Polygenic Prediction of Weight and Obesity Trajectories from Birth to Adulthood

Amit V. Khera*, Mark Chaffin, Kaitlin H. Wade, Sohail Zahid, Joseph Brancale, Rui Xia, Marina Distefano, Ozlem Senol-Cosar, Mary E. Haas, Alexander Bick, Krishna G. Aragam, Eric S. Lander, George Davey Smith, Heather Mason-Suares, Myriam Fornage, Matthew Lebo, Nicholas J. Timpson, Lee M. Kaplan, Sekar Kathiresan

*Corresponding author for this work

Research output: Contribution to journalArticle (Academic Journal)peer-review

162 Citations (Scopus)
43 Downloads (Pure)

Abstract

Severe obesity is a rapidly growing global health threat. Although often attributed to unhealthy lifestyle choices or environmental factors, obesity is known to be heritable and highly polygenic – the majority of inherited susceptibility is related to the cumulative impact of many common DNA variants. Here, we derive and validate a new polygenic predictor comprised of 2.1 million common variants to quantify this susceptibility and test this predictor in >300,000 individuals ranging from middle age to birth. Among middle-aged adults, we observe a 13 kg gradient in weight and a 25-fold gradient in risk of severe obesity across polygenic score deciles. In a longitudinal birth cohort, we note minimal difference in birthweight across score deciles, but a significant gradient emerged in early childhood and reached 12 kg by age 18 years. This new approach to quantify inherited susceptibility to obesity using affords new opportunities for clinical prevention and mechanistic assessment.
Original languageEnglish
Pages (from-to)587-596.e9
Number of pages20
JournalCell
Volume177
Issue number3
Early online date18 Apr 2019
DOIs
Publication statusPublished - 18 Apr 2019

Structured keywords

  • ALSPAC

Keywords

  • polygenic score
  • human genetics
  • genomic medicine
  • genetic risk prediction
  • melanocortin 4 receptor
  • severe obesity
  • weight
  • body mass index
  • UK Biobank

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