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Genetic Variants in Adult Bone Mineral Density and Fracture Risk Genes Are Associated with the Rate of Bone Mineral Density Acquisition in Adolescence

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Journal Hum Mol Genet
Date 2015 May 6
PMID 25941325
Citations 19
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Abstract

Previous studies have identified 63 single-nucleotide polymorphisms (SNPs) associated with bone mineral density (BMD) in adults. These SNPs are thought to reflect variants that influence bone maintenance and/or loss in adults. It is unclear whether they affect the rate of bone acquisition during adolescence. Bone measurements and genetic data were available on 6397 individuals from the Avon Longitudinal Study of Parents and Children at up to five follow-up clinics. Linear mixed effects models with smoothing splines were used for longitudinal modelling of BMD and its components bone mineral content (BMC) and bone area (BA), from 9 to 17 years. Genotype data from the 63 adult BMD associated SNPs were investigated individually and as a genetic risk score in the longitudinal model. Each additional BMD lowering allele of the genetic risk score was associated with lower BMD at age 13 [per allele effect size, 0.002 g/cm(2) (SE = 0.0001, P = 1.24 × 10(-38))] and decreased BMD acquisition from 9 to 17 years (P = 9.17 × 10(-7)). This association was driven by changes in BMC rather than BA. The genetic risk score explained ∼2% of the variation in BMD at 9 and 17 years, a third of that explained in adults (6%). Genetic variants that putatively affect bone maintenance and/or loss in adults appear to have a small influence on the rate of bone acquisition through adolescence.

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References
1.
Duncan E, Danoy P, Kemp J, Leo P, McCloskey E, Nicholson G . Genome-wide association study using extreme truncate selection identifies novel genes affecting bone mineral density and fracture risk. PLoS Genet. 2011; 7(4):e1001372. PMC: 3080863. DOI: 10.1371/journal.pgen.1001372. View

2.
Tobias J, Steer C, Vilarino-Guell C, Brown M . Estrogen receptor alpha regulates area-adjusted bone mineral content in late pubertal girls. J Clin Endocrinol Metab. 2006; 92(2):641-7. DOI: 10.1210/jc.2006-1555. View

3.
Macdonald-Wallis C, Lawlor D, Palmer T, Tilling K . Multivariate multilevel spline models for parallel growth processes: application to weight and mean arterial pressure in pregnancy. Stat Med. 2012; 31(26):3147-64. PMC: 3569877. DOI: 10.1002/sim.5385. View

4.
Matkovic V, Jelic T, Wardlaw G, Ilich J, Goel P, Wright J . Timing of peak bone mass in Caucasian females and its implication for the prevention of osteoporosis. Inference from a cross-sectional model. J Clin Invest. 1994; 93(2):799-808. PMC: 293933. DOI: 10.1172/JCI117034. View

5.
Cheng J, Edwards L, Maldonado-Molina M, Komro K, Muller K . Real longitudinal data analysis for real people: building a good enough mixed model. Stat Med. 2009; 29(4):504-20. PMC: 2811235. DOI: 10.1002/sim.3775. View