bioRxivGenomicsPreprint
Genetic architecture of early childhood growth phenotypes gives insights into their link with later obesity
N. Maneka G. De Silva, Sylvain P SebertiD, Alexessander Couto AlvesiD, Ulla SovioiD, Shikta Das, Hendrik Rob TaaliD, Nicole M. WarringtoniD, Alex LewiniD, Marika KaakineniD, Diana L. CousmineriD, Elisabeth ThieringiD, Nicholas John TimpsoniD, Ville KarhuneniD, Tom A. BondiD, Xavier EstivilliD, Virpi LindiiD, Jonathan P. Bradfield, Frank GelleriD, Lachlan CoiniD, Marie Chiew Shia LohiD, Sheila J. BartoniD, Lawrence Joseph BeiliniD, Hans Flinker BisgaardiD, Klaus BønnelykkeiD, Rohia AliliiD, , Marjo‐Riitta JärveliniD
Abstract
Early childhood growth patterns are associated with adult metabolic health, but the underlying mechanisms are unclear. We performed genome-wide meta-analyses and follow-up in up to 22,769 European children for six early growth phenotypes derived from longitudinal data: peak height and weight velocities, age and body mass index (BMI) at adiposity peak (AP ~9 months) and rebound (AR ~5-6 years). We identified four associated loci (P< 5x10-8): LEPR/LEPROT with BMI at AP, FTO and TFAP2B with Age at AR and GNPDA2 with BMI at AR. The observed AR-associated SNPs at FTO, TFAP2B and GNPDA2 represent known adult BMI-associated variants. The common BMI at AP associated variant at LEPR/LEPROT was not associated with adult BMI but was associated with LEPROT gene expression levels, especially in subcutaneous fat (P<2x10-51). We identify strong positive genetic correlations between early growth and later adiposity traits, and analysis of the full discovery stage results for Age at AR revealed enrichment for insulin-like growth factor 1 (IGF-1) signaling and apolipoprotein pathways. This genome-wide association study suggests mechanistic links between early childhood growth and adiposity in later childhood and adulthood, highlighting these early growth phenotypes as potential targets for the prevention of obesity.
- organism: human
- assay: GWAS summary statistics
- research focus: GWAS / association mapping
- approach: statistical model
- Citations
- 1
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