Epigenome-wide contributions to individual differences in childhood phenotypes: A GREML approach

Alexander Neumann; Jean-Baptiste Pingault; Janine F Felix; Vincent W V Jaddoe; Henning Tiemeier; Charlotte A.M. Cecil; Esther Walton

doi:10.1186/s13148-022-01268-w

Epigenome-wide contributions to individual differences in childhood phenotypes: A GREML approach

Alexander Neumann, Jean-Baptiste Pingault, Janine F Felix, Vincent W V Jaddoe, Henning Tiemeier, Charlotte A.M. Cecil, Esther Walton

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Abstract

Background

DNA methylation is an epigenetic mechanism involved in human development. Numerous epigenome-wide association studies (EWAS) have investigated the associations of DNA methylation at single CpG sites with childhood outcomes. However, the overall contribution of DNA methylation across the genome (R2Methylation) towards childhood phenotypes is unknown. An estimate of R2Methylation would provide context regarding the importance of DNA methylation explaining variance in health outcomes. We therefore estimated the variance explained by epigenome-wide cord blood methylation (R2Methylation) for five childhood phenotypes: gestational age, birth weight, and body mass index (BMI), IQ and ADHD symptoms at school age. We adapted a genome-based restricted maximum likelihood (GREML) approach with cross-validation (CV) to DNA methylation data and applied it in two population-based birth cohorts: ALSPAC (n = 775) and Generation R (n = 1382).
Results

Using information from > 470,000 autosomal probes we estimated that DNA methylation at birth explains 32% (SDCV = 0.06) of gestational age variance and 5% (SDCV = 0.02) of birth weight variance. The R2Methylation estimates for BMI, IQ and ADHD symptoms at school age estimates were near 0% across almost all cross-validation iterations.
Conclusions

The results suggest that cord blood methylation explains a moderate degree of variance in gestational age and birth weight, in line with the success of previous EWAS in identifying numerous CpG sites associated with these phenotypes. In contrast, we could not obtain a reliable estimate for school-age BMI, IQ and ADHD symptoms. This may reflect a null bias due to insufficient sample size to detect variance explained in more weakly associated phenotypes, although the true R2Methylation for these phenotypes is likely below that of gestational age and birth weight when using DNA methylation at birth.

Original language	English
Article number	53
Journal	CLINICAL EPIGENETICS
Volume	14
Issue number	1
DOIs	https://doi.org/10.1186/s13148-022-01268-w
Publication status	Published - 31 Dec 2022

Bibliographical note

Funding Information:
We thank all the children and families who took part in this study, as well as the support of general practitioners, hospitals, midwives and pharmacies. ALSPAC We are extremely grateful to all the families who took part in this study, the midwives for their help in recruiting them, and the whole ALSPAC team, which includes interviewers, computer and laboratory technicians, clerical workers, research scientists, volunteers, managers, receptionists and nurses. GENR The Generation R Study is conducted by Erasmus MC in close collaboration with the Erasmus University Rotterdam, Faculty of Social Sciences, the Municipal Health Service Rotterdam area, the Rotterdam Homecare Foundation, Rotterdam and the Stichting Trombosedienst & Artsenlaboratorium Rijnmond (STAR-MDC), Rotterdam. The generation and management of the Illumina 450K methylation array data (EWAS data) for the Generation R Study was executed by the Human Genotyping Facility of the Genetic Laboratory of the Department of Internal Medicine, Erasmus MC, the Netherlands. We thank Mr. Michael Verbiest, Ms. Mila Jhamai, Ms. Sarah Higgins, Mr. Marijn Verkerk and Dr. Lisette Stolk for their help in creating the EWAS database. We thank Dr. A.Teumer for his work on the quality control and normalization scripts.

Funding Information:
ALSPAC The UK Medical Research Council (MRC) and Wellcome (Grant ref: 102215/2/13/2) and the University of Bristol provide core support for ALSPAC. This publication is the work of the authors and EW will serve as guarantors for the contents of this paper. A comprehensive list of grants funding is available on the ALSPAC website ( http://www.bristol.ac.uk/alspac/external/documents/grant-acknowledgements.pdf ). Methylation data in the ALSPAC cohort were generated as part of the UK BBSRC funded (BB/I025751/1 and BB/I025263/1) Accessible Resource for Integrated Epigenomic Studies (ARIES, http://www.ariesepigenomics.org.uk ). EW was partially funded by the Bath Institute for Mathematical Innovation. EW is also funded by the European Union’s Horizon 2020 research and innovation programme (grant nº 848158) and by CLOSER, whose mission is to maximise the use, value and impact of longitudinal studies. CLOSER was funded by the Economic and Social Research Council (ESRC) and the Medical Research Council (MRC) between 2012 and 2017. Its initial five year grant has since been extended to March 2021 by the ESRC (grant reference: ES/K000357/1). The funders took no role in the design, execution, analysis or interpretation of the data or in the writing up of the findings. www.closer.ac.uk . GENR The general design of the Generation R Study is made possible by financial support from Erasmus MC, University Medical Center Rotterdam, Erasmus University Rotterdam, the Netherlands Organization for Health Research and Development (ZonMw) and the Ministry of Health, Welfare and Sport. The EWAS data was funded by a grant from the Netherlands Genomics Initiative (NGI)/Netherlands Organisation for Scientific Research (NWO) Netherlands Consortium for Healthy Aging (NCHA; project nr. 050-060-810), by funds from the Genetic Laboratory of the Department of Internal Medicine, Erasmus MC, and by a grant from the National Institute of Child and Human Development (R01HD068437). A. Neumann and H. Tiemeier are supported by a grant of the Dutch Ministry of Education, Culture, and Science and the Netherlands Organization for Scientific Research (NWO grant No. 024.001.003, Consortium on Individual Development). A. Neumann is also supported by a Canadian Institutes of Health Research team grant. The work of H. Tiemeier is further supported by a NWO-VICI grant (NWO-ZonMW: 016.VICI.170.200). The work of CC has received funding from the European Union’s Horizon 2020 Research and Innovation Programme under the Marie Skłodowska-Curie grant agreement No 707404.This project received funding from the European Union’s Horizon 2020 research and innovation programme (733206, LifeCycle; 633595, DynaHEALTH; 848158, EarlyCause, 874739, LongITools)) and from the European Joint Programming Initiative “A Healthy Diet for a Healthy Life” (JPI HDHL, NutriPROGRAM project, ZonMw the Netherlands no.529051022 and PREcisE project ZonMw the Netherlands no.529051023).

Keywords

ADHD
BMI
Birth weight
Child development
DNA methylation
Epigenetics
GCTA
GREML
Gestational age
IQ

ASJC Scopus subject areas

Molecular Biology
Genetics
Developmental Biology
Genetics(clinical)

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1186/s13148-022-01268-wLicence: CC BY

Cite this

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title = "Epigenome-wide contributions to individual differences in childhood phenotypes: A GREML approach",

abstract = "BackgroundDNA methylation is an epigenetic mechanism involved in human development. Numerous epigenome-wide association studies (EWAS) have investigated the associations of DNA methylation at single CpG sites with childhood outcomes. However, the overall contribution of DNA methylation across the genome (R2Methylation) towards childhood phenotypes is unknown. An estimate of R2Methylation would provide context regarding the importance of DNA methylation explaining variance in health outcomes. We therefore estimated the variance explained by epigenome-wide cord blood methylation (R2Methylation) for five childhood phenotypes: gestational age, birth weight, and body mass index (BMI), IQ and ADHD symptoms at school age. We adapted a genome-based restricted maximum likelihood (GREML) approach with cross-validation (CV) to DNA methylation data and applied it in two population-based birth cohorts: ALSPAC (n = 775) and Generation R (n = 1382).ResultsUsing information from > 470,000 autosomal probes we estimated that DNA methylation at birth explains 32% (SDCV = 0.06) of gestational age variance and 5% (SDCV = 0.02) of birth weight variance. The R2Methylation estimates for BMI, IQ and ADHD symptoms at school age estimates were near 0% across almost all cross-validation iterations.ConclusionsThe results suggest that cord blood methylation explains a moderate degree of variance in gestational age and birth weight, in line with the success of previous EWAS in identifying numerous CpG sites associated with these phenotypes. In contrast, we could not obtain a reliable estimate for school-age BMI, IQ and ADHD symptoms. This may reflect a null bias due to insufficient sample size to detect variance explained in more weakly associated phenotypes, although the true R2Methylation for these phenotypes is likely below that of gestational age and birth weight when using DNA methylation at birth.",

keywords = "ADHD, BMI, Birth weight, Child development, DNA methylation, Epigenetics, GCTA, GREML, Gestational age, IQ",

author = "Alexander Neumann and Jean-Baptiste Pingault and Felix, {Janine F} and Jaddoe, {Vincent W V} and Henning Tiemeier and Cecil, {Charlotte A.M.} and Esther Walton",

note = "Funding Information: We thank all the children and families who took part in this study, as well as the support of general practitioners, hospitals, midwives and pharmacies. ALSPAC We are extremely grateful to all the families who took part in this study, the midwives for their help in recruiting them, and the whole ALSPAC team, which includes interviewers, computer and laboratory technicians, clerical workers, research scientists, volunteers, managers, receptionists and nurses. GENR The Generation R Study is conducted by Erasmus MC in close collaboration with the Erasmus University Rotterdam, Faculty of Social Sciences, the Municipal Health Service Rotterdam area, the Rotterdam Homecare Foundation, Rotterdam and the Stichting Trombosedienst & Artsenlaboratorium Rijnmond (STAR-MDC), Rotterdam. The generation and management of the Illumina 450K methylation array data (EWAS data) for the Generation R Study was executed by the Human Genotyping Facility of the Genetic Laboratory of the Department of Internal Medicine, Erasmus MC, the Netherlands. We thank Mr. Michael Verbiest, Ms. Mila Jhamai, Ms. Sarah Higgins, Mr. Marijn Verkerk and Dr. Lisette Stolk for their help in creating the EWAS database. We thank Dr. A.Teumer for his work on the quality control and normalization scripts. Funding Information: ALSPAC The UK Medical Research Council (MRC) and Wellcome (Grant ref: 102215/2/13/2) and the University of Bristol provide core support for ALSPAC. This publication is the work of the authors and EW will serve as guarantors for the contents of this paper. A comprehensive list of grants funding is available on the ALSPAC website ( http://www.bristol.ac.uk/alspac/external/documents/grant-acknowledgements.pdf ). Methylation data in the ALSPAC cohort were generated as part of the UK BBSRC funded (BB/I025751/1 and BB/I025263/1) Accessible Resource for Integrated Epigenomic Studies (ARIES, http://www.ariesepigenomics.org.uk ). EW was partially funded by the Bath Institute for Mathematical Innovation. EW is also funded by the European Union{\textquoteright}s Horizon 2020 research and innovation programme (grant nº 848158) and by CLOSER, whose mission is to maximise the use, value and impact of longitudinal studies. CLOSER was funded by the Economic and Social Research Council (ESRC) and the Medical Research Council (MRC) between 2012 and 2017. Its initial five year grant has since been extended to March 2021 by the ESRC (grant reference: ES/K000357/1). The funders took no role in the design, execution, analysis or interpretation of the data or in the writing up of the findings. www.closer.ac.uk . GENR The general design of the Generation R Study is made possible by financial support from Erasmus MC, University Medical Center Rotterdam, Erasmus University Rotterdam, the Netherlands Organization for Health Research and Development (ZonMw) and the Ministry of Health, Welfare and Sport. The EWAS data was funded by a grant from the Netherlands Genomics Initiative (NGI)/Netherlands Organisation for Scientific Research (NWO) Netherlands Consortium for Healthy Aging (NCHA; project nr. 050-060-810), by funds from the Genetic Laboratory of the Department of Internal Medicine, Erasmus MC, and by a grant from the National Institute of Child and Human Development (R01HD068437). A. Neumann and H. Tiemeier are supported by a grant of the Dutch Ministry of Education, Culture, and Science and the Netherlands Organization for Scientific Research (NWO grant No. 024.001.003, Consortium on Individual Development). A. Neumann is also supported by a Canadian Institutes of Health Research team grant. The work of H. Tiemeier is further supported by a NWO-VICI grant (NWO-ZonMW: 016.VICI.170.200). The work of CC has received funding from the European Union{\textquoteright}s Horizon 2020 Research and Innovation Programme under the Marie Sk{\l}odowska-Curie grant agreement No 707404.This project received funding from the European Union{\textquoteright}s Horizon 2020 research and innovation programme (733206, LifeCycle; 633595, DynaHEALTH; 848158, EarlyCause, 874739, LongITools)) and from the European Joint Programming Initiative “A Healthy Diet for a Healthy Life” (JPI HDHL, NutriPROGRAM project, ZonMw the Netherlands no.529051022 and PREcisE project ZonMw the Netherlands no.529051023). ",

year = "2022",

month = dec,

day = "31",

doi = "10.1186/s13148-022-01268-w",

language = "English",

volume = "14",

journal = "CLINICAL EPIGENETICS",

issn = "1868-7075",

publisher = "Springer Verlag",

number = "1",

}

TY - JOUR

T1 - Epigenome-wide contributions to individual differences in childhood phenotypes

T2 - A GREML approach

AU - Neumann, Alexander

AU - Pingault, Jean-Baptiste

AU - Felix, Janine F

AU - Jaddoe, Vincent W V

AU - Tiemeier, Henning

AU - Cecil, Charlotte A.M.

AU - Walton, Esther

N1 - Funding Information: We thank all the children and families who took part in this study, as well as the support of general practitioners, hospitals, midwives and pharmacies. ALSPAC We are extremely grateful to all the families who took part in this study, the midwives for their help in recruiting them, and the whole ALSPAC team, which includes interviewers, computer and laboratory technicians, clerical workers, research scientists, volunteers, managers, receptionists and nurses. GENR The Generation R Study is conducted by Erasmus MC in close collaboration with the Erasmus University Rotterdam, Faculty of Social Sciences, the Municipal Health Service Rotterdam area, the Rotterdam Homecare Foundation, Rotterdam and the Stichting Trombosedienst & Artsenlaboratorium Rijnmond (STAR-MDC), Rotterdam. The generation and management of the Illumina 450K methylation array data (EWAS data) for the Generation R Study was executed by the Human Genotyping Facility of the Genetic Laboratory of the Department of Internal Medicine, Erasmus MC, the Netherlands. We thank Mr. Michael Verbiest, Ms. Mila Jhamai, Ms. Sarah Higgins, Mr. Marijn Verkerk and Dr. Lisette Stolk for their help in creating the EWAS database. We thank Dr. A.Teumer for his work on the quality control and normalization scripts. Funding Information: ALSPAC The UK Medical Research Council (MRC) and Wellcome (Grant ref: 102215/2/13/2) and the University of Bristol provide core support for ALSPAC. This publication is the work of the authors and EW will serve as guarantors for the contents of this paper. A comprehensive list of grants funding is available on the ALSPAC website ( http://www.bristol.ac.uk/alspac/external/documents/grant-acknowledgements.pdf ). Methylation data in the ALSPAC cohort were generated as part of the UK BBSRC funded (BB/I025751/1 and BB/I025263/1) Accessible Resource for Integrated Epigenomic Studies (ARIES, http://www.ariesepigenomics.org.uk ). EW was partially funded by the Bath Institute for Mathematical Innovation. EW is also funded by the European Union’s Horizon 2020 research and innovation programme (grant nº 848158) and by CLOSER, whose mission is to maximise the use, value and impact of longitudinal studies. CLOSER was funded by the Economic and Social Research Council (ESRC) and the Medical Research Council (MRC) between 2012 and 2017. Its initial five year grant has since been extended to March 2021 by the ESRC (grant reference: ES/K000357/1). The funders took no role in the design, execution, analysis or interpretation of the data or in the writing up of the findings. www.closer.ac.uk . GENR The general design of the Generation R Study is made possible by financial support from Erasmus MC, University Medical Center Rotterdam, Erasmus University Rotterdam, the Netherlands Organization for Health Research and Development (ZonMw) and the Ministry of Health, Welfare and Sport. The EWAS data was funded by a grant from the Netherlands Genomics Initiative (NGI)/Netherlands Organisation for Scientific Research (NWO) Netherlands Consortium for Healthy Aging (NCHA; project nr. 050-060-810), by funds from the Genetic Laboratory of the Department of Internal Medicine, Erasmus MC, and by a grant from the National Institute of Child and Human Development (R01HD068437). A. Neumann and H. Tiemeier are supported by a grant of the Dutch Ministry of Education, Culture, and Science and the Netherlands Organization for Scientific Research (NWO grant No. 024.001.003, Consortium on Individual Development). A. Neumann is also supported by a Canadian Institutes of Health Research team grant. The work of H. Tiemeier is further supported by a NWO-VICI grant (NWO-ZonMW: 016.VICI.170.200). The work of CC has received funding from the European Union’s Horizon 2020 Research and Innovation Programme under the Marie Skłodowska-Curie grant agreement No 707404.This project received funding from the European Union’s Horizon 2020 research and innovation programme (733206, LifeCycle; 633595, DynaHEALTH; 848158, EarlyCause, 874739, LongITools)) and from the European Joint Programming Initiative “A Healthy Diet for a Healthy Life” (JPI HDHL, NutriPROGRAM project, ZonMw the Netherlands no.529051022 and PREcisE project ZonMw the Netherlands no.529051023).

PY - 2022/12/31

Y1 - 2022/12/31

N2 - BackgroundDNA methylation is an epigenetic mechanism involved in human development. Numerous epigenome-wide association studies (EWAS) have investigated the associations of DNA methylation at single CpG sites with childhood outcomes. However, the overall contribution of DNA methylation across the genome (R2Methylation) towards childhood phenotypes is unknown. An estimate of R2Methylation would provide context regarding the importance of DNA methylation explaining variance in health outcomes. We therefore estimated the variance explained by epigenome-wide cord blood methylation (R2Methylation) for five childhood phenotypes: gestational age, birth weight, and body mass index (BMI), IQ and ADHD symptoms at school age. We adapted a genome-based restricted maximum likelihood (GREML) approach with cross-validation (CV) to DNA methylation data and applied it in two population-based birth cohorts: ALSPAC (n = 775) and Generation R (n = 1382).ResultsUsing information from > 470,000 autosomal probes we estimated that DNA methylation at birth explains 32% (SDCV = 0.06) of gestational age variance and 5% (SDCV = 0.02) of birth weight variance. The R2Methylation estimates for BMI, IQ and ADHD symptoms at school age estimates were near 0% across almost all cross-validation iterations.ConclusionsThe results suggest that cord blood methylation explains a moderate degree of variance in gestational age and birth weight, in line with the success of previous EWAS in identifying numerous CpG sites associated with these phenotypes. In contrast, we could not obtain a reliable estimate for school-age BMI, IQ and ADHD symptoms. This may reflect a null bias due to insufficient sample size to detect variance explained in more weakly associated phenotypes, although the true R2Methylation for these phenotypes is likely below that of gestational age and birth weight when using DNA methylation at birth.

AB - BackgroundDNA methylation is an epigenetic mechanism involved in human development. Numerous epigenome-wide association studies (EWAS) have investigated the associations of DNA methylation at single CpG sites with childhood outcomes. However, the overall contribution of DNA methylation across the genome (R2Methylation) towards childhood phenotypes is unknown. An estimate of R2Methylation would provide context regarding the importance of DNA methylation explaining variance in health outcomes. We therefore estimated the variance explained by epigenome-wide cord blood methylation (R2Methylation) for five childhood phenotypes: gestational age, birth weight, and body mass index (BMI), IQ and ADHD symptoms at school age. We adapted a genome-based restricted maximum likelihood (GREML) approach with cross-validation (CV) to DNA methylation data and applied it in two population-based birth cohorts: ALSPAC (n = 775) and Generation R (n = 1382).ResultsUsing information from > 470,000 autosomal probes we estimated that DNA methylation at birth explains 32% (SDCV = 0.06) of gestational age variance and 5% (SDCV = 0.02) of birth weight variance. The R2Methylation estimates for BMI, IQ and ADHD symptoms at school age estimates were near 0% across almost all cross-validation iterations.ConclusionsThe results suggest that cord blood methylation explains a moderate degree of variance in gestational age and birth weight, in line with the success of previous EWAS in identifying numerous CpG sites associated with these phenotypes. In contrast, we could not obtain a reliable estimate for school-age BMI, IQ and ADHD symptoms. This may reflect a null bias due to insufficient sample size to detect variance explained in more weakly associated phenotypes, although the true R2Methylation for these phenotypes is likely below that of gestational age and birth weight when using DNA methylation at birth.

KW - ADHD

KW - BMI

KW - Birth weight

KW - Child development

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KW - Epigenetics

KW - GCTA

KW - GREML

KW - Gestational age

KW - IQ

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Epigenome-wide contributions to individual differences in childhood phenotypes: A GREML approach

Abstract

Bibliographical note

Keywords

ASJC Scopus subject areas

UN SDGs

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Other files and links

Fingerprint

EarlyCause

Cite this

Epigenome-wide contributions to individual differences in childhood phenotypes: A GREML approach

Abstract

Bibliographical note

Keywords

ASJC Scopus subject areas

UN SDGs

Access to Document

Other files and links

Fingerprint

Projects

EarlyCause

Cite this