Genetic determinants of BMI from early childhood to adolescence: the Santiago Longitudinal Study. Issue 3 (4th December 2018)

• Record Type: Journal Article
• Authors: Justice, A.E.; Chittoor, G.; Blanco, E.; Graff, M.; Wang, Y.; Albala, C.; Santos, J.L.; Angel, B.; Lozoff, B.; Voruganti, V.S.; North, K.E.; Gahagan, S.
• Is Part Of: Pediatric obesity. Volume 14:Issue 3(2019)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2018-12-04
• Subjects: BMI -- genetic risk score -- Latin America -- obesity; Obesity in children -- Periodicals; Obesity in adolescence -- Periodicals; Obesity -- Periodicals; Overweight children -- Periodicals; 618.92398
• DOI: 10.1111/ijpo.12479
• Languages: English
• ISSNs: 1747-7174

Abstract

Summary: Background: While the genetic contribution to obesity is well established, few studies have examined how genetic variants influence standardized body mass index Z ‐score (BMIz) in Hispanics/Latinos, especially across childhood and adolescence. Objectives: We estimated the effect of established BMIz loci in Chilean children of the Santiago Longitudinal Study (SLS). Methods: We examined associations with BMIz at age 10 for 15 loci previously identified in European children. For significant loci, we performed association analyses at ages 5 and 16 years, for which we have smaller sample sizes. We tested associations of unweighted genetic risk scores (GRSs) for previously identified tag variants (GRS_EUR) and from the most significant variants in SLS at each locus (GRS_SLS). Results: We generalized five variants at age 10 ( P < 0.05 and directionally consistent), including rs543874 that reached Bonferroni‐corrected significance. The effect on BMIz was greatest at age 10 for all significant loci, except FTO, which exhibited an increase in effect from ages 5 to 16. Both GRSs were associated with BMIz ( P < 0.0001), but GRS_SLS explained a much greater proportion of the variation (13.63%). Conclusion: Our results underscore the importance of conducting genetic investigations across life stages and selecting ancestry appropriate tag variants in future studies for disease prediction and clinical evaluation.