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Can adult polygenic scores improve prediction of body mass index in childhood?

International Journal of Obesity volume 46, pages 1375–1383 (2022)Cite this article

Subjects

Abstract

Background/objectives

Modelling genetic pre-disposition may identify children at risk of obesity. However, most polygenic scores (PGSs) have been derived in adults, and lack validation during childhood. This study compared the utility of existing large-scale adult-derived PGSs to predict common anthropometric traits (body mass index (BMI), waist circumference, and body fat) in children and adults, and examined whether childhood BMI prediction could be improved by combining PGSs and non-genetic factors (maternal and earlier child BMI).

Subjects/methods

Participants (n = 1365 children, and n = 2094 adults made up of their parents) were drawn from the Longitudinal Study of Australian Children. Children were weighed and measured every two years from 0–1 to 12–13 years, and adults were measured or self-reported measurements were obtained concurrently (average analysed). Participants were genotyped from blood or oral samples, and PGSs were derived based on published genome-wide association studies. We used linear regression to compare the relative utility of these PGSs to predict their respective traits at different ages.

Results

BMI PGSs explained up to 12% of child BMI z-score variance in 10–13 year olds, compared with up to 15% in adults. PGSs for waist circumference and body fat explained less variance (up to 8%). An interaction between BMI PGSs and puberty (p = 0.001–0.002) suggests the effect of some variants may differ across the life course. Individual BMI measures across childhood predicted 10–60% of the variance in BMI at 12–13 years, and maternal BMI and BMI PGS each added 1–9% above this.

Conclusion

Adult-derived PGSs for BMI, particularly those derived by modelling between-variant interactions, may be useful for predicting BMI during adolescence with similar accuracy to that obtained in adulthood. The level of precision presented here to predict BMI during childhood may be relevant to public health, but is likely to be less useful for individual clinical purposes.

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Fig. 1: Participant flow chart and sample sizes from original LSAC B-cohort to genetic analysis sample.
Fig. 2: Prediction of BMI at different ages in childhood and their adult parents.
Fig. 3: Percentage of variance explained (R2) in BMI z-score at 12–13 years for multi-variate linear regression models of predictors measured every 2 years from birth.

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Data availability

Dataset and technical documents available from Growing Up in Australia: The Longitudinal Study of Australian Children via low-cost license for bone fide researchers. More information is available at www.growingupinaustralia.gov.au. Genetic data can be accessed by contacting the authors.

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Acknowledgements

This paper uses unit record data from Growing Up in Australia, the Longitudinal Study of Australian Children. The study is conducted in partnership between the Department of Social Services (DSS), AIFS and the Australian Bureau of Statistics (ABS). The findings and views reported in this paper are those of the author and should not be attributed to DSS, AIFS or the ABS. REDCap (Research Electronic Data Capture) electronic data capture tools were used in this study. More information about this software can be found at: www.project-redcap.org.

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Author notes

  1. These authors contributed equally: Melissa Wake, Richard Saffery.

Authors and Affiliations

  1. Murdoch Children’s Research Institute, Parkville, VIC, Australia

    Katherine Lange, Jessica A. Kerr, Toby Mansell, David P. Burgner, Susan A. Clifford, Tim Olds, Terence Dwyer, Melissa Wake & Richard Saffery

  2. Department of Paediatrics, University of Melbourne, Parkville, VIC, Australia

    Katherine Lange, Jessica A. Kerr, Toby Mansell, David P. Burgner, Susan A. Clifford, Melissa Wake & Richard Saffery

  3. Department of Psychological Medicine, University of Otago, Christchurch, New Zealand

    Jessica A. Kerr

  4. Liggins Institute, University of Auckland, Grafton, Auckland, New Zealand

    Justin M. O’Sullivan & Melissa Wake

  5. MRC Lifecourse Epidemiology Unit, University of Southampton, Southampton, UK

    Justin M. O’Sullivan

  6. Department of Paediatrics, Monash University, Clayton, VIC, Australia

    David P. Burgner

  7. Alliance for Research in Exercise, Nutrition and Activity (ARENA), University of South Australia, Adelaide, SA, Australia

    Tim Olds

  8. The George Institute for Global Health, University of Oxford, Oxford, UK

    Terence Dwyer

  9. Institute for Medical Research, University of Tasmania, Hobart, TAS, Australia

    Terence Dwyer

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Contributions

KL was involved in conceptual design, data acquisition, all analyses, interpretation of results, paper preparation, and has had full access to the data in the study and final responsibility for the decision to submit for publication. JK, DB, MW and RS were involved in conceptual design, data acquisition, interpretation of results, and revising the paper. TM was involved in conceptual design, interpretation of results, and revising the paper. JOS, SC, TO and TD were involved in data acquisition, interpretation of results, and revising the paper. All authors have approved the final version of the paper, and agreed to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

Corresponding author

Correspondence to Katherine Lange.

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Competing interests

This work was supported by the National Health and Medical Research Council (NHMRC) of Australia [1041352, 1109355]; the Royal Children’s Hospital Foundation [2014–241]; the Murdoch Children’s Research Institute (MCRI); The University of Melbourne, the National Heart Foundation of Australia [100660]; Financial Markets Foundation for Children [2014–055, 2016–310]; the Victoria Deaf Education Institute; and MBIE Catalyst grant (The New Zealand-Australia Life Course Collaboration on Genes, Environment, Nutrition and Obesity (GENO); UOAX1611; to JOS). The following were supported by NHMRC: Senior Research Fellowships MW [1046518] and DPB [1064629]; Principal Research Fellowship MW [1160906]; Investigator Grant DPB [1175744]. The following was supported by the Royal Children’s Hospital Foundation: Postdoctoral Fellowship KL, JK [2018–984]. Research at the MCRI is supported by the Victorian Government’s Operational Infrastructure Support Program.

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Lange, K., Kerr, J.A., Mansell, T. et al. Can adult polygenic scores improve prediction of body mass index in childhood?. Int J Obes 46, 1375–1383 (2022). https://doi.org/10.1038/s41366-022-01130-2

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