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Leveraging DNA methylation quantitative trait loci to characterize the relationship between methylomic variation, gene expression and complex traits

View ORCID ProfileEilis Hannon, View ORCID ProfileTyler J Gorrie-Stone, View ORCID ProfileMelissa C Smart, Joe Burrage, Amanda Hughes, View ORCID ProfileYanchun Bao, Meena Kumari, View ORCID ProfileLeonard C Schalkwyk, View ORCID ProfileJonathan Mill
doi: https://doi.org/10.1101/297176
Eilis Hannon
1University of Exeter Medical School, University of Exeter, Exeter EX2 5DW, United Kingdom
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Tyler J Gorrie-Stone
2School of Biological Sciences, University of Essex, Colchester, CO4 3SQ, United Kingdom
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Melissa C Smart
3Institute for Social and Economic Research, University of Essex, Colchester CO3 3LG, United Kingdom
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Joe Burrage
1University of Exeter Medical School, University of Exeter, Exeter EX2 5DW, United Kingdom
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Amanda Hughes
3Institute for Social and Economic Research, University of Essex, Colchester CO3 3LG, United Kingdom
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Yanchun Bao
3Institute for Social and Economic Research, University of Essex, Colchester CO3 3LG, United Kingdom
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Meena Kumari
3Institute for Social and Economic Research, University of Essex, Colchester CO3 3LG, United Kingdom
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Leonard C Schalkwyk
2School of Biological Sciences, University of Essex, Colchester, CO4 3SQ, United Kingdom
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Jonathan Mill
1University of Exeter Medical School, University of Exeter, Exeter EX2 5DW, United Kingdom
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  • For correspondence: j.mill@exeter.ac.uk
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ABSTRACT

Characterizing the complex relationship between genetic, epigenetic and transcriptomic variation has the potential to increase understanding about the mechanisms underpinning health and disease phenotypes. In this study, we describe the most comprehensive analysis of common genetic variation on DNA methylation (DNAm) to date, using the Illumina EPIC array to profile samples from the UK Household Longitudinal study. We identified 12,689,548 significant DNA methylation quantitative trait loci (mQTL) associations (P < 6.52x10-14) occurring between 2,907,234 genetic variants and 93,268 DNAm sites, including a large number not identified using previous DNAm-profiling methods. We demonstrate the utility of these data for interpreting the functional consequences of common genetic variation associated with > 60 human traits, using Summary data–based Mendelian Randomization (SMR) to identify 1,662 pleiotropic associations between 36 complex traits and 1,246 DNAm sites. We also use SMR to characterize the relationship between DNAm and gene expression, identifying 6,798 pleiotropic associations between 5,420 DNAm sites and the transcription of 1,702 genes. Our mQTL database and SMR results are available via a searchable online database (http://www.epigenomicslab.com/online-data-resources/) as a resource to the research community.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC 4.0 International license.
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Posted April 07, 2018.
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Leveraging DNA methylation quantitative trait loci to characterize the relationship between methylomic variation, gene expression and complex traits
Eilis Hannon, Tyler J Gorrie-Stone, Melissa C Smart, Joe Burrage, Amanda Hughes, Yanchun Bao, Meena Kumari, Leonard C Schalkwyk, Jonathan Mill
bioRxiv 297176; doi: https://doi.org/10.1101/297176
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Leveraging DNA methylation quantitative trait loci to characterize the relationship between methylomic variation, gene expression and complex traits
Eilis Hannon, Tyler J Gorrie-Stone, Melissa C Smart, Joe Burrage, Amanda Hughes, Yanchun Bao, Meena Kumari, Leonard C Schalkwyk, Jonathan Mill
bioRxiv 297176; doi: https://doi.org/10.1101/297176

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