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1. Detecting cord blood cell type-specific epigenetic associations with gestational diabetes mellitus and early childhood growth.

Author

Lu T, Cardenas A, Perron P, Hivert MF, Bouchard L, and Greenwood CMT

Subjects
Adult, Biomarkers blood, Birth Cohort, Body Height, Body Mass Index, Body Weight, Canada, Child, Preschool, Epigenome genetics, Female, Humans, Infant, Newborn, Male, Pregnancy, Prospective Studies, Child Development, Diabetes, Gestational genetics, Epigenesis, Genetic genetics, Epigenomics methods, Fetal Blood chemistry, Genome-Wide Association Study methods
Abstract

Background: Epigenome-wide association studies (EWAS) have provided opportunities to understand the role of epigenetic mechanisms in development and pathophysiology of many chronic diseases. However, an important limitation of conventional EWAS is that profiles of epigenetic variability are often obtained in samples of mixed cell types. Here, we aim to assess whether changes in cord blood DNA methylation (DNAm) associated with gestational diabetes mellitus (GDM) exposure and early childhood growth markers occur in a cell type-specific manner., Results: We analyzed 275 cord blood samples collected at delivery from a prospective pre-birth cohort with genome-wide DNAm profiled by the Illumina MethylationEPIC array. We estimated proportions of seven common cell types in each sample using a cord blood-specific DNAm reference panel. Leveraging a recently developed approach named CellDMC, we performed cell type-specific EWAS to identify CpG loci significantly associated with GDM, or 3-year-old body mass index (BMI) z-score. A total of 1410 CpG loci displayed significant cell type-specific differences in methylation level between 23 GDM cases and 252 controls with a false discovery rate < 0.05. Gene Ontology enrichment analysis indicated that LDL transportation emerged from CpG specifically identified from B-cells DNAm analyses and the mitogen-activated protein kinase pathway emerged from CpG specifically identified from natural killer cells DNAm analyses. In addition, we identified four and six loci associated with 3-year-old BMI z-score that were specific to CD8+ T-cells and monocytes, respectively. By performing genome-wide permutation tests, we validated that most of our detected signals had low false positive rates., Conclusion: Compared to conventional EWAS adjusting for the effects of cell type heterogeneity, the proposed approach based on cell type-specific EWAS could provide additional biologically meaningful associations between CpG methylation, prenatal maternal GDM or 3-year-old BMI. With careful validation, these findings may provide new insights into the pathogenesis, programming, and consequences of related childhood metabolic dysregulation. Therefore, we propose that cell type-specific analyses are worth cautious explorations.

Published
2021
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2. Detecting differentially methylated regions with multiple distinct associations.

Author

Lent S, Cardenas A, Rifas-Shiman SL, Perron P, Bouchard L, Liu CT, Hivert MF, and Dupuis J

Subjects
Adolescent, Female, Gene Expression Profiling, Humans, Infant, Male, Prospective Studies, Computer Simulation, CpG Islands, DNA Methylation, Epigenesis, Genetic, Fetal Blood metabolism, Gene Expression Regulation, Models, Genetic
Abstract

Aim: We evaluated five methods for detecting differentially methylated regions (DMRs): DMRcate, comb-p, seqlm, GlobalP and dmrff. Materials & methods: We used a simulation study and real data analysis to evaluate performance. Additionally, we evaluated the use of an ancestry-matched reference cohort to estimate correlations between CpG sites in cord blood. Results: Several methods had inflated Type I error, which increased at more stringent significant levels. In power simulations with 1-2 causal CpG sites with the same direction of effect, dmrff was consistently among the most powerful methods. Conclusion: This study illustrates the need for more thorough simulation studies when evaluating novel methods. More work must be done to develop methods with well-controlled Type I error that do not require individual-level data.

Published
2021
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3. DNA methylation at LRP1 gene locus mediates the association between maternal total cholesterol changes in pregnancy and cord blood leptin levels.

Author

Guay SP, Houde AA, Breton E, Baillargeon JP, Perron P, Gaudet D, Hivert MF, Brisson D, and Bouchard L

Subjects
Adult, Female, Gene Expression Regulation, Humans, Infant, Newborn, Male, Pregnancy, Pregnancy Trimesters, Prognosis, Receptors, LDL genetics, Scavenger Receptors, Class B genetics, Cholesterol metabolism, DNA Methylation, Epigenesis, Genetic, Fetal Blood metabolism, Leptin metabolism, Low Density Lipoprotein Receptor-Related Protein-1 genetics, Placenta metabolism
Abstract

Placental lipids transfer is essential for optimal fetal development, and alterations of these mechanisms could lead to a higher risk of adverse birth outcomes. Low-density lipoprotein receptor (LDLR), LDL receptor-related protein 1 (LRP1), and scavenger receptor class B type 1 (SCARB1) genes are encoding lipoprotein receptors expressed in the placenta where they participate in cholesterol exchange from maternal to fetal circulation. The aim of this study was thus to investigate the association between maternal lipid changes occurring in pregnancy, placental DNA methylation (DNAm) variations at LDLR, LRP1, and SCARB1 gene loci, and newborn's anthropometric profile at birth. Sixty-nine normoglycemic women were followed from the first trimester of pregnancy until delivery. Placental DNAm was quantified at 43 Cytosine-phosphate-Guanines (CpGs) at LDLR, LRP1, and SCARB1 gene loci using pyrosequencing: 4 CpGs were retained for further analysis. Maternal clinical data were collected at each trimester of pregnancy. Newborns' data were collected from medical records. Statistical models included minimally newborn sex and gestational and maternal age. Maternal total cholesterol changes during pregnancy (ΔT3-T1) were correlated with DNAm variations at LDLR (r = -0.32, p = 0.01) and LRP1 (r = 0.34, p = 0.007). DNAm at these loci was also correlated with newborns' cord blood triglyceride and leptin levels. Mediation analysis supports a causal relationship between maternal cholesterol changes, DNAm levels at LRP1 locus, and cord blood leptin concentration (pmediation = 0.02). These results suggest that LRP1 DNAm link maternal blood cholesterol changes in pregnancy and offspring adiposity at birth, which provide support for a better follow-up of blood lipids in pregnancy.

Published
2020
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4. Meta-analysis of epigenome-wide association studies in neonates reveals widespread differential DNA methylation associated with birthweight.

Author

Küpers LK, Monnereau C, Sharp GC, Yousefi P, Salas LA, Ghantous A, Page CM, Reese SE, Wilcox AJ, Czamara D, Starling AP, Novoloaca A, Lent S, Roy R, Hoyo C, Breton CV, Allard C, Just AC, Bakulski KM, Holloway JW, Everson TM, Xu CJ, Huang RC, van der Plaat DA, Wielscher M, Merid SK, Ullemar V, Rezwan FI, Lahti J, van Dongen J, Langie SAS, Richardson TG, Magnus MC, Nohr EA, Xu Z, Duijts L, Zhao S, Zhang W, Plusquin M, DeMeo DL, Solomon O, Heimovaara JH, Jima DD, Gao L, Bustamante M, Perron P, Wright RO, Hertz-Picciotto I, Zhang H, Karagas MR, Gehring U, Marsit CJ, Beilin LJ, Vonk JM, Jarvelin MR, Bergström A, Örtqvist AK, Ewart S, Villa PM, Moore SE, Willemsen G, Standaert ARL, Håberg SE, Sørensen TIA, Taylor JA, Räikkönen K, Yang IV, Kechris K, Nawrot TS, Silver MJ, Gong YY, Richiardi L, Kogevinas M, Litonjua AA, Eskenazi B, Huen K, Mbarek H, Maguire RL, Dwyer T, Vrijheid M, Bouchard L, Baccarelli AA, Croen LA, Karmaus W, Anderson D, de Vries M, Sebert S, Kere J, Karlsson R, Arshad SH, Hämäläinen E, Routledge MN, Boomsma DI, Feinberg AP, Newschaffer CJ, Govarts E, Moisse M, Fallin MD, Melén E, Prentice AM, Kajantie E, Almqvist C, Oken E, Dabelea D, Boezen HM, Melton PE, Wright RJ, Koppelman GH, Trevisi L, Hivert MF, Sunyer J, Munthe-Kaas MC, Murphy SK, Corpeleijn E, Wiemels J, Holland N, Herceg Z, Binder EB, Davey Smith G, Jaddoe VWV, Lie RT, Nystad W, London SJ, Lawlor DA, Relton CL, Snieder H, and Felix JF

Subjects
Adolescent, Adult, Body Mass Index, Child, CpG Islands, DNA genetics, DNA Methylation, Female, Fetal Development genetics, Fetus, Folic Acid blood, Genome-Wide Association Study, Humans, Infant, Newborn, Male, Pregnancy, Prenatal Exposure Delayed Effects blood, Prenatal Exposure Delayed Effects etiology, Prenatal Exposure Delayed Effects genetics, Prenatal Exposure Delayed Effects physiopathology, Smoking adverse effects, Smoking blood, Smoking genetics, Birth Weight genetics, DNA metabolism, Epigenesis, Genetic, Genome, Human
Abstract

Birthweight is associated with health outcomes across the life course, DNA methylation may be an underlying mechanism. In this meta-analysis of epigenome-wide association studies of 8,825 neonates from 24 birth cohorts in the Pregnancy And Childhood Epigenetics Consortium, we find that DNA methylation in neonatal blood is associated with birthweight at 914 sites, with a difference in birthweight ranging from -183 to 178 grams per 10% increase in methylation (P Bonferroni  < 1.06 x 10 -7 ). In additional analyses in 7,278 participants, <1.3% of birthweight-associated differential methylation is also observed in childhood and adolescence, but not adulthood. Birthweight-related CpGs overlap with some Bonferroni-significant CpGs that were previously reported to be related to maternal smoking (55/914, p = 6.12 x 10 -74 ) and BMI in pregnancy (3/914, p = 1.13x10 -3 ), but not with those related to folate levels in pregnancy. Whether the associations that we observe are causal or explained by confounding or fetal growth influencing DNA methylation (i.e. reverse causality) requires further research.

Published
2019
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5. Placental DNA Methylation Adaptation to Maternal Glycemic Response in Pregnancy.

Author

Cardenas A, Gagné-Ouellet V, Allard C, Brisson D, Perron P, Bouchard L, and Hivert MF

Subjects
Adult, Biomarkers blood, Biomarkers metabolism, Birth Weight, Cohort Studies, CpG Islands, Cyclic Nucleotide Phosphodiesterases, Type 4 genetics, Female, Gene Expression Regulation, Developmental, Glucose Tolerance Test, Glycated Hemoglobin analysis, Humans, Infant, Newborn, Placenta enzymology, Placentation, Pregnancy, Prospective Studies, RecQ Helicases genetics, RecQ Helicases metabolism, Receptors, LDL genetics, Receptors, Tumor Necrosis Factor, Type II genetics, Term Birth, Young Adult, Cyclic Nucleotide Phosphodiesterases, Type 4 metabolism, DNA Methylation, Epigenesis, Genetic, Insulin Resistance, Placenta metabolism, Receptors, LDL metabolism, Receptors, Tumor Necrosis Factor, Type II metabolism
Abstract

Maternal hyperglycemia during pregnancy is associated with excess fetal growth and adverse perinatal and developmental outcomes. Placental epigenetic maladaptation may underlie these associations. We performed an epigenome-wide association study (>850,000 CpG sites) of term placentas and prenatal maternal glycemic response 2-h post oral glucose challenge at 24-30 weeks of gestation among 448 mother-infant pairs. Maternal 2-h glycemia postload was strongly associated with lower DNA methylation of four CpG sites (false discovery rate [FDR] q <0.05) within the phosphodiesterase 4B gene ( PDE4B ). Additionally, three other individual CpG sites were differentially methylated relative to maternal glucose response within the TNFRSF1B , LDLR , and BLM genes (FDR q <0.05). DNA methylation correlated with expression of its respective genes in placental tissue at three out of four independent identified loci: PDE4B ( r = 0.31, P < 0.01), TNFRSF1B ( r = -0.24, P = 0.013), and LDLR ( r = 0.32, P < 0.001). In an independent replication cohort ( N = 65-108 samples), results were consistent in direction but not significantly replicated among tested CpG sites in PDE4B and TNFRSF1B Our study provides evidence that maternal glycemic response during pregnancy is associated with placental DNA methylation of key inflammatory genes whose expression levels are partially under epigenetic control., (© 2018 by the American Diabetes Association.)

Published
2018
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6. Cohort Profile: Pregnancy And Childhood Epigenetics (PACE) Consortium.

Author

Felix JF, Joubert BR, Baccarelli AA, Sharp GC, Almqvist C, Annesi-Maesano I, Arshad H, Baïz N, Bakermans-Kranenburg MJ, Bakulski KM, Binder EB, Bouchard L, Breton CV, Brunekreef B, Brunst KJ, Burchard EG, Bustamante M, Chatzi L, Cheng Munthe-Kaas M, Corpeleijn E, Czamara D, Dabelea D, Davey Smith G, De Boever P, Duijts L, Dwyer T, Eng C, Eskenazi B, Everson TM, Falahi F, Fallin MD, Farchi S, Fernandez MF, Gao L, Gaunt TR, Ghantous A, Gillman MW, Gonseth S, Grote V, Gruzieva O, Håberg SE, Herceg Z, Hivert MF, Holland N, Holloway JW, Hoyo C, Hu D, Huang RC, Huen K, Järvelin MR, Jima DD, Just AC, Karagas MR, Karlsson R, Karmaus W, Kechris KJ, Kere J, Kogevinas M, Koletzko B, Koppelman GH, Küpers LK, Ladd-Acosta C, Lahti J, Lambrechts N, Langie SAS, Lie RT, Liu AH, Magnus MC, Magnus P, Maguire RL, Marsit CJ, McArdle W, Melén E, Melton P, Murphy SK, Nawrot TS, Nisticò L, Nohr EA, Nordlund B, Nystad W, Oh SS, Oken E, Page CM, Perron P, Pershagen G, Pizzi C, Plusquin M, Raikkonen K, Reese SE, Reischl E, Richiardi L, Ring S, Roy RP, Rzehak P, Schoeters G, Schwartz DA, Sebert S, Snieder H, Sørensen TIA, Starling AP, Sunyer J, Taylor JA, Tiemeier H, Ullemar V, Vafeiadi M, Van Ijzendoorn MH, Vonk JM, Vriens A, Vrijheid M, Wang P, Wiemels JL, Wilcox AJ, Wright RJ, Xu CJ, Xu Z, Yang IV, Yousefi P, Zhang H, Zhang W, Zhao S, Agha G, Relton CL, Jaddoe VWV, and London SJ

Subjects
Child Health, Cohort Studies, Environmental Pollution analysis, Environmental Pollution statistics & numerical data, Female, Folic Acid blood, Humans, Infant, Newborn, Maternal Exposure statistics & numerical data, Maternal Health, Pregnancy, Prenatal Exposure Delayed Effects epidemiology, DNA Methylation physiology, Epigenesis, Genetic, Maternal Exposure adverse effects
Published
2018
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7. Genetics of Glucose regulation in Gestation and Growth (Gen3G): a prospective prebirth cohort of mother-child pairs in Sherbrooke, Canada.

Author

Guillemette L, Allard C, Lacroix M, Patenaude J, Battista MC, Doyon M, Moreau J, Ménard J, Bouchard L, Ardilouze JL, Perron P, and Hivert MF

Subjects
Adiponectin metabolism, Adult, Body Composition, Body Mass Index, Canada epidemiology, Diabetes, Gestational metabolism, Female, Fetal Blood metabolism, Glucose Tolerance Test, Humans, Infant, Newborn, Insulin Resistance, Male, Obesity metabolism, Placenta metabolism, Pregnancy, Pregnancy Complications epidemiology, Pregnancy Complications metabolism, Prospective Studies, Risk Factors, Tumor Necrosis Factor-alpha metabolism, Vitamin D analogs & derivatives, Vitamin D metabolism, Vitamin D Deficiency metabolism, Young Adult, Blood Glucose metabolism, Diabetes, Gestational epidemiology, Epigenesis, Genetic genetics, Obesity epidemiology, Vitamin D Deficiency epidemiology
Abstract

Purpose: We initiated the Genetics of Glucose regulation in Gestation and Growth (Gen3G) prospective cohort to increase our understanding of biological, environmental and genetic determinants of glucose regulation during pregnancy and their impact on fetal development., Participants: Between January 2010 and June 2013, we invited pregnant women aged ≥ 18 years old who visited the blood sampling in pregnancy clinic in Sherbrooke for their first trimester clinical blood samples: 1034 women accepted to participate in our cohort study., Findings to Date: At first and second trimester, we collected demographics and lifestyle questionnaires, anthropometry measures (including fat and lean mass estimated using bioimpedance), blood pressure, and blood samples. At second trimester, women completed a full 75 g oral glucose tolerance test and we collected additional blood samples. At delivery, we collected cord blood and placenta samples; obstetrical and neonatal clinical data were abstracted from electronic medical records. We also collected buffy coats and extracted DNA from maternal and/or offspring samples (placenta and blood cells) to pursue genetic and epigenetic hypotheses. So far, we have found that low adiponectin and low vitamin D maternal levels in first trimester predict higher risk of developing gestational diabetes., Future Plans: We are now in the phase of prospective follow-up of mothers and offspring 3 and 5 years postdelivery to investigate the consequences of maternal dysglycaemia during pregnancy on offspring adiposity and metabolic profile., Trial Registration Number: NCT01623934., (Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/)

Published
2016
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8. LRP1B, BRD2 and CACNA1D: new candidate genes in fetal metabolic programming of newborns exposed to maternal hyperglycemia.

Author

Houde AA, Ruchat SM, Allard C, Baillargeon JP, St-Pierre J, Perron P, Gaudet D, Brisson D, Hivert MF, and Bouchard L

Subjects
Adult, Birth Weight, Blood Glucose metabolism, Calcium Channels, L-Type metabolism, Cohort Studies, DNA Methylation, Diabetes, Gestational blood, Diabetes, Gestational physiopathology, Energy Metabolism genetics, Female, Fetal Blood metabolism, Fetal Development genetics, Fetus, Humans, Hyperglycemia blood, Hyperglycemia physiopathology, Infant, Newborn, Male, Placenta metabolism, Pregnancy, Protein Serine-Threonine Kinases metabolism, Receptors, LDL metabolism, Transcription Factors, Calcium Channels, L-Type genetics, Diabetes, Gestational genetics, Epigenesis, Genetic, Hyperglycemia genetics, Protein Serine-Threonine Kinases genetics, Receptors, LDL genetics
Abstract

Aim: To assess the associations between gestational diabetes mellitus (GDM) and DNA methylation levels at genes related to energy metabolism., Patients & Methods: Ten loci were selected from our recent epigenome-wide association study on GDM. DNA methylation levels were quantified by bisulfite pyrosequencing in 80 placenta and cord blood samples (20 exposed to GDM) from an independent birth cohort (Gen3G)., Results: We did not replicate association between DNA methylation and GDM. However, in normoglycemic women, glucose levels were associated with DNA methylation changes at LRP1B and BRD2 and at CACNA1D and LRP1B gene loci in placenta and cord blood, respectively., Conclusion: These results suggest that maternal glucose levels, within the normal range, are associated with DNA methylation changes at genes related to energy metabolism and previously associated with GDM. Maternal glycemia might thus be involved in fetal metabolic programming.

Published
2015
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9. Prenatal exposure to maternal cigarette smoking and DNA methylation: epigenome-wide association in a discovery sample of adolescents and replication in an independent cohort at birth through 17 years of age.

Author

Lee KW, Richmond R, Hu P, French L, Shin J, Bourdon C, Reischl E, Waldenberger M, Zeilinger S, Gaunt T, McArdle W, Ring S, Woodward G, Bouchard L, Gaudet D, Smith GD, Relton C, Paus T, and Pausova Z

Subjects
Adolescent, Adult, Child, Cohort Studies, England epidemiology, Female, Genome-Wide Association Study, Humans, Infant, Newborn, Male, Pregnancy, Prenatal Exposure Delayed Effects, Quebec epidemiology, Smoking adverse effects, DNA Methylation, Epigenesis, Genetic, Maternal Exposure, Smoking epidemiology
Abstract

Background: Prenatal exposure to maternal cigarette smoking (prenatal smoke exposure) had been associated with altered DNA methylation (DNAm) at birth., Objective: We examined whether such alterations are present from birth through adolescence., Methods: We used the Infinium HumanMethylation450K BeadChip to search across 473,395 CpGs for differential DNAm associated with prenatal smoke exposure during adolescence in a discovery cohort (n = 132) and at birth, during childhood, and during adolescence in a replication cohort (n = 447)., Results: In the discovery cohort, we found five CpGs in MYO1G (top-ranking CpG: cg12803068, p = 3.3 × 10-11) and CNTNAP2 (cg25949550, p = 4.0 × 10-9) to be differentially methylated between exposed and nonexposed individuals during adolescence. The CpGs in MYO1G and CNTNAP2 were associated, respectively, with higher and lower DNAm in exposed versus nonexposed adolescents. The same CpGs were differentially methylated at birth, during childhood, and during adolescence in the replication cohort. In both cohorts and at all developmental time points, the differential DNAm was in the same direction and of a similar magnitude, and was not altered appreciably by adjustment for current smoking by the participants or their parents. In addition, four of the five EWAS (epigenome-wide association study)-significant CpGs in the adolescent discovery cohort were also among the top sites of differential methylation in a previous birth cohort, and differential methylation of CpGs in CYP1A1, AHRR, and GFI1 observed in that study was also evident in our discovery cohort., Conclusions: Our findings suggest that modifications of DNAm associated with prenatal maternal smoking may persist in exposed offspring for many years-at least until adolescence.

Published
2015
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10. Mendelian randomization supports causality between maternal hyperglycemia and epigenetic regulation of leptin gene in newborns.

Author

Allard C, Desgagné V, Patenaude J, Lacroix M, Guillemette L, Battista MC, Doyon M, Ménard J, Ardilouze JL, Perron P, Bouchard L, and Hivert MF

Subjects
Adult, Cohort Studies, DNA Methylation, Female, Glucose metabolism, Humans, Infant, Newborn, Male, Mendelian Randomization Analysis methods, Pregnancy, Epigenesis, Genetic, Gene Expression Regulation, Hyperglycemia genetics, Leptin genetics, Maternal-Fetal Exchange
Abstract

Leptin is an adipokine that acts in the central nervous system and regulates energy balance. Animal models and human observational studies have suggested that leptin surge in the perinatal period has a critical role in programming long-term risk of obesity. In utero exposure to maternal hyperglycemia has been associated with increased risk of obesity later in life. Epigenetic mechanisms are suspected to be involved in fetal programming of long term metabolic diseases. We investigated whether DNA methylation levels near LEP locus mediate the relation between maternal glycemia and neonatal leptin levels using the 2-step epigenetic Mendelian randomization approach. We used data and samples from up to 485 mother-child dyads from Gen3G, a large prospective population-based cohort. First, we built a genetic risk score to capture maternal glycemia based on 10 known glycemic genetic variants (GRS10) and showed it was an adequate instrumental variable (β = 0.046 mmol/L of maternal fasting glucose per additional risk allele; SE = 0.007; P = 7.8 × 10(-11); N = 467). A higher GRS10 was associated with lower methylation levels at cg12083122 located near LEP (β = -0.072 unit per additional risk allele; SE = 0.04; P = 0.05; N = 166). Direction and effect size of association between the instrumental variable GRS10 and methylation at cg12083122 were consistent with the negative association we observed using measured maternal glycemia. Lower DNA methylation levels at cg12083122 were associated with higher cord blood leptin levels (β = -0.17 log of cord blood leptin per unit; SE = 0.07; P = 0.01; N = 170). Our study supports that maternal glycemia is part of causal pathways influencing offspring leptin epigenetic regulation.

Published
2015
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11. A study in familial hypercholesterolemia suggests reduced methylomic plasticity in men with coronary artery disease.

Author

Guay SP, Brisson D, Mathieu P, Bossé Y, Gaudet D, and Bouchard L

Subjects
Collagen genetics, Coronary Artery Disease complications, Epigenomics methods, Gene Ontology, Genome-Wide Association Study, Glycoproteins genetics, Humans, Hyperlipoproteinemia Type II genetics, Male, Matrix Metalloproteinase 9 genetics, Middle Aged, Coronary Artery Disease genetics, DNA Methylation, Epigenesis, Genetic, Hyperlipoproteinemia Type II complications
Abstract

Aim: To assess whether DNA methylation is associated with coronary artery disease (CAD)., Materials & Methods: An epigenome-wide analysis has been performed on leucocytes from familial hypercholesterolemic (FH) men with (n = 6) or without CAD (n = 6). The results were replicated in an extended sample of FH men (n = 61) and in non-FH men (n = 100) for two of the top differentially methylated loci., Results: FH men with CAD had significantly more hypomethylated and hypermethylated loci and showed less DNA methylation level variability compared with men without CAD (p < 0.001). Moreover, COL14A1 and MMP9 DNA methylation levels were associated with CAD, age of onset of CAD or CAD risk factors., Conclusion: These results suggest that epigenome-wide changes are associated with CAD occurrence in men.

Published
2015
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13. Epigenome-wide analysis in familial hypercholesterolemia identified new loci associated with high-density lipoprotein cholesterol concentration.

Author

Guay SP, Voisin G, Brisson D, Munger J, Lamarche B, Gaudet D, and Bouchard L

Subjects
Cholesterol, HDL blood, Cohort Studies, DNA Methylation, Genetic Loci, Genome-Wide Association Study, Humans, Hyperlipoproteinemia Type II blood, Lipid Metabolism, Promoter Regions, Genetic, RNA, Messenger biosynthesis, Troponin T metabolism, Cholesterol, HDL genetics, Epigenesis, Genetic, Hyperlipoproteinemia Type II genetics, Troponin T genetics
Abstract

Aim: This study aims to assess whether epigenetic changes may account for high-density lipoprotein cholesterol (HDL-C) level variability in familial hypercholesterolemia (FH), a recognized human model to study cardiovascular disease risk modulators., Materials & Methods: A genome-wide DNA methylation analysis (Infinium HumanMethylation27 BeadChip, Illumina) was performed on peripheral blood DNA samples obtained from men with FH with low (n = 10) or high (n = 11) HDL-C concentrations. The initial association with one of the top differentially methylated loci located in the promoter of the TNNT1 gene was replicated in a cohort of 276 FH subjects using pyrosequencing., Results: According to the Ingenuity Pathway Analysis software, the HDL-C differentially methylated loci identified were significantly associated with pathways related to lipid metabolism and cardiovascular disease. TNNT1 DNA methylation levels were positively correlated with mean HDL particle size, HDL-phospholipid, HDL-apolipoprotein AI, HDL-C and TNNT1 expression levels., Conclusion: These results suggest that epigenome-wide changes account for interindividual variations in HDL particle metabolism and that TNNT1 is a new candidate gene for dyslipidemia.

Published
2012
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14. Differential epigenomic and transcriptomic responses in subcutaneous adipose tissue between low and high responders to caloric restriction.

Author

Bouchard L, Rabasa-Lhoret R, Faraj M, Lavoie ME, Mill J, Pérusse L, and Vohl MC

Subjects
Aged, Anthropometry, DNA Methylation, Female, Gene Expression Profiling, Gene Expression Regulation, Humans, Middle Aged, Obesity metabolism, Oligonucleotide Array Sequence Analysis, RNA chemistry, RNA genetics, Reverse Transcriptase Polymerase Chain Reaction, Statistics, Nonparametric, Caloric Restriction, Epigenesis, Genetic, Obesity genetics, Subcutaneous Fat metabolism, Transcription, Genetic
Abstract

Background: Caloric restriction is recommended for the treatment of obesity, but it is generally characterized by large interindividual variability in responses. The factors affecting the magnitude of weight loss remain poorly understood. Epigenetic factors (ie, heritable but reversible changes to genomic function that regulate gene expression independently of DNA sequence) may explain some of the interindividual variability seen in weight-loss responses., Objective: The objective was to determine whether epigenetics and gene expression changes may play a role in weight-loss responsiveness., Design: Overweight/obese postmenopausal women were recruited for a standard 6-mo caloric restriction intervention. Abdominal subcutaneous adipose tissue biopsy samples were collected before (n = 14) and after (n = 14) intervention, and the epigenomic and transcriptomic profiles of the high and low responders to dieting, on the basis of changes in percentage body fat, were compared by using microarray analysis., Results: Significant DNA methylation differences at 35 loci were found between the high and low responders before dieting, with 3 regions showing differential methylation after intervention. Some of these regions contained genes known to be involved in weight control and insulin secretion, whereas others were localized in known imprinted genomic regions. Differences in gene expression profiles were observed only after dieting, with 644 genes being differentially expressed between the 2 groups. These included genes likely to be involved in metabolic pathways related to angiogenesis and cerebellar long-term depression., Conclusions: These data show that both DNA methylation and gene expression are responsive to caloric restriction and provide new insights about the molecular pathways involved in body weight loss as well as methylation regulation during adulthood.

Published
2010
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15. Epigenomic profiling reveals DNA-methylation changes associated with major psychosis.

Author

Mill J, Tang T, Kaminsky Z, Khare T, Yazdanpanah S, Bouchard L, Jia P, Assadzadeh A, Flanagan J, Schumacher A, Wang SC, and Petronis A

Subjects
Adult, Base Sequence, Brain metabolism, CpG Islands genetics, Female, Genes, Humans, Male, Middle Aged, Molecular Sequence Data, Oligonucleotide Array Sequence Analysis, Bipolar Disorder genetics, DNA Methylation, Epigenesis, Genetic, Schizophrenia genetics
Abstract

Epigenetic misregulation is consistent with various non-Mendelian features of schizophrenia and bipolar disorder. To date, however, few studies have investigated the role of DNA methylation in major psychosis, and none have taken a genome-wide epigenomic approach. In this study we used CpG-island microarrays to identify DNA-methylation changes in the frontal cortex and germline associated with schizophrenia and bipolar disorder. In the frontal cortex we find evidence for psychosis-associated DNA-methylation differences in numerous loci, including several involved in glutamatergic and GABAergic neurotransmission, brain development, and other processes functionally linked to disease etiology. DNA-methylation changes in a significant proportion of these loci correspond to reported changes of steady-state mRNA level associated with psychosis. Gene-ontology analysis highlighted epigenetic disruption to loci involved in mitochondrial function, brain development, and stress response. Methylome network analysis uncovered decreased epigenetic modularity in both the brain and the germline of affected individuals, suggesting that systemic epigenetic dysfunction may be associated with major psychosis. We also report evidence for a strong correlation between DNA methylation in the MEK1 gene promoter region and lifetime antipsychotic use in schizophrenia patients. Finally, we observe that frontal-cortex DNA methylation in the BDNF gene is correlated with genotype at a nearby nonsynonymous SNP that has been previously associated with major psychosis. Our data are consistent with the epigenetic theory of major psychosis and suggest that DNA-methylation changes are important to the etiology of schizophrenia and bipolar disorder.

Published
2008
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16. Cohort Profile: Pregnancy And Childhood Epigenetics (PACE) Consortium

Author

Felix, JF, Joubert, BR, Baccarelli, AA, Sharp, GC, Almqvist, C, Annesi-Maesano, I, Arshad, H, Baïz, N, Bakermans-Kranenburg, MJ, Bakulski, KM, Binder, EB, Bouchard, L, Breton, CV, Brunekreef, B, Brunst, KJ, Burchard, EG, Bustamante, M, Chatzi, L, Munthe-Kaas, M, Corpeleijn, E, Czamara, D, Dabelea, D, Smith, G, De Boever, P, Duijts, L, Dwyer, T, Eng, C, Eskenazi, B, Everson, TM, Falahi, F, Fallin, MD, Farchi, S, Fernandez, MF, Gao, L, Gaunt, TR, Ghantous, A, Gillman, MW, Gonseth, S, Grote, V, Gruzieva, O, Håberg, SE, Herceg, Z, Hivert, M-F, Holland, N, Holloway, JW, Hoyo, C, Hu, D, Huang, R-C, Huen, K, Järvelin, M-R, Jima, DD, Just, AC, Karagas, MR, Karlsson, R, Karmaus, W, Kechris, KJ, Kere, J, Kogevinas, M, Koletzko, B, Koppelman, GH, Küpers, LK, Ladd-Acosta, C, Lahti, J, Lambrechts, N, Langie, SAS, Lie, RT, Liu, AH, Magnus, MC, Magnus, P, Maguire, RL, Marsit, CJ, McArdle, W, Melén, E, Melton, P, Murphy, SK, Nawrot, TS, Nisticò, L, Nohr, EA, Nordlund, B, Nystad, W, Oh, SS, Oken, E, Page, CM, Perron, P, Pershagen, G, Pizzi, C, Plusquin, M, Raikkonen, K, Reese, SE, Reischl, E, Richiardi, L, Ring, S, Roy, RP, Rzehak, P, Schoeters, G, Schwartz, DA, Sebert, S, Snieder, H, Sørensen, TIA, Starling, AP, Sunyer, J, Taylor, JA, Tiemeier, H, Ullemar, V, Vafeiadi, M, Van Ijzendoorn, MH, Vonk, JM, Vriens, A, Vrijheid, M, Wang, P, Wiemels, JL, Wilcox, AJ, Wright, RJ, Xu, C-J, Xu, Z, Yang, IV, Yousefi, P, Zhang, H, Zhang, W, Zhao, S, Agha, G, Relton, CL, Jaddoe, VWV, London, SJ, Epidemiology, Erasmus MC other, Pediatrics, Child and Adolescent Psychiatry / Psychology, Psychiatry, Research Methods and Techniques, dIRAS RA-2, One Health Chemisch, Reproductive Origins of Adult Health and Disease (ROAHD), Lifestyle Medicine (LM), Groningen Research Institute for Asthma and COPD (GRIAC), Life Course Epidemiology (LCE), Department of Psychology and Logopedics, Helsinki Collegium for Advanced Studies, Medicum, University of Helsinki, and Developmental Psychology Research Group

Subjects
DNA Methylation/physiology, Epidemiology, Maternal Health, education, Embaràs, DISEASE, Environmental Pollution/analysis, Epigenesis, Genetic, Cohort Studies, Prenatal Exposure Delayed Effects/epidemiology, Folic Acid, Pregnancy, Journal Article, Humans, MATERNAL SMOKING, CORD BLOOD, GeneralLiterature_REFERENCE(e.g.,dictionaries,encyclopedias,glossaries), Cohort Profiles, METAANALYSIS, PRENATAL EXPOSURE, Maternal Exposure/adverse effects, EPIGENOME-WIDE ASSOCIATION, 0104 Statistics, Child Health, Infant, Newborn, DNA METHYLATION DATA, DNA Methylation, Epigenètica, BIRTH-WEIGHT, 3142 Public health care science, environmental and occupational health, Folic Acid/blood, 1117 Public Health And Health Services, Maternal Exposure, Prenatal Exposure Delayed Effects, MENDELIAN RANDOMIZATION, Epigenetics, Female, Human medicine, Environmental Pollution
Abstract

UK Medical Research Council; Wellcome Trust [102215/2/13/2, WT088806, 084762MA]; UK Biotechnology and Biological Sciences Research Council [BB/I025751/1, BB/I025263/1]; UK Medical Research Council Integrative Epidemiology Unit; University of Bristol [MC_UU_12013_1, MC_UU_12013_2, MC_UU_12013_5, MC_UU_12013_8]; United States National Institute of Diabetes and Digestive and Kidney Diseases [R01 DK10324]; Swedish Research Council; Swedish Heart-Lung Foundation; Freemason Child House Foundation in Stockholm; MeDALL (Mechanisms of the Development of ALLergy), within the European Union [261357]; Stockholm County Council (ALF); Swedish Foundation for Strategic Research (SSF) [RBc08-0027]; Strategic Research Programme (SFO) in Epidemiology at Karolinska Institutet; Swedish Research Council Formas; Swedish Environment Protection Agency; Center for Integrative Research on Childhood Leukemia and the Environment [P01ES018172]; NIH [P50ES018172, R01ES09137, 5P30CA082103, P01 ES009605, R01 ES021369, R01ES023067, K01ES017801, R01ES022216, P30ES007048, R01ES014447, P01ES009581, R826708-01, RD831861-01, P50ES026086, R01DK068001, R01 DK100340, R01 DK076648, R01ES022934, R01HL111108, R01NR013945, R37 HD034568, UL1 TR001082, P30 DK56350]; EPA [RD83451101, RD83615901, RD 82670901, RD 83451301, 83615801-0]; UCSF Comprehensive Cancer Center Support grant [P30 CA82103]; Swiss Science National Foundation [P2LAP3_158674]; Sutter-Stottner Foundation; Commission of the European Community, specific RTD Programme 'Quality of Life and Management of Living Resources' within the 5th Framework Programme [QLRT-2001-00389, QLK1-CT-2002-30582]; 6th Framework Programme [007036]; European Union's Seventh Framework Programme (FP7), project EarlyNutrition [289346]; European Research Council Advanced grant ERC-AdG [322605 META-GROWTH]; Autism Speaks grant [260377]; Funds for Research in Respiratory Health; French Ministry of Research: IFR program; INSERM Nutrition Research Program; French Ministry of Health: Perinatality Program; French National Institute for Population Health Surveillance (INVS); Paris-Sud University; French National Institute for Health Education (INPES); Nestle; Mutuelle Generale de l'Education Nationale (MGEN); French-speaking association for the study of diabetes and metabolism (Alfediam) [2012/51290-6]; EU; European Research Council [ERC-2012-StG.310898, 268479-BREATHE]; Flemish Scientific Research Council (FWO) [N1516112 / G.0.873.11N.10]; European Community's Seventh Framework Programme FP7 project EXPOsOMICS [308610]; People Program (Marie Curie Actions) of the European Union's Seventh Framework Program FP7 under REA grant [628858]; Bijzonder Onderzoeksfonds (BOF) Hasselt University; Ministry of the Flemish Community (Department of Economics, Science and Innovation); Ministry of the Flemish Community (Department of Environment, Nature and Energy); CEFIC LRI award by the Research Foundation-Flanders (FWO); CEFIC LRI award by the Research Foundation-Flanders (FWO) [12L5216N]; Flemish Institute for Technological Research (VITO) [12L5216N]; Bill AMP; Melinda Gates Foundation Grand Challenges Exploration grant [OPP119403]; Sandler Family Foundation; American Asthma Foundation; National Institutes of Health; National Heart, Lung and Blood Institute [HL117004]; National Institute of Environmental Health Sciences [ES24844]; National Institute on Minority Health and Health Disparities [MD006902, MD009523]; National Institute of General Medical Sciences [GM007546]; Tobacco-Related Disease Research Program [24RT-0025]; Hutchison Whampoa Ltd, Hong Kong; University of Groningen; Well Baby Clinic Foundation Icare; Noordlease; Youth Health Care Drenthe; Biobanking and Biomolecular Research Infrastructure Netherlands [CP2011-19]; Erasmus Medical Center, Rotterdam; Erasmus University Rotterdam; Netherlands Organization for Health Research and Development; Netherlands Genomics Initiative (NGI)/Netherlands Organization for Scientific Research (NWO); Netherlands Consortium for Healthy Aging (NCHA) [050-060-810]; Genetic Laboratory of the Department of Internal Medicine, Erasmus MC; European Union's Horizon research and innovation programme [733206, 633595]; National Institute of Child and Human Development [R01HD068437]; Netherlands Organization for Health Research and Development [VIDI 016.136.361]; Consolidator grant from the European Research Council [ERC-2014-CoG-648916]; Netherlands' Organization for Scientific Research (NWO VICI); European Research Council ERC; Netherlands' Organization for Scientific Research (NWO Spinoza Award); Gravitation program of the Dutch Ministry of Education, Culture, and Science; Netherlands Organization for Scientific Research (NWO) [024.001.003]; Lung Foundation Netherlands [3.2.12.089]; Fonds de Recherche du Quebec en Sante (FRQ-S) [20697]; Canadian Institute of Health Reseach (CIHR) [MOP 115071]; Diabete Quebec grant; Canadian Diabetes Association operating grant [OG-3-08-2622]; American Diabetes Association Pathways Accelerator Early Investigator Award [1-15-ACE-26]; MRC Integrative Epidemiology Unit - Medical Research Council [MC_UU_12013/1-9]; National Institute of Environmental Health Sciences, National Institutes of Health [K99ES025817]; Instituto de Salud Carlos III [Red INMA G03/176, CB06/02/0041]; Spanish Ministry of Health [FIS-PI04/1436, FIS-PI08/1151]; Spanish Ministry of Health (FEDER funds) [FIS-PI11/00610, FIS-FEDER-PI06/0867, FIS-FEDER-PI03-1615]; Generalitat de Catalunya [CIRIT 1999SGR 00241]; Fundacio La Marato de TV3 [090430]; EU Commission [261357-MeDALL]; National Institute of Allergy and Infectious Diseases [N01-AI90052]; National Institutes of Health USA [R01 HL082925, R01 HL132321]; Asthma UK [364]; NIAID/NIH [R01AI091905, R01AI121226]; National Institute of Health [R01AI121226, R01 AI091905, R01HL132321]; NIH/NIEHS [N01-ES75558]; NIH/NINDS [1 UO1 NS 047537-01, 2 UO1 NS 047537-06A1]; Intramural Research Program of the NIH, National Institute of Environmental Health Sciences [Z01-ES-49019, Z01 ES044005, ES049033, ES049032]; Norwegian Research Council/BIOBANK [221097]; Oslo University Hospital; Unger-Vetlesens foundation; Norwegian American Womens Club; INCA/Plan Cancer-EVA-INSERM, France; International Childhood Cancer Cohort Consortium (I4C); INCA/Plan Cancer-EVA-INSERM (France); IARC Postdoctoral Fellowship; EC FP7 Marie Curie Actions-People-Co-funding of regional, national and international programmes (COFUND); NIEHS [R21ES014947, R01ES016772]; NIDDK [R01DK085173]; National Institute of Environmental Health Science [P30 ES025128]; University of Oulu grant [65354]; Oulu University Hospital [2/97, 8/97]; Ministry of Health and Social Affairs [23/251/97, 160/97, 190/97]; National Institute for Health and Welfare, Helsinki [54121]; Regional Institute of Occupational Health, Oulu, Finland [50621, 54231]; EU [QLG1-CT-2000-01643, E51560]; NorFA grant [731, 20056, 30167]; Academy of Finland; NIH-NIEHS [P01 ES022832]; US EPA [RD83544201]; NIH-NIGMS [P20GM104416]; NCI [R25CA134286]; Netherlands Organization for Scientific Research and Development; Netherlands Asthma Fund; Netherlands Ministry of Spatial Planning, Housing, and the Environment; Netherlands Ministry of Health, Welfare, and Sport; MeDALL; European Union under the Health Cooperation Work Program of the 7th Framework program [261357]; Italian National Centre for Disease Prevention and Control (CCM grant); Italian Ministry of Health (art 12); Italian Ministry of Health (12bis Dl.gs.vo) [502/92]; EraNet; EVO; University of Helsinki Research Funds; Signe and Ane Gyllenberg foundation; Emil Aaltonen Foundation; Finnish Medical Foundation; Jane and Aatos Erkko Foundation; Novo Nordisk Foundation; Paivikki and Sakari Sohlberg Foundation; Sigrid Juselius Foundation; University of Helsinki; University of Western Australia (UWA); Curtin University; Raine Medical Research Foundation; UWA Faculty of Medicine, Dentistry and Health Sciences; Telethon Kids Institute; Women's and Infant's Research Foundation (KEMH); Edith Cowan University; National Health and Medical Research Council [1059711]; National Health and Medical Research Council (NHMRC) fellowship [1053384]; Australian National Health and Medical Research Council; United States National Institute of Health; Greek Ministry of Health (programme of prevention of obesity and neurodevelopmental disorders in preschool children, in Heraklion district, Crete, Greece); Greek Ministry of Health ('Rhea Plus': Primary Prevention Program of Environmental Risk Factors for Reproductive Health, and Child Health); European Union (EU) [EU FP6-2003-Food-3-NewGeneris, EU FP7 ENV.2007.1.2.2.2, 211250 ESCAPE, EU FP7-2008-ENV-1.2.1.4 Envirogenomarkers, EU FP7 ENV.2008.1.2.1.6, 226285 ENRIECO]; National Institutes of Health [NIH-NIMH R01MH094609, NIH-NIEHS R01ES022223, NIH-NIEHS R01ES025145]; Centers for Disease Control and Prevention [U10DD000180, U10DD000181, U10DD000182, U10DD000183, U10DD000184, U10DD000498]; Autism Speaks [7659]; Swedish Research Council through the Swedish Initiative for research on Microdata in the Social And Medical Sciences (SIMSAM) [340-2013-5867]; Stockholm County Council (ALF projects); Strategic Research Program in Epidemiology at Karolinska Institutet; Swedish Asthma and Allergy Association's Research Foundation; Stiftelsen Frimurare Barnahuset Stockholm; Norwegian Ministry of Health and Care Services; Ministry of the Flemish Community (Flemish Agency for Care and Health); University of Bristol; Ministry of Education and Research; European Union (EU) (EU FP7-HEALTH-single stage CHICOS); European Union (EU) (EU-FP7-HEALTH) [308333 HELIX]; European Union (EU) (EU FP6. STREP HiWATE); UK Medical Research Council; Wellcome Trust [102215/2/13/2, WT088806, 084762MA]; UK Biotechnology and Biological Sciences Research Council [BB/I025751/1, BB/I025263/1]; UK Medical Research Council Integrative Epidemiology Unit; University of Bristol [MC_UU_12013_1, MC_UU_12013_2, MC_UU_12013_5, MC_UU_12013_8]; United States National Institute of Diabetes and Digestive and Kidney Diseases [R01 DK10324]; Swedish Research Council; Swedish Heart-Lung Foundation; Freemason Child House Foundation in Stockholm; MeDALL (Mechanisms of the Development of ALLergy), within the European Union [261357]; Stockholm County Council (ALF); Swedish Foundation for Strategic Research (SSF) [RBc08-0027]; Strategic Research Programme (SFO) in Epidemiology at Karolinska Institutet; Swedish Research Council Formas; Swedish Environment Protection Agency; Center for Integrative Research on Childhood Leukemia and the Environment [P01ES018172]; NIH [P50ES018172, R01ES09137, 5P30CA082103, P01 ES009605, R01 ES021369, R01ES023067, K01ES017801, R01ES022216, P30ES007048, R01ES014447, P01ES009581, R826708-01, RD831861-01, P50ES026086, R01DK068001, R01 DK100340, R01 DK076648, R01ES022934, R01HL111108, R01NR013945, R37 HD034568, UL1 TR001082, P30 DK56350]; EPA [RD83451101, RD83615901, RD 82670901, RD 83451301, 83615801-0]; UCSF Comprehensive Cancer Center Support grant [P30 CA82103]; Swiss Science National Foundation [P2LAP3_158674]; Sutter-Stottner Foundation; Commission of the European Community, specific RTD Programme 'Quality of Life and Management of Living Resources' within the 5th Framework Programme [QLRT-2001-00389, QLK1-CT-2002-30582]; 6th Framework Programme [007036]; European Union's Seventh Framework Programme (FP7), project EarlyNutrition [289346]; European Research Council Advanced grant ERC-AdG [322605 META-GROWTH]; Autism Speaks grant [260377]; Funds for Research in Respiratory Health; French Ministry of Research: IFR program; INSERM Nutrition Research Program; French Ministry of Health: Perinatality Program; French National Institute for Population Health Surveillance (INVS); Paris-Sud University; French National Institute for Health Education (INPES); Nestle; Mutuelle Generale de l'Education Nationale (MGEN); French-speaking association for the study of diabetes and metabolism (Alfediam) [2012/51290-6]; EU; European Research Council [ERC-2012-StG.310898, 268479-BREATHE]; Flemish Scientific Research Council (FWO) [N1516112 / G.0.873.11N.10]; European Community's Seventh Framework Programme FP7 project EXPOsOMICS [308610]; People Program (Marie Curie Actions) of the European Union's Seventh Framework Program FP7 under REA grant [628858]; Bijzonder Onderzoeksfonds (BOF) Hasselt University; Ministry of the Flemish Community (Department of Economics, Science and Innovation); Ministry of the Flemish Community (Department of Environment, Nature and Energy); CEFIC LRI award by the Research Foundation-Flanders (FWO); CEFIC LRI award by the Research Foundation-Flanders (FWO) [12L5216N]; Flemish Institute for Technological Research (VITO) [12L5216N]; Bill AMP; Melinda Gates Foundation Grand Challenges Exploration grant [OPP119403]; Sandler Family Foundation; American Asthma Foundation; National Institutes of Health; National Heart, Lung and Blood Institute [HL117004]; National Institute of Environmental Health Sciences [ES24844]; National Institute on Minority Health and Health Disparities [MD006902, MD009523]; National Institute of General Medical Sciences [GM007546]; Tobacco-Related Disease Research Program [24RT-0025]; Hutchison Whampoa Ltd, Hong Kong; University of Groningen; Well Baby Clinic Foundation Icare; Noordlease; Youth Health Care Drenthe; Biobanking and Biomolecular Research Infrastructure Netherlands [CP2011-19]; Erasmus Medical Center, Rotterdam; Erasmus University Rotterdam; Netherlands Organization for Health Research and Development; Netherlands Genomics Initiative (NGI)/Netherlands Organization for Scientific Research (NWO); Netherlands Consortium for Healthy Aging (NCHA) [050-060-810]; Genetic Laboratory of the Department of Internal Medicine, Erasmus MC; European Union's Horizon research and innovation programme [733206, 633595]; National Institute of Child and Human Development [R01HD068437]; Netherlands Organization for Health Research and Development [VIDI 016.136.361]; Consolidator grant from the European Research Council [ERC-2014-CoG-648916]; Netherlands' Organization for Scientific Research (NWO VICI); European Research Council ERC; Netherlands' Organization for Scientific Research (NWO Spinoza Award); Gravitation program of the Dutch Ministry of Education, Culture, and Science; Netherlands Organization for Scientific Research (NWO) [024.001.003]; Lung Foundation Netherlands [3.2.12.089]; Fonds de Recherche du Quebec en Sante (FRQ-S) [20697]; Canadian Institute of Health Reseach (CIHR) [MOP 115071]; Diabete Quebec grant; Canadian Diabetes Association operating grant [OG-3-08-2622]; American Diabetes Association Pathways Accelerator Early Investigator Award [1-15-ACE-26]; MRC Integrative Epidemiology Unit - Medical Research Council [MC_UU_12013/1-9]; National Institute of Environmental Health Sciences, National Institutes of Health [K99ES025817]; Instituto de Salud Carlos III [Red INMA G03/176, CB06/02/0041]; Spanish Ministry of Health [FIS-PI04/1436, FIS-PI08/1151]; Spanish Ministry of Health (FEDER funds) [FIS-PI11/00610, FIS-FEDER-PI06/0867, FIS-FEDER-PI03-1615]; Generalitat de Catalunya [CIRIT 1999SGR 00241]; Fundacio La Marato de TV3 [090430]; EU Commission [261357-MeDALL]; National Institute of Allergy and Infectious Diseases [N01-AI90052]; National Institutes of Health USA [R01 HL082925, R01 HL132321]; Asthma UK [364]; NIAID/NIH [R01AI091905, R01AI121226]; National Institute of Health [R01AI121226, R01 AI091905, R01HL132321]; NIH/NIEHS [N01-ES75558]; NIH/NINDS [1 UO1 NS 047537-01, 2 UO1 NS 047537-06A1]; Intramural Research Program of the NIH, National Institute of Environmental Health Sciences [Z01-ES-49019, Z01 ES044005, ES049033, ES049032]; Norwegian Research Council/BIOBANK [221097]; Oslo University Hospital; Unger-Vetlesens foundation; Norwegian American Womens Club; INCA/Plan Cancer-EVA-INSERM, France; International Childhood Cancer Cohort Consortium (I4C); INCA/Plan Cancer-EVA-INSERM (France); IARC Postdoctoral Fellowship; EC FP7 Marie Curie Actions-People-Co-funding of regional, national and international programmes (COFUND); NIEHS [R21ES014947, R01ES016772]; NIDDK [R01DK085173]; National Institute of Environmental Health Science [P30 ES025128]; University of Oulu grant [65354]; Oulu University Hospital [2/97, 8/97]; Ministry of Health and Social Affairs [23/251/97, 160/97, 190/97]; National Institute for Health and Welfare, Helsinki [54121]; Regional Institute of Occupational Health, Oulu, Finland [50621, 54231]; EU [QLG1-CT-2000-01643, E51560]; NorFA grant [731, 20056, 30167]; Academy of Finland; NIH-NIEHS [P01 ES022832]; US EPA [RD83544201]; NIH-NIGMS [P20GM104416]; NCI [R25CA134286]; Netherlands Organization for Scientific Research and Development; Netherlands Asthma Fund; Netherlands Ministry of Spatial Planning, Housing, and the Environment; Netherlands Ministry of Health, Welfare, and Sport; MeDALL; European Union under the Health Cooperation Work Program of the 7th Framework program [261357]; Italian National Centre for Disease Prevention and Control (CCM grant); Italian Ministry of Health (art 12); Italian Ministry of Health (12bis Dl.gs.vo) [502/92]; EraNet; EVO; University of Helsinki Research Funds; Signe and Ane Gyllenberg foundation; Emil Aaltonen Foundation; Finnish Medical Foundation; Jane and Aatos Erkko Foundation; Novo Nordisk Foundation; Paivikki and Sakari Sohlberg Foundation; Sigrid Juselius Foundation; University of Helsinki; University of Western Australia (UWA); Curtin University; Raine Medical Research Foundation; UWA Faculty of Medicine, Dentistry and Health Sciences; Telethon Kids Institute; Women's and Infant's Research Foundation (KEMH); Edith Cowan University; National Health and Medical Research Council [1059711]; National Health and Medical Research Council (NHMRC) fellowship [1053384]; Australian National Health and Medical Research Council; United States National Institute of Health; Greek Ministry of Health (programme of prevention of obesity and neurodevelopmental disorders in preschool children, in Heraklion district, Crete, Greece); Greek Ministry of Health ('Rhea Plus': Primary Prevention Program of Environmental Risk Factors for Reproductive Health, and Child Health); European Union (EU) [EU FP6-2003-Food-3-NewGeneris, EU FP7 ENV.2007.1.2.2.2, 211250 ESCAPE, EU FP7-2008-ENV-1.2.1.4 Envirogenomarkers, EU FP7 ENV.2008.1.2.1.6, 226285 ENRIECO]; National Institutes of Health [NIH-NIMH R01MH094609, NIH-NIEHS R01ES022223, NIH-NIEHS R01ES025145]; Centers for Disease Control and Prevention [U10DD000180, U10DD000181, U10DD000182, U10DD000183, U10DD000184, U10DD000498]; Autism Speaks [7659]; Swedish Research Council through the Swedish Initiative for research on Microdata in the Social And Medical Sciences (SIMSAM) [340-2013-5867]; Stockholm County Council (ALF projects); Strategic Research Program in Epidemiology at Karolinska Institutet; Swedish Asthma and Allergy Association's Research Foundation; Stiftelsen Frimurare Barnahuset Stockholm; Norwegian Ministry of Health and Care Services; Ministry of the Flemish Community (Flemish Agency for Care and Health); University of Bristol; Ministry of Education and Research; European Union (EU) (EU FP7-HEALTH-single stage CHICOS); European Union (EU) (EU-FP7-HEALTH) [308333 HELIX]; European Union (EU) (EU FP6. STREP HiWATE); [R01ES017646]; [R01ES01900]; [R01ES16443]; [USA / NIHH 2000 G DF682]; [50945]; [R01 HL095606]; [R01 HL1143396]

Published
2018

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