Melissa Wake1,2,3, Richard Saffery1,2, Katherine Lange4,5, Jessica A Kerr1,2,6, Toby Mansell1,2, Justin M O'Sullivan3,7, David P Burgner1,2,8, Susan A Clifford1,2, Tim Olds1,9, Terence Dwyer1,10,11. 1. Murdoch Children's Research Institute, Parkville, VIC, Australia. 2. Department of Paediatrics, University of Melbourne, Parkville, VIC, Australia. 3. Liggins Institute, University of Auckland, Grafton, Auckland, New Zealand. 4. Murdoch Children's Research Institute, Parkville, VIC, Australia. katherine.lange@mcri.edu.au. 5. Department of Paediatrics, University of Melbourne, Parkville, VIC, Australia. katherine.lange@mcri.edu.au. 6. Department of Psychological Medicine, University of Otago, Christchurch, New Zealand. 7. MRC Lifecourse Epidemiology Unit, University of Southampton, Southampton, UK. 8. Department of Paediatrics, Monash University, Clayton, VIC, Australia. 9. Alliance for Research in Exercise, Nutrition and Activity (ARENA), University of South Australia, Adelaide, SA, Australia. 10. The George Institute for Global Health, University of Oxford, Oxford, UK. 11. Institute for Medical Research, University of Tasmania, Hobart, TAS, Australia.
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.
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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