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Characterization of the genetic architecture of infant and early childhood body mass index

Nature Metabolism volume 4, pages 344–358 (2022)Cite this article

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An Author Correction to this article was published on 09 February 2024

This article has been updated

Abstract

Early childhood obesity is a growing global concern; however, the role of common genetic variation on infant and child weight development is unclear. Here, we identify 46 loci associated with early childhood body mass index at specific ages, matching different child growth phases, and representing four major trajectory patterns. We perform genome-wide association studies across 12 time points from birth to 8 years in 28,681 children and their parents (27,088 mothers and 26,239 fathers) in the Norwegian Mother, Father and Child Cohort Study. Monogenic obesity genes are overrepresented near identified loci, and several complex association signals near LEPR, GLP1R, PCSK1 and KLF14 point towards a major influence for common variation affecting the leptin–melanocortin system in early life, providing a link to putative treatment strategies. We also demonstrate how different polygenic risk scores transition from birth to adult profiles through early child growth. In conclusion, our results offer a fine-grained characterization of a changing genetic landscape sustaining early childhood growth.

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Fig. 1: Longitudinal association effect size profiles for the 46 top hits.
Fig. 2: Comparison with previous studies on birth weight and adult BMI.
Fig. 3: MoBa effect trajectories overlayed with association profiles obtained from ALSPAC.
Fig. 4: Trio-resolved and haplotype-resolved association profiles.
Fig. 5: Polygenic risk score analyses.

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Data availability

The full GWAS summary statistics for all time points are available at https://www.fhi.no/en/studies/moba/for-forskere-artikler/gwas-data-from-moba/. Access to genotypes and phenotypes from MoBa is subject to controlled access by the Norwegian Institute of Public Health in accordance with national and international regulations. Conditions of access including contact details for requests can be found at the Norwegian Institute of Public Health website (https://www.fhi.no/en/studies/moba/).

HRC or 1000G Imputation preparation and checking: https://www.well.ox.ac.uk/~wrayner/tools/.

Sanger imputation service: https://imputation.sanger.ac.uk/.

LD score repository: https://alkesgroup.broadinstitute.org/LDSCORE/.

Genotype-Tissue Expression: https://www.gtexportal.org/.

Birth weight reference data29: http://egg-consortium.org/BW5/Fetal_BW_European_meta.NG2019.txt.gz.

Adult BMI reference data15: http://portals.broadinstitute.org/collaboration/giant/images/c/c8/Meta-analysis_Locke_et_al%2BUKBiobank_2018_UPDATED.txt.gz.

T2D67: https://www.diagram-consortium.org/downloads.html.

• T2D GWAS meta-analysis–unadjusted for BMI67.

• T2D GWAS meta-analysis–adjusted for BMI67.

Childhood obesity18: http://egg-consortium.org/Childhood_Obesity_2019/CHILDHOOD_OBESITY.TRANS_ANCESTRAL.RESULTS.txt.gz.

Childhood BMI16: http://egg-consortium.org/Childhood_BMI/EGG_BMI_HapMap_DISCOVERY.txt.gz.

ALSPAC data dictionary and variable search tool: http://www.bristol.ac.uk/alspac/researchers/our-data/.

Change history

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Acknowledgements

This work was supported by grants (to S.J.) from Helse Vest’s Open Research (nos. 912250 and F-12144), the Novo Nordisk Foundation (NNF19OC0057445) and the Research Council of Norway (no. 315599)); and (to P.R.N.) from the European Research Council (AdG SELECTionPREDISPOSED no. 293574), the Bergen Research Foundation (Utilizing the Mother and Child Cohort and the Medical Birth Registry for Better Health), Stiftelsen Kristian Gerhard Jebsen (Translational Medical Center), the University of Bergen, the Research Council of Norway (FRIPRO no. 240413), the Western Norway Regional Health Authority (Strategic Fund Personalized Medicine for Children and Adults), the Novo Nordisk Foundation (no. 54741) and the Norwegian Diabetes Association. This work was partly supported by the Research Council of Norway through its Centres of Excellence funding scheme (nos. 262700 and 223273), Better Health by Harvesting Biobanks (no. 229624) and The Swedish Research Council, Stockholm, Sweden (2015-02559), the Research Council of Norway (FRIMEDBIO nos. 547711 and 273291) and March of Dimes (no. 21-FY16-121). MoBa is supported by the Norwegian Ministry of Health and Care Services and the Ministry of Education and Research, National Institutes of Health (NIH)/NIEHS (contract no. N01-ES-75558), NIH/NINDS (grant nos. UO1 NS 047537-01 and UO1 NS 047537-06A1).

We are grateful to all the families in Norway who are taking part in the ongoing MoBa cohort study.

We are extremely grateful to all the families who took part in the ALSPAC cohort 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. The UK Medical Research Council (MRC) and Wellcome (grant ref. 217065/Z/19/Z) and the University of Bristol provide core support for ALSPAC. This publication is the work of the authors and S.J and M.V. serve as guarantors for the contents of this paper. A comprehensive list of grant funding is available on the ALSPAC website (http://www.bristol.ac.uk/alspac/external/documents/grant-acknowledgements.pdf); this research was specifically funded by Wellcome Trust and the MRC (core; 076467/Z/05/Z). ALSPAC GWAS data were generated by sample logistics and genotyping facilities at Wellcome Sanger Institute and LabCorp (Laboratory Corporation of America) using support from 23andMe.

All analyses were performed using digital laboratories in HUNT Cloud at the Norwegian University of Science and Technology, Trondheim, Norway. We are grateful for outstanding support from the HUNT Cloud community.

Author information

Author notes

  1. These authors contributed equally: Øyvind Helgeland, Marc Vaudel.

Authors and Affiliations

  1. Center for Diabetes Research, Department of Clinical Science, University of Bergen, Bergen, Norway

    Øyvind Helgeland, Marc Vaudel, Ingvild L. Koløen, Bente B. Johansson, Pål R. Njølstad & Stefan Johansson

  2. Department of Genetics and Bioinformatics, Health Data and Digitalization, Norwegian Institute of Public Health, Oslo, Norway

    Øyvind Helgeland & Bo Jacobsson

  3. Department of Obstetrics and Gynecology, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden

    Pol Sole-Navais, Christopher Flatley, Julius Juodakis, Jonas Bacelis & Bo Jacobsson

  4. Department of Medical Genetics, Haukeland University Hospital, Bergen, Norway

    Ingvild L. Koløen & Stefan Johansson

  5. Norwegian Institute of Public Health, Oslo, Norway

    Gun Peggy Knudsen & Camilla Stoltenberg

  6. Centre for Fertility and Health, Norwegian Institute of Public Health, Oslo, Norway

    Per Magnus

  7. Department of Mental Disorders, Norwegian Institute of Public Health, Oslo, Norway

    Ted Reichborn Kjennerud

  8. Institute of Clinical Medicine, University of Oslo, Oslo, Norway

    Ted Reichborn Kjennerud

  9. Department of Health Registry Research and Development, National Institute of Public Health, Bergen, Norway

    Petur B. Juliusson

  10. Department of Clinical Science, University of Bergen, Bergen, Norway

    Petur B. Juliusson

  11. Children and Youth Clinic, Haukeland University Hospital, Bergen, Norway

    Petur B. Juliusson & Pål R. Njølstad

  12. HUNT Research Centre, Department of Public Health and Nursing, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway

    Oddgeir L. Holmen

  13. NORMENT Centre, Institute of Clinical Medicine, University of Oslo, Oslo, Norway

    Ole A. Andreassen

  14. Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway

    Ole A. Andreassen

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  1. Øyvind Helgeland
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Contributions

Ø.H., M.V., P.R.N. and S.J. designed the study. Ø.H. and M.V. analysed the data. Ø.H., M.V. and S.J. interpreted the data. J.J., J.B., G.P.K., T.R.K., P.M., C.S. and O.A.A. contributed to sample acquisition and genotyping. J.J. and J.B. assisted with genotype QC. P.S.-N., C.F., I.L.K., B.B.J., B.J. and P.R.N. critically revised the manuscript for important intellectual content. Ø.H., M.V. and S.J. wrote the manuscript. All authors participated in preparing the manuscript by reading and commenting on drafts before submission. P.R.N. and S.J. acquired the funding.

Corresponding authors

Correspondence to Pål R. Njølstad or Stefan Johansson.

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Competing interests

O.A.A. is a consultant to HealthLytix. The other authors declare no competing interests.

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Peer review information

Nature Metabolism thanks Timothy Frayling and the other, anonymous, reviewers for their contribution to the peer review of this work. Primary Handling Editor: Isabella Samuelson, in collaboration with the Nature Metabolism team.

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Extended data

Extended Data Fig. 1 Overlay with association profiles obtained from ALSPAC.

Effect size estimates for all 46 hits obtained in the MoBa and ALSPAC cohorts. The quadrant plots to the left display the shape of the effect size estimate over time as obtained in Fig. 1B, for both cohorts, between birth and eight years of age. The effect size estimates are plotted at each age to the right using line and ribbons for MoBa and point and error bars for ALSPAC. Note that to maintain readability of earlier time points, the scale of the x axis is not linear. Thick and thin error bars/ribbons represent one standard error estimate on each side of the effect size estimates and 95% confidence intervals, respectively. See Supplementary Table 1 for the number of samples at each time point.

Extended Data Fig. 2 SNP-based heritability.

H2 estimates from LD score regression for BMI plotted at each time point (black) along with locally estimated scatterplot smoothing (LOESS) local regression (in blue). Error bars represent ±SEM. See Supplementary Table 1 for the number of samples at each time point.

Extended Data Fig. 3 LD-score regression.

Genetic correlation estimate, rg, of selected traits with early growth BMI at birth, 6 weeks, 3, 6, 8 months, and 1, 1.5, 2, 3, 5, 7, and 8 years of age. Ribbons represent one standard error estimate on each side of the rg estimate. See methods for details and Supplementary Table 7 for correlation with other traits. See Supplementary Table 1 for the number of samples at each time point.

Extended Data Fig. 4 Comparison with previous studies on birth weight and adult BMI for the variants near monogenic obesity genes.

Heatmap of the effect size for the none top hits near monogenic diabetes genes (see Supplementary Table 2) from birth to adulthood obtained similarly as for Fig. 2A. Note that, in contrast to Fig. 2A, the name of the nearest monogenic obesity gene is used on the y axis, and not the locus name. See Supplementary Table 1 for the number of samples at each time point.

Extended Data Fig. 5 Loci with multiple independent associations signals.

Effect size estimates with child BMI from birth to eight years of age for the lead SNPs of the signals near (A) LEPR, (B) GLP1R, and (C) PCSK1. Dark and light Ribbons represent one standard error estimate on each side of the effect size estimate and 95% confidence intervals, respectively. For each SNP, regional plots are displayed at the age at peak association, highlighting the lead SNPs with red diamonds and SNPs coloured according to the LD R2, with the exons of the gene according to Ensembl at the bottom and recombination rates in blue. See Supplementary Table 1 for the number of samples at each time point, the methods for the statistical analysis.

Extended Data Fig. 6 Polygenic risk score (PRS) analyses.

A-D) Mean standardized BMI of children in this study at each time point after stratification in PRS deciles using PRS trained using summary statistics from meta-analyses (bottom), and share of obese children at a given time point in the top PRS decile (top), where obesity is defined as belonging to the top 5 BMI percentile. PRS training was performed using summary statistics for (A) birth weight from Warrington et al29. (B) childhood obesity from Bradfield et al18. (C) childhood BMI from Felix et al16. (D) adult BMI from Yengo et al15. (E) Mean standardized BMI for the children in this study falling in the top and bottom deciles of type 2 Diabetes (T2D) risk scores at each time point. PRS for T2D and T2D adjusted for BMI, represented in dashed and solid lines, respectively, were trained using summary statistics from Mahajan et al67. (F) R2 estimated at each time point when training the PRS for Birth weight, childhood obesity, childhood BMI, adult BMI, and T2D in Fig. 5A-E. (G) Mean standardized BMI of children in MoBa that were kept out of the discovery sample falling in the top and bottom quintiles of time-resolved early growth PRSs trained using summary statistics of this study at each time point (solid lines) and of the adult BMI PRS of Fig. 1D (dashed line) (Bottom), along with the respective R2 estimated when training the PRSs (Top). (H) Mean standardized BMI of children in MoBa that were kept out of the discovery sample falling in the bottom, intermediate, and top quintiles of time-resolved early growth PRSs trained using summary statistics of this study at each time point, in blue, black, and red, respectively. At each time point, rectangles represent one standard error estimate on each side of the mean estimate. Transitions between time points represent the share of children moving from one quintile category to the other. For each time point, mean BMI estimates for these children after stratification in quintiles are plotted for time-resolved early growth PRSs against the adult BMI PRS of Fig. 1D in inserts. All error bars/ribbons represent one standard error estimate on each side of the mean estimate. See Supplementary Table 1 for the number of samples at each time point.

Extended Data Fig. 7 Growth curves processing.

Length and Weight curves were inspected for outliers and missing values were imputed. This process was repeated until no value was changed. Then length values were inspected for negative growth and adjusted. The entire process was repeated until no value was changed.

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Helgeland, Ø., Vaudel, M., Sole-Navais, P. et al. Characterization of the genetic architecture of infant and early childhood body mass index. Nat Metab 4, 344–358 (2022). https://doi.org/10.1038/s42255-022-00549-1

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