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Meta-Analysis
. 2019 Apr 23;10(1):1893.
doi: 10.1038/s41467-019-09671-3.

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

Leanne K Küpers  1   2   3   4 Claire Monnereau  5   6   7 Gemma C Sharp  1   8 Paul Yousefi  1   2   9 Lucas A Salas  10   11 Akram Ghantous  12 Christian M Page  13   14 Sarah E Reese  15 Allen J Wilcox  15 Darina Czamara  16 Anne P Starling  17 Alexei Novoloaca  12 Samantha Lent  18 Ritu Roy  19   20 Cathrine Hoyo  21   22 Carrie V Breton  23 Catherine Allard  24 Allan C Just  25 Kelly M Bakulski  26 John W Holloway  27   28 Todd M Everson  29 Cheng-Jian Xu  30   31 Rae-Chi Huang  32 Diana A van der Plaat  33 Matthias Wielscher  34 Simon Kebede Merid  35 Vilhelmina Ullemar  36 Faisal I Rezwan  28 Jari Lahti  37   38 Jenny van Dongen  39 Sabine A S Langie  40   41   42 Tom G Richardson  1   2 Maria C Magnus  1   2   13 Ellen A Nohr  43 Zongli Xu  44 Liesbeth Duijts  4   44   45 Shanshan Zhao  46 Weiming Zhang  47 Michelle Plusquin  48   49 Dawn L DeMeo  50 Olivia Solomon  8 Joosje H Heimovaara  3 Dereje D Jima  22   51 Lu Gao  23 Mariona Bustamante  11   52   53   54 Patrice Perron  24   55 Robert O Wright  25 Irva Hertz-Picciotto  56 Hongmei Zhang  57 Margaret R Karagas  10   58 Ulrike Gehring  59 Carmen J Marsit  29 Lawrence J Beilin  60 Judith M Vonk  33 Marjo-Riitta Jarvelin  34   61   62   63 Anna Bergström  35   64 Anne K Örtqvist  36 Susan Ewart  65 Pia M Villa  66 Sophie E Moore  67   68 Gonneke Willemsen  39 Arnout R L Standaert  40 Siri E Håberg  13 Thorkild I A Sørensen  1   69   70 Jack A Taylor  15 Katri Räikkönen  38 Ivana V Yang  71 Katerina Kechris  45 Tim S Nawrot  48   72 Matt J Silver  67 Yun Yun Gong  73 Lorenzo Richiardi  74   75 Manolis Kogevinas  11   53   54   76 Augusto A Litonjua  50 Brenda Eskenazi  9   77 Karen Huen  9 Hamdi Mbarek  78 Rachel L Maguire  21   79 Terence Dwyer  80 Martine Vrijheid  11   53   54 Luigi Bouchard  81   82 Andrea A Baccarelli  83   84 Lisa A Croen  85 Wilfried Karmaus  57 Denise Anderson  32 Maaike de Vries  33 Sylvain Sebert  61   62   86 Juha Kere  87   88   89 Robert Karlsson  36 Syed Hasan Arshad  27   90 Esa Hämäläinen  91 Michael N Routledge  92 Dorret I Boomsma  39   93 Andrew P Feinberg  94 Craig J Newschaffer  95 Eva Govarts  40 Matthieu Moisse  96   97 M Daniele Fallin  98 Erik Melén  35   99 Andrew M Prentice  67 Eero Kajantie  100   101   102 Catarina Almqvist  36   103 Emily Oken  104 Dana Dabelea  105 H Marike Boezen  33 Phillip E Melton  106   107 Rosalind J Wright  25 Gerard H Koppelman  30 Letizia Trevisi  108 Marie-France Hivert  55   104   109 Jordi Sunyer  11   53   54   76 Monica C Munthe-Kaas  110   111 Susan K Murphy  112 Eva Corpeleijn  3 Joseph Wiemels  113 Nina Holland  9 Zdenko Herceg  12 Elisabeth B Binder  16   114 George Davey Smith  1   2 Vincent W V Jaddoe  5   6   7 Rolv T Lie  13   115 Wenche Nystad  116 Stephanie J London  15 Debbie A Lawlor  117   118 Caroline L Relton  119   120 Harold Snieder  121 Janine F Felix  122   123   124
Affiliations
Meta-Analysis

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

Leanne K Küpers et al. Nat Commun. .

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 (PBonferroni < 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.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Fig. 1
Fig. 1
Hypothetical paths that might link intrauterine exposures to DNA methylation, birthweight and later-life health outcomes. Red arrows summarise the paths that have motivated the analyses undertaken in this study (i.e. that maternal environmental exposures influence DNA methylation that in turn influences fetal growth and hence birthweight). The EWAS meta-analysis undertaken sought to identify methylation associated with birthweight. Blue arrows summarise other plausible paths, including that maternal exposures influence fetal growth first and it then influences DNA methylation or that maternal exposures may influence fetal growth/birthweight and later-life health outcomes through other pathways than DNA methylation
Fig. 2
Fig. 2
Design of the study. Schematic representation of the main meta-analysis, secondary meta-analyses, follow-up analyses and exploration of persistence at older ages. *We removed multiple births from all analyses and excluded preterm births (<37 weeks) and offspring of mothers with pre-eclampsia or diabetes (three major pathological causes of differences). **For sufficient power in the low vs normal BW analyses, we only included nine studies with >10 low birthweight cases
Fig. 3
Fig. 3
Volcano plot showing the direction of associations of DNA methylation with birthweight in 8825 neonates from 24 studies. The X-axis represents the difference in birthweight in grams per 10% methylation difference, the Y-axis represents the −log10(P). The red line shows the Bonferroni-corrected significance threshold for multiple testing (p < 1.06 × 10−7). Highlighted in orange are the 914 CpGs with p < 1.06 × 10−7 and I2 ≤ 50% and highlighted in blue are the 115 CpGs with p < 1.06 × 10−7 and I2 > 50%
Fig. 4
Fig. 4
Circos plot showing the (Bonferroni-corrected p < 1.06 × 10−7) results for associations of DNA methylation with birthweight. Results are presented as CpG-specific associations (−log10(P), each dot represents a CpG) by genomic position, per chromosome. From outer to inner track: [1, orange] Main analysis results for associations between DNA methylation and birthweight as a continuous measure (n = 8825), [2, blue] Results from participants from European ethnicity only, DNA methylation and birthweight as a continuous measure (n = 6023), [3, red] Results from analysis without exclusion for preterm births, pre-eclampsia and maternal diabetes, DNA methylation and birthweight as a continuous measure n = 5414), [4, purple] Results from logistic regression analysis without exclusion for preterm births, pre-eclampsia and maternal diabetes, for low (n = 178) vs normal (n = 4197) birthweight, [5, yellow] Results from logistic regression analysis for associations between DNA methylation and high (n = 1590) vs normal (n = 6114) birthweight, [6, green] Results from look-up analysis in methylation samples taken during childhood and its association with birthweight as a continuous measure (n = 2756). Track 1: highlighted in red are 115 CpGs with I2 > 50%. Tracks 2–6: highlighted in red are CpGs that were not found in the 914 main meta-analysis hits (though note differences in sample size and hence statistical power for different analyses presented in the different tracks)

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References

    1. Tyrrell J, et al. Genetic evidence for causal relationships between maternal obesity-related traits and birth weight. JAMA. 2016;315:1129. doi: 10.1001/jama.2016.1975. - DOI - PMC - PubMed
    1. Tyrrell J, et al. Genetic variation in the 15q25 nicotinic acetylcholine receptor gene cluster (CHRNA5-CHRNA3-CHRNB4) interacts with maternal self-reported smoking status during pregnancy to influence birth weight. Hum. Mol. Genet. 2012;21:5344–5358. doi: 10.1093/hmg/dds372. - DOI - PMC - PubMed
    1. Bakker R, Steegers EAP, Hofman A, Jaddoe VWV. Blood pressure in different gestational trimesters, fetal growth, and the risk of adverse birth outcomes: the generation R study. Am. J. Epidemiol. 2011;174:797–806. doi: 10.1093/aje/kwr151. - DOI - PubMed
    1. Lawlor DA, et al. Association of existing diabetes, gestational diabetes and glycosuria in pregnancy with macrosomia and offspring body mass index, waist and fat mass in later childhood: Findings from a prospective pregnancy cohort. Diabetologia. 2010;53:89–97. doi: 10.1007/s00125-009-1560-z. - DOI - PubMed
    1. van Uitert EM, Steegers-Theunissen RPM. Influence of maternal folate status on human fetal growth parameters. Mol. Nutr. Food. Res. 2013;57:582–595. doi: 10.1002/mnfr.201200084. - DOI - PubMed

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