation profiles represent environmental effects. The differential effects of the BMI methylation profiles by age support previous observations of age modulation of genetic contributions. In contrast, methylation profiles accounted for almost no variation in height, consistent with a mainly genetic contribution to inter-individual variation. The BMI results suggest that combining genetic and epigenetic information might have greater utility for complex-trait prediction.","<h4>Background</h4>DNA methylation levels change with age. Recent studies have identified biomarkers of chronological age based on DNA methylation levels. It is not yet known whether DNA methylation age captures aspects of biological age.<h4>Results</h4>Here we test whether differences between people's chronological ages and estimated ages, DNA methylation age, predict all-cause mortality in later life. The difference between DNA methylation age and chronological age (Δage) was calculated in four longitudinal cohorts of older people. Meta-analysis of proportional hazards models from the four cohorts was used to determine the association between Δage and mortality. A 5-year higher Δage is associated with a 21% higher mortality risk, adjusting for age and sex. After further adjustments for childhood IQ, education, social class, hypertension, diabetes, cardiovascular disease, and APOE e4 status, there is a 16% increased mortality risk for those with a 5-year higher Δage. A pedigree-based heritability analysis of Δage was conducted in a separate cohort. The heritability of Δage was 0.43.<h4>Conclusions</h4>DNA methylation-derived measures of accelerated aging are heritable traits that predict mortality independently of health status, lifestyle factors, and known genetic factors.","<h4>Background</h4>The DNA methylation-based 'epigenetic clock' correlates strongly with chronological age, but it is currently unclear what drives individual differences. We examine cross-sectional and longitudinal associations between the epigenetic clock and four mortality-linked markers of physical and mental fitness: lung function, walking speed, grip strength and cognitive ability.<h4>Methods</h4>DNA methylation-based age acceleration (residuals of the epigenetic clock estimate regressed on chronological age) were estimated in the Lothian Birth Cohort 1936 at ages 70 (n = 920), 73 (n = 299) and 76 (n = 273) years. General cognitive ability, walking speed, lung function and grip strength were measured concurrently. Cross-sectional correlations between age acceleration and the fitness variables were calculated. Longitudinal change in the epigenetic clock estimates and the fitness variables were assessed via linear mixed models and latent growth curves. Epigenetic age acceleration at age 70 was used as a predictor of longitudinal change in fitness. Epigenome-wide association studies (EWASs) were conducted on the four fitness measures.<h4>Results</h4>Cross-sectional correlations were significant between greater age acceleration and poorer performance on the lung function, cognition and grip strength measures (r range: -0.07 to -0.05, P range: 9.7 x 10(-3) to 0.024). All of the fitness variables declined over time but age acceleration did not correlate with subsequent change over 6 years. There were no EWAS hits for the fitness traits.<h4>Conclusions</h4>Markers of physical and mental fitness are associated with the epigenetic clock (lower abilities associated with age acceleration). However, age acceleration does not associate with decline in these measures, at least over a relatively short follow-up.","Epigenetic mechanisms such as DNA methylation (DNAm) are essential for regulation of gene expression. DNAm is dynamic, influenced by both environmental and genetic factors. Epigenetic drift is the divergence of the epigenome as a function of age due to stochastic changes in methylation. Here we show that epigenetic drift may be constrained at many CpGs across the human genome by DNA sequence variation and by lifetime environmental exposures. We estimate repeatability of DNAm at 234,811 autosomal CpGs in whole blood using longitudinal data (2-3 repeated measurements) on 478 older people from two Scottish birth cohorts--the Lothian Birth Cohorts of 1921 and 1936. Median age was 79 yr and 70 yr, and the follow-up period was ∼10 yr and ∼6 yr, respectively. We compare this to methylation heritability estimated in the Brisbane Systems Genomics Study, a cross-sectional study of 117 families (offspring median age 13 yr; parent median age 46 yr). CpG repeatability in older people was highly correlated (0.68) with heritability estimated in younger people. Highly heritable sites had strong underlying cis-genetic effects. Thirty-seven and 1687 autosomal CpGs were associated with smoking and sex, respectively. Both sets were strongly enriched for high repeatability. Sex-associated CpGs were also strongly enriched for high heritability. Our results show that a large number of CpGs across the genome, as a result of environmental and/or genetic constraints, have stable DNAm variation over the human lifetime. Moreover, at a number of CpGs, most variation in the population is due to genetic factors, despite some sites being highly modifiable by the environment.","DNA-methylation (DNAm) levels at age-associated CpG sites can be combined into epigenetic aging signatures to estimate donor age. It has been demonstrated that the difference between such epigenetic age-predictions and chronological age is indicative for of all-cause mortality in later life. In this study, we tested alternative epigenetic signatures and followed the hypothesis that even individual age-associated CpG sites might be indicative for life-expectancy. Using a 99-CpG aging model, a five-year higher age-prediction was associated with 11% greater mortality risk in DNAm profiles of the Lothian Birth Cohort 1921 study. However, models based on three CpGs, or even individual CpGs, generally revealed very high offsets in age-predictions if applied to independent microarray datasets. On the other hand, we demonstrate that DNAm levels at several individual age-associated CpGs seem to be associated with life expectancy - e.g., at CpGs associated with the genesPDE4C and CLCN6. Our results support the notion that small aging signatures should rather be analysed by more quantitative methods, such as site-specific pyrosequencing, as the precision of age-predictions is rather low on independent microarray datasets. Nevertheless, the results hold the perspective that simple epigenetic biomarkers, based on few or individual age-associated CpGs, could assist the estimation of biological age."],"pubmed_title":["Genetic and environmental exposures constrain epigenetic drift over the human life course.","Improving Phenotypic Prediction by Combining Genetic and Epigenetic Associations.","The epigenetic clock is correlated with physical and cognitive fitness in the Lothian Birth Cohort 1936.","DNA methylation levels at individual age-associated CpG sites can be indicative for life expectancy.","DNA methylation age of blood predicts all-cause mortality in later life."],"pubmed_authors":["Marioni Riccardo E RE, Shah Sonia S, McRae Allan F AF, Ritchie Stuart J SJ, Muniz-Terrera Graciela G, Harris Sarah E SE, Gibson Jude J, Redmond Paul P, Cox Simon R SR, Pattie Alison A, Corley Janie J, Taylor Adele A, Murphy Lee L, Starr John M JM, Horvath Steve S, Visscher Peter M PM, Wray Naomi R NR, Deary Ian J IJ","Shah Sonia S, Bonder Marc J MJ, Marioni Riccardo E RE, Zhu Zhihong Z, McRae Allan F AF, Zhernakova Alexandra A, Harris Sarah E SE, Liewald Dave D, Henders Anjali K AK, Mendelson Michael M MM, Liu Chunyu C, Joehanes Roby R, Liang Liming L, Levy Daniel D, Martin Nicholas G NG, Starr John M JM, Wijmenga Cisca C, Wray Naomi R NR, Yang Jian J, Montgomery Grant W GW, Franke Lude L, Deary Ian J IJ, Visscher Peter M PM","Shah Sonia S, McRae Allan F AF, Marioni Riccardo E RE, Harris Sarah E SE, Gibson Jude J, Henders Anjali K AK, Redmond Paul P, Cox Simon R SR, Pattie Alison A, Corley Janie J, Murphy Lee L, Martin Nicholas G NG, Montgomery Grant W GW, Starr John M JM, Wray Naomi R NR, Deary Ian J IJ, Visscher Peter M PM","Marioni Riccardo E RE, Shah Sonia S, McRae Allan F AF, Chen Brian H BH, Colicino Elena E, Harris Sarah E SE, Gibson Jude J, Henders Anjali K AK, Redmond Paul P, Cox Simon R SR, Pattie Alison A, Corley Janie J, Murphy Lee L, Martin Nicholas G NG, Montgomery Grant W GW, Feinberg Andrew P AP, Fallin M Daniele MD, Multhaup Michael L ML, Jaffe Andrew E AE, Joehanes Roby R, Schwartz Joel J, Just Allan C AC, Lunetta Kathryn L KL, Murabito Joanne M JM, Starr John M JM, Horvath Steve S, Baccarelli Andrea A AA, Levy Daniel D, Visscher Peter M PM, Wray Naomi R NR, Deary Ian J IJ","Lin Qiong Q, Weidner Carola I CI, Costa Ivan G IG, Marioni Riccardo E RE, Ferreira Marcelo R P MR, Deary Ian J IJ, Wagner Wolfgang W"],"name_synonyms":["lobular, ARHGEF13, gastric cancer, Methylations., Ht31, familial diffuse breast cancer, BRX, c-lbc, PROTO-LB, LBC, PRKA13, p47, DGLBC, methylation, HDGC, hereditary diffuse, diffuse gastric and lobular breast cancer syndrome, PROTO-LBC, AKAP-Lbc, HA-3, AKAP-13"],"pubmed_title_synonyms":["genetic, Human, Exposures, Environmental, lifespan, human being, Man (Taxonomy), Homo sapiens, Exposure, Epigenetic, entire lifespan, Modern Man, entire life cycle, Modern, familial, life., inherited genetic, Epigenetics, constitutitional genetic, Environmental Exposures, hereditary, Man, human, Epigenomic"],"description_synonyms":["DNA, DNA methylation maintenance, DNA Methylations, DNA., DNA methylation, Methylations, Methylation"],"pubmed_abstract_synonyms":["Teenager, Genome-Wide Association, Female Adolescent, DNS, Female Adolescents, Male, (Deoxyribonucleotide)n, Index, RnBP, DNAn+1, Genome Wide Association Analysis, Whole Genome Association Study, familial, Youths, GlcNAc 2-epimerase, Male Adolescents, Profiles, Double-Stranded, Teenagers, Genome-Wide, Deoxyribonucleic acids, predicted, height, (Deoxyribonucleotide)n+m, Genotype Profile, N-acetyl-D-glucosamine 2-epimerase, GWA Studies, Deoxyribonucleic Acid, Youth, Genotype, Body Mass, Genome Wide Association Study, RENBP, Quetelet's Index, Epigenomic., Studies, Birth Cohorts, Cohort, methylation, Whole Genome Association Analysis, ds-DNA, Adolescent, desoxyribose nucleic acid, Adults, adult, Quetelet Index, Adolescence, study, GWA Study, thymus nucleic acid, Genetic, Epigenetic, Birth, Profile, GWA, Double Stranded, Deoxyribonucleic acid, Female, Adolescents, Teen, sample population, Genome Wide Association Studies, BMI, Genome Wide Association Scan, Genome-Wide Association Studies, AGE, genetic, Study, Phenotypes, Quetelets Index, Quetelet, sample, ds DNA, Desoxyribonukleinsaeure, Genetic Profiles, Association Studies, renin-binding protein, Double-Stranded DNA, inherited genetic, (Deoxyribonucleotide)m, Association Study, Male Adolescent, Genotype Profiles, DNA, deoxyribonucleic acids, Teens, DNAn, Epigenetics, constitutitional genetic, hereditary, Methylations"],"additional_accession":[]},"is_claimable":false,"name":"LBC_Methylation - samples","description":"DNA methylation data using Illumina 450K","dates":{"updated":"2017-07-26 15:39:25"},"accession":"EGAD00010000604","cross_references":{"TAXONOMY":["9606"],"pubmed":["25617346","26928272","26119815","25633388","25249537"],"EGA":["EGAC00001000218","EGAS00001000910"]}}