Author
Caroline L Relton
Other affiliations: Health Science University, University of Newcastle, University Hospitals Bristol NHS Foundation Trust ...read more
Bio: Caroline L Relton is an academic researcher from University of Bristol. The author has contributed to research in topics: DNA methylation & Mendelian randomization. The author has an hindex of 71, co-authored 394 publications receiving 17221 citations. Previous affiliations of Caroline L Relton include Health Science University & University of Newcastle.
Papers published on a yearly basis
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TL;DR: MR-Base is a platform that integrates a curated database of complete GWAS results (no restrictions according to statistical significance) with an application programming interface, web app and R packages that automate 2SMR, and includes several sensitivity analyses for assessing the impact of horizontal pleiotropy and other violations of assumptions.
Abstract: Results from genome-wide association studies (GWAS) can be used to infer causal relationships between phenotypes, using a strategy known as 2-sample Mendelian randomization (2SMR) and bypassing the need for individual-level data. However, 2SMR methods are evolving rapidly and GWAS results are often insufficiently curated, undermining efficient implementation of the approach. We therefore developed MR-Base ( http://www.mrbase.org ): a platform that integrates a curated database of complete GWAS results (no restrictions according to statistical significance) with an application programming interface, web app and R packages that automate 2SMR. The software includes several sensitivity analyses for assessing the impact of horizontal pleiotropy and other violations of assumptions. The database currently comprises 11 billion single nucleotide polymorphism-trait associations from 1673 GWAS and is updated on a regular basis. Integrating data with software ensures more rigorous application of hypothesis-driven analyses and allows millions of potential causal relationships to be efficiently evaluated in phenome-wide association studies.
2,520 citations
1,061 citations
Wellcome Trust Centre for Human Genetics1, Imperial College London2, Agency for Science, Technology and Research3, University of Oulu4, National Institutes of Health5, King's College London6, Ealing Hospital7, National University of Singapore8, University of Turin9, University Medical Center Groningen10, University of Tartu11, University of Bristol12, University College London13, University of Eastern Finland14, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico15, University of Kiel16, Leiden University Medical Center17, Dresden University of Technology18, University of Düsseldorf19, University of Surrey20, Erasmus University Rotterdam21, Max Healthcare22, Technische Universität München23, University of Naples Federico II24, Wellcome Trust Sanger Institute25, Science for Life Laboratory26, University of Ulm27, Ludwig Maximilian University of Munich28, University of Kelaniya29, Institute of Cancer Research30, Queen Mary University of London31, King Abdulaziz University32, Massachusetts Institute of Technology33, Health Protection Agency34, University of Oxford35, Churchill Hospital36, Imperial College Healthcare37
TL;DR: In this article, the authors used epigenome-wide association to show that body mass index (BMI), a key measure of adiposity, is associated with widespread changes in DNA methylation.
Abstract: Approximately 1.5 billion people worldwide are overweight or affected by obesity, and are at risk of developing type 2 diabetes, cardiovascular disease and related metabolic and inflammatory disturbances1,2. Although the mechanisms linking adiposity to associated clinical conditions are poorly understood, recent studies suggest that adiposity may influence DNA methylation3,4,5,6, a key regulator of gene expression and molecular phenotype7. Here we use epigenome-wide association to show that body mass index (BMI; a key measure of adiposity) is associated with widespread changes in DNA methylation (187 genetic loci with P < 1 × 10−7, range P = 9.2 × 10−8 to 6.0 × 10−46; n = 10,261 samples). Genetic association analyses demonstrate that the alterations in DNA methylation are predominantly the consequence of adiposity, rather than the cause. We find that methylation loci are enriched for functional genomic features in multiple tissues (P < 0.05), and show that sentinel methylation markers identify gene expression signatures at 38 loci (P < 9.0 × 10−6, range P = 5.5 × 10−6 to 6.1 × 10−35, n = 1,785 samples). The methylation loci identify genes involved in lipid and lipoprotein metabolism, substrate transport and inflammatory pathways. Finally, we show that the disturbances in DNA methylation predict future development of type 2 diabetes (relative risk per 1 standard deviation increase in methylation risk score: 2.3 (2.07–2.56); P = 1.1 × 10−54). Our results provide new insights into the biologic pathways influenced by adiposity, and may enable development of new strategies for prediction and prevention of type 2 diabetes and other adverse clinical consequences of obesity.
667 citations
International Agency for Research on Cancer1, University of Bristol2, University Hospitals Bristol NHS Foundation Trust3, Vanderbilt University Medical Center4, University of Kentucky5, University of Copenhagen6, Lund University7, Technische Universität München8, Fred Hutchinson Cancer Research Center9, Harvard University10, Dartmouth College11, University of Liverpool12, Umeå University13, National Institute of Occupational Health14, New Generation University College15, Radboud University Nijmegen16, BC Cancer Agency17, Washington State University18, University of Hawaii19, Ontario Institute for Cancer Research20, University of Southern California21, Technion – Israel Institute of Technology22, University of Salzburg23, Curie Institute24, Nofer Institute of Occupational Medicine25, University of Ostrava26, Charles University in Prague27, Nanjing Medical University28, University of Oviedo29, University of Sheffield30, University of Texas MD Anderson Cancer Center31, University of Pittsburgh32, Lunenfeld-Tanenbaum Research Institute33
TL;DR: The results are consistent with a causal role of fasting insulin and low-density lipoprotein cholesterol in lung cancer etiology, as well as for BMI in squamous cell and small cell carcinoma, and the latter relation may be mediated by a previously unrecognized effect of obesity on smoking behavior.
Abstract: Background: Assessing the relationship between lung cancer and metabolic conditions is challenging because of the confounding effect of tobacco. Mendelian randomization (MR), or the use of genetic ...
653 citations
United States Department of Health and Human Services1, Erasmus University Rotterdam2, University of California, Berkeley3, Johns Hopkins University4, Icahn School of Medicine at Mount Sinai5, University of Southern California6, Duke University7, University of Bristol8, University Medical Center Groningen9, University of California, San Francisco10, North Carolina State University11, Karolinska Institutet12, Pompeu Fabra University13, University of Paris14, University of Memphis15, Centre Hospitalier Universitaire de Grenoble16, University of Bergen17, Isfahan University of Medical Sciences18, Brigham and Women's Hospital19, Oslo University Hospital20, Utrecht University21, French Institute of Health and Medical Research22, Norwegian Institute of Public Health23, Johns Hopkins University School of Medicine24, Harvard University25, International Agency for Research on Cancer26, Paris Descartes University27, Michigan State University28, Centre national de la recherche scientifique29, Fred Hutchinson Cancer Research Center30, Swiss Tropical and Public Health Institute31, University of Basel32, Stockholm County Council33, University of Southampton34
TL;DR: This large scale meta-analysis of methylation data identified numerous loci involved in response to maternal smoking in pregnancy with persistence into later childhood and provide insights into mechanisms underlying effects of this important exposure.
Abstract: Epigenetic modifications, including DNA methylation, represent a potential mechanism for environmental impacts on human disease. Maternal smoking in pregnancy remains an important public health problem that impacts child health in a myriad of ways and has potential lifelong consequences. The mechanisms are largely unknown, but epigenetics most likely plays a role. We formed the Pregnancy And Childhood Epigenetics (PACE) consortium and meta-analyzed, across 13 cohorts (n = 6,685), the association between maternal smoking in pregnancy and newborn blood DNA methylation at over 450,000 CpG sites (CpGs) by using the Illumina 450K BeadChip. Over 6,000 CpGs were differentially methylated in relation to maternal smoking at genome-wide statistical significance (false discovery rate, 5%), including 2,965 CpGs corresponding to 2,017 genes not previously related to smoking and methylation in either newborns or adults. Several genes are relevant to diseases that can be caused by maternal smoking (e.g., orofacial clefts and asthma) or adult smoking (e.g., certain cancers). A number of differentially methylated CpGs were associated with gene expression. We observed enrichment in pathways and processes critical to development. In older children (5 cohorts, n = 3,187), 100% of CpGs gave at least nominal levels of significance, far more than expected by chance (p value < 2.2 × 10(-16)). Results were robust to different normalization methods used across studies and cell type adjustment. In this large scale meta-analysis of methylation data, we identified numerous loci involved in response to maternal smoking in pregnancy with persistence into later childhood and provide insights into mechanisms underlying effects of this important exposure.
646 citations
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01 Jan 2016
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5,249 citations
01 Feb 2015
TL;DR: In this article, the authors describe the integrative analysis of 111 reference human epigenomes generated as part of the NIH Roadmap Epigenomics Consortium, profiled for histone modification patterns, DNA accessibility, DNA methylation and RNA expression.
Abstract: The reference human genome sequence set the stage for studies of genetic variation and its association with human disease, but epigenomic studies lack a similar reference. To address this need, the NIH Roadmap Epigenomics Consortium generated the largest collection so far of human epigenomes for primary cells and tissues. Here we describe the integrative analysis of 111 reference human epigenomes generated as part of the programme, profiled for histone modification patterns, DNA accessibility, DNA methylation and RNA expression. We establish global maps of regulatory elements, define regulatory modules of coordinated activity, and their likely activators and repressors. We show that disease- and trait-associated genetic variants are enriched in tissue-specific epigenomic marks, revealing biologically relevant cell types for diverse human traits, and providing a resource for interpreting the molecular basis of human disease. Our results demonstrate the central role of epigenomic information for understanding gene regulation, cellular differentiation and human disease.
4,409 citations
TL;DR: MR-Base is a platform that integrates a curated database of complete GWAS results (no restrictions according to statistical significance) with an application programming interface, web app and R packages that automate 2SMR, and includes several sensitivity analyses for assessing the impact of horizontal pleiotropy and other violations of assumptions.
Abstract: Results from genome-wide association studies (GWAS) can be used to infer causal relationships between phenotypes, using a strategy known as 2-sample Mendelian randomization (2SMR) and bypassing the need for individual-level data. However, 2SMR methods are evolving rapidly and GWAS results are often insufficiently curated, undermining efficient implementation of the approach. We therefore developed MR-Base ( http://www.mrbase.org ): a platform that integrates a curated database of complete GWAS results (no restrictions according to statistical significance) with an application programming interface, web app and R packages that automate 2SMR. The software includes several sensitivity analyses for assessing the impact of horizontal pleiotropy and other violations of assumptions. The database currently comprises 11 billion single nucleotide polymorphism-trait associations from 1673 GWAS and is updated on a regular basis. Integrating data with software ensures more rigorous application of hypothesis-driven analyses and allows millions of potential causal relationships to be efficiently evaluated in phenome-wide association studies.
2,520 citations