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      Systematic Mendelian randomization framework elucidates hundreds of CpG sites which may mediate the influence of genetic variants on disease

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          Abstract

          We have undertaken a systematic Mendelian randomization (MR) study using methylation quantitative trait loci (meQTL) as genetic instruments to assess the relationship between genetic variation, DNA methylation and 139 complex traits. Using two-sample MR, we identified 1148 associations across 61 traits where genetic variants were associated with both proximal DNA methylation (i.e. cis-meQTL) and complex trait variation ( P < 1.39 × 10 −08). Joint likelihood mapping provided evidence that the genetic variant which influenced DNA methylation levels for 348 of these associations across 47 traits was also responsible for variation in complex traits. These associations showed a high rate of replication in the BIOS QTL and UK Biobank datasets for 14 selected traits, as 101 of the attempted 128 associations survived multiple testing corrections ( P < 3.91 × 10 −04). Integrating expression quantitative trait loci (eQTL) data suggested that genetic variants responsible for 306 of the 348 refined meQTL associations also influence gene expression, which indicates a coordinated system of effects that are consistent with causality. CpG sites were enriched for histone mark peaks in tissue types relevant to their associated trait and implicated genes were enriched across relevant biological pathways. Though we are unable to distinguish mediation from horizontal pleiotropy in these analyses, our findings should prove valuable in prioritizing candidate loci where DNA methylation may influence traits and help develop mechanistic insight into the aetiology of complex disease.

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          'Mendelian randomization': can genetic epidemiology contribute to understanding environmental determinants of disease?

          Associations between modifiable exposures and disease seen in observational epidemiology are sometimes confounded and thus misleading, despite our best efforts to improve the design and analysis of studies. Mendelian randomization-the random assortment of genes from parents to offspring that occurs during gamete formation and conception-provides one method for assessing the causal nature of some environmental exposures. The association between a disease and a polymorphism that mimics the biological link between a proposed exposure and disease is not generally susceptible to the reverse causation or confounding that may distort interpretations of conventional observational studies. Several examples where the phenotypic effects of polymorphisms are well documented provide encouraging evidence of the explanatory power of Mendelian randomization and are described. The limitations of the approach include confounding by polymorphisms in linkage disequilibrium with the polymorphism under study, that polymorphisms may have several phenotypic effects associated with disease, the lack of suitable polymorphisms for studying modifiable exposures of interest, and canalization-the buffering of the effects of genetic variation during development. Nevertheless, Mendelian randomization provides new opportunities to test causality and demonstrates how investment in the human genome project may contribute to understanding and preventing the adverse effects on human health of modifiable exposures.
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            Using published data in Mendelian randomization: a blueprint for efficient identification of causal risk factors

            Finding individual-level data for adequately-powered Mendelian randomization analyses may be problematic. As publicly-available summarized data on genetic associations with disease outcomes from large consortia are becoming more abundant, use of published data is an attractive analysis strategy for obtaining precise estimates of the causal effects of risk factors on outcomes. We detail the necessary steps for conducting Mendelian randomization investigations using published data, and present novel statistical methods for combining data on the associations of multiple (correlated or uncorrelated) genetic variants with the risk factor and outcome into a single causal effect estimate. A two-sample analysis strategy may be employed, in which evidence on the gene-risk factor and gene-outcome associations are taken from different data sources. These approaches allow the efficient identification of risk factors that are suitable targets for clinical intervention from published data, although the ability to assess the assumptions necessary for causal inference is diminished. Methods and guidance are illustrated using the example of the causal effect of serum calcium levels on fasting glucose concentrations. The estimated causal effect of a 1 standard deviation (0.13 mmol/L) increase in calcium levels on fasting glucose (mM) using a single lead variant from the CASR gene region is 0.044 (95 % credible interval −0.002, 0.100). In contrast, using our method to account for the correlation between variants, the corresponding estimate using 17 genetic variants is 0.022 (95 % credible interval 0.009, 0.035), a more clearly positive causal effect. Electronic supplementary material The online version of this article (doi:10.1007/s10654-015-0011-z) contains supplementary material, which is available to authorized users.
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              Comprehensive characterization, annotation and innovative use of Infinium DNA methylation BeadChip probes

              Abstract Illumina Infinium DNA Methylation BeadChips represent the most widely used genome-scale DNA methylation assays. Existing strategies for masking Infinium probes overlapping repeats or single nucleotide polymorphisms (SNPs) are based largely on ad hoc assumptions and subjective criteria. In addition, the recently introduced MethylationEPIC (EPIC) array expands on the utility of this platform, but has not yet been well characterized. We present in this paper an extensive characterization of probes on the EPIC and HM450 microarrays, including mappability to the latest genome build, genomic copy number of the 3΄ nested subsequence and influence of polymorphisms including a previously unrecognized color channel switch for Type I probes. We show empirical evidence for exclusion criteria for underperforming probes, providing a sounder basis than current ad hoc criteria for exclusion. In addition, we describe novel probe uses, exemplified by the addition of a total of 1052 SNP probes to the existing 59 explicit SNP probes on the EPIC array and the use of these probes to predict ethnicity. Finally, we present an innovative out-of-band color channel application for the dual use of 62 371 probes as internal bisulfite conversion controls.
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                Author and article information

                Journal
                Hum Mol Genet
                Hum. Mol. Genet
                hmg
                Human Molecular Genetics
                Oxford University Press
                0964-6906
                1460-2083
                15 September 2018
                08 June 2018
                08 June 2018
                : 27
                : 18
                : 3293-3304
                Affiliations
                MRC Integrative Epidemiology Unit (IEU), Bristol Medical School (Population Health Sciences), University of Bristol, Oakfield House, Oakfield Grove, Bristol, UK
                Author notes
                To whom correspondence should be addressed at: MRC Integrative Epidemiology Unit, Bristol Medical School (Population Health Sciences), University of Bristol, Oakfield House, Oakfield Grove, Bristol BS8 2BN, UK. Tel: +44 1173313370; Fax: +44 1173310123; Email: tom.g.richardson@ 123456bristol.ac.uk
                Article
                ddy210
                10.1093/hmg/ddy210
                6121186
                29893838
                028d67f7-ddb1-4bf4-99c7-8c044ac5ae01
                © The Author(s) 2018. Published by Oxford University Press.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 09 December 2017
                : 10 April 2018
                : 29 April 2018
                Page count
                Pages: 12
                Funding
                Funded by: UK Medical Research Council
                Funded by: Wellcome Trust 10.13039/100004440
                Award ID: 102215/2/13/2
                Funded by: University of Bristol 10.13039/501100000883
                Funded by: Wellcome Trust 10.13039/100004440
                Funded by: UK BBSRC
                Award ID: BB/I025751/1
                Award ID: BB/I025263/1
                Funded by: Accessible Resource for Integrated Epigenomic Studies
                Funded by: UK Biobank data
                Award ID: 8786
                Award ID: 15825
                Funded by: UK Medical Research Council
                Award ID: MC UU 12013/1
                Award ID: MC UU 12013/2
                Award ID: MC UU 12013/3
                Award ID: MC UU 12013/8
                Funded by: Wellcome Trust 10.13039/100004440
                Award ID: 208806/Z/17/Z
                Funded by: Wellcome Trust Investigator
                Award ID: 202802/Z/16/Z
                Funded by: MRC Integrative Epidemiology Unit
                Award ID: MC_UU_12013/3
                Funded by: UKRI Innovation Research Fellow
                Award ID: MR/S003886/1
                Funded by: Elizabeth Blackwell Institute Proximity to Discovery
                Award ID: EBI 424
                Funded by: Medical Research Council 10.13039/501100000265
                Categories
                Original Article

                Genetics
                Genetics

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