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Genetic architecture of human thinness compared to severe obesity

Authors
  • Farooqi, Ismaa
  • Riveros-McKay, Fernando
  • Mistry, Vanisha
  • Bounds, Rebecca
  • Hendricks, Audrey
  • Keogh, Julia M
  • Thomas, Hannah
  • Henning, Elana
  • Corbin, Laura J
  • O'Rahilly, Stephen
  • Zeggini, Eleftheria
  • Wheeler, Eleanor
  • Barroso, Maria
Publication Date
Jan 30, 2019
Source
Apollo - University of Cambridge Repository
Keywords
License
Green
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Abstract

The variation in weight within a shared environment is largely attributable to genetic factors. Whilst many genes/loci confer susceptibility to obesity, little is known about the genetic architecture of healthy thinness. Here, we characterise the heritability of thinness which we found was comparable to that of severe obesity (h2=28.07 vs 32.33% respectively), although with incomplete genetic overlap (r=-0.49, 95% CI [-0.17, -0.82], p=0.003). In a genome-wide association analysis of thinness (n=1,471) vs severe obesity (n=1,456), we identified 10 loci previously associated with obesity, and demonstrate enrichment for established BMI-associated loci (pbinomial=3.05x10-5). Simulation analyses showed that different association results between the extremes were likely in agreement with additive effects across the BMI distribution, suggesting different effects on thinness and obesity could be due to their different degrees of extremeness. In further analyses, we detected a novel obesity and BMI-associated locus at PKHD1 (rs2784243, obese vs. thin p=5.99x10-6, obese vs. controls p=2.13x10-6 pBMI=2.3x10-13), associations at loci recently discovered with much larger sample sizes (e.g. FAM150B and PRDM6-CEP120), and novel variants driving associations at previously established signals (e.g. rs205262 at the SNRPC/C6orf106 locus and rs112446794 at the PRDM6-CEP120 locus). Our ability to replicate loci found with much larger sample sizes demonstrates the value of clinical extremes and suggest that characterisation of the genetics of thinness may provide a more nuanced understanding of the genetic architecture of body weight regulation and may inform the identification of potential anti-obesity targets.

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