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nutrients Article Epigenome-Wide Study Identified Methylation Sites Associated with the Risk of Obesity Majid Nikpay 1,*, Sepehr Ravati 2, Robert Dent 3 and Ruth McPherson 1,4,* 1 Ruddy Canadian Cardiovascular Genetics Centre, University of Ottawa Heart Institute, 40 Ruskin St–H4208, Ottawa, ON K1Y 4W7, Canada 2 Plastenor Technologies Company, Montreal, QC H2P 2G4, Canada; [email protected] 3 Department of Medicine, Division of Endocrinology, University of Ottawa, the Ottawa Hospital, Ottawa, ON K1Y 4E9, Canada; [email protected] 4 Atherogenomics Laboratory, University of Ottawa Heart Institute, Ottawa, ON K1Y 4W7, Canada * Correspondence: [email protected] (M.N.); [email protected] (R.M.) Abstract: Here, we performed a genome-wide search for methylation sites that contribute to the risk of obesity. We integrated methylation quantitative trait locus (mQTL) data with BMI GWAS information through a SNP-based multiomics approach to identify genomic regions where mQTLs for a methylation site co-localize with obesity risk SNPs. We then tested whether the identified site contributed to BMI through Mendelian randomization. We identified multiple methylation sites causally contributing to the risk of obesity. We validated these findings through a replication stage. By integrating expression quantitative trait locus (eQTL) data, we noted that lower methylation at cg21178254 site upstream of CCNL1 contributes to obesity by increasing the expression of this gene. Higher methylation at cg02814054 increases the risk of obesity by lowering the expression of MAST3, whereas lower methylation at cg06028605 contributes to obesity by decreasing the expression of SLC5A11. Finally, we noted that rare variants within 2p23.3 impact obesity by making the cg01884057 Citation: Nikpay, M.; Ravati, S.; Dent, R.; McPherson, R. site more susceptible to methylation, which consequently lowers the expression of POMC, ADCY3 Epigenome-Wide Study Identified and DNAJC27. In this study, we identify methylation sites associated with the risk of obesity and Methylation Sites Associated with the reveal the mechanism whereby a number of these sites exert their effects. This study provides a Risk of Obesity. Nutrients 2021, 13, framework to perform an omics-wide association study for a phenotype and to understand the 1984. https://doi.org/10.3390/ mechanism whereby a rare variant causes a disease. nu13061984 Keywords: obesity; EWAS; epigenetics; multiomics; Mendelian randomization Academic Editor: M. Luisa Bonet Received: 5 May 2021 Accepted: 7 June 2021 1. Introduction Published: 9 June 2021 Obesity is a complex phenotype and the outcome of numerous genes and environmen- tal factors. Epigenetic sites are considered as the sites of gene–environment interactions. Publisher’s Note: MDPI stays neutral Epigenetics provides an elegant solution to modify gene expression activity in response to with regard to jurisdictional claims in published maps and institutional affil- external stimuli without altering the DNA code. As such, it has an important role in the iations. regulation and manifestation of complex phenotypes. The relation between epigenetics and obesity has been the subject of numerous studies over the past few years [1,2]. Methodological improvements, and the global increase in obesity, have contributed to this interest. The general consensus from these studies is that interindividual variation in epigenetic modifications correlates with body weight. Copyright: © 2021 by the authors. Furthermore, findings from these studies support not only a role for epigenetics in gaining Licensee MDPI, Basel, Switzerland. weight, but also epigenetic alterations as a response to obesity [1–6]. This article is an open access article distributed under the terms and As reviewed by Ling et al. [1], the majority of previous studies had small sample conditions of the Creative Commons sizes, which lowers the power of statistical tests. Furthermore, they typically measured Attribution (CC BY) license (https:// both epigenetic levels and BMI in the same group of individuals and then investigated creativecommons.org/licenses/by/ the epigenetic sites that showed differential levels of modifications in individuals with a 4.0/). higher BMI than those with a lower BMI; however, such a design cannot tell us whether a Nutrients 2021, 13, 1984. https://doi.org/10.3390/nu13061984 https://www.mdpi.com/journal/nutrients Nutrients 2021, 13, 1984 2 of 10 significant association indicates causation (e.g., Methylation at a site ! Obesity), correlation (Methylation confounders ! Obesity) or reverse causation (Obesity ! Methylation). Nutrients 2021, 13, x FOR PEER REVIEW 2 of 10 To overcome these issues, in this study, we used Mendelian randomization (MR) that can control for both confounding and reverse causation. MR is a form of instrumental vari- able analysis that investigates the relationship between the exposure (DNA methylation) andhigher the outcome BMI than (BMI) those using with a an lower instrument BMI; however, (a set of such independent a design cannot SNPs) thattell us is knownwhether a tosignificant cause change association in the exposure. indicates Allelescausation of independent(e.g., Methylation SNPs at are a site randomly→Obesity), allocated correla- totion offspring (Methylation at conception←confound (Mendel’sers→Obesity) second law);or reverse therefore, causation an instrument (Obesity→Methylation) of SNPs is . inherentlyTo overcome immune to these the confoundingissues, in this effect study, of environmentalwe used Mendelian factors randomization that can bias an(MR) associationthat can study.control Furthermore, for both confounding by excluding and SNPs reverse with pleiotropiccausation. MR effect is (Methylation a form of instr u- SNPmental! Obesity) variable from analysis the instrument, that investigates it is possible the relationship to rule out the between correlation the exposure scenario, and(DNA bymethylation) swapping the and places the of outcome exposure (BMI) and outcome using an and instrument repeating the(a set test, of we independent can investigate SNPs) thethat possibility is known of reverseto cause causation. change in the exposure. Alleles of independent SNPs are ran- domlyTo increase allocated the to statistical offspring power at conception of our analyses, (Mendel’s in this second study, law); we used therefore a two-sample, an instru- MRment design of SNPs (Figure is 1inherently) that incorporated immune to data the from confounding separate studieseffect of to environmental estimate a causal factors effectthat of can the bias exposure an association on the outcome. study. Furthermore, Hence, with by this excluding design,we SNPs can with achieve pleiotropic a better ef- statisticalfect (Methylation power by← includingSNP→Obesity) data from from the the GWAS instrument, consortia it [is7]. possible to rule out the cor- Examining the association between change in DNA methylation at every site in the relation scenario, and by swapping the places of exposure and outcome and repeating the genome and obesity using MR is cumbersome. Therefore, in this study, we used a SNP- test, we can investigate the possibility of reverse causation. based multiomics pipeline that also included MR, in order to efficiently narrow down our To increase the statistical power of our analyses, in this study, we used a two-sample search and identify methylation sites that contribute to the risk of obesity. We validated MR design (Figure 1) that incorporated data from separate studies to estimate a causal our findings through the replication stage; we also investigated the mechanism whereby effect of the exposure on the outcome. Hence, with this design, we can achieve a better these sites contribute to obesity by integrating eQTLs data into our analysis. statistical power by including data from the GWAS consortia [7]. FigureFigure 1. Concept1. Concept of two-sampleof two-sample Mendelian Mendelian randomization randomization (MR) (MR) design. design.Under Under this this design, design, we we firstfirst constructed constructed an instrumentan instrument based based on a on set a of set independent of independent SNPs SNPs that were that significantlywere significantly associated associ- ated with the exposure in the first sample. Next, we contrasted effect sizes (betas) of SNPs on the with the exposure in the first sample. Next, we contrasted effect sizes (betas) of SNPs on the exposure exposure with their corresponding effect sizes on the outcome (obtained from the second sample) with their corresponding effect sizes on the outcome (obtained from the second sample) to find out if to find out if there was a significant association. This design is immune to confounding environ- theremental was afactors significant because association. it uses SNPs This designas instrument. is immune Furthermore, to confounding by removing environmental SNPs with factors peli- becauseotropic it useseffect SNPs genetic as instrument. confounding Furthermore, is prevented. by removingFurther information SNPs with is peliotropic provided effect, in the genetic Methods confoundingsection.Examining is prevented. the association Further information between chan is providedge in DNA in themethylation Methods section.at every site in the genome and obesity using MR is cumbersome. Therefore, in this study, we used a SNP-based multiomics 2. Methodspipeline that also included MR, in order to efficiently narrow down our search and identify meth- ylationOur SNP-basedsites that contribute multiomics to the
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