44. Translational Genetics of Osteoporosis: From Population Association to Individualized Prognosis

  1. Clifford J. Rosen MD
  1. Bich H. Tran,
  2. Jacqueline R. Center and
  3. Tuan V. Nguyen

Published Online: 19 JUL 2013

DOI: 10.1002/9781118453926.ch44

Primer on the Metabolic Bone Diseases and Disorders of Mineral Metabolism, Eighth Edition

Primer on the Metabolic Bone Diseases and Disorders of Mineral Metabolism, Eighth Edition

How to Cite

Tran, B. H., Center, J. R. and Nguyen, T. V. (2013) Translational Genetics of Osteoporosis: From Population Association to Individualized Prognosis, in Primer on the Metabolic Bone Diseases and Disorders of Mineral Metabolism, Eighth Edition (ed C. J. Rosen), John Wiley & Sons, Inc., Ames, USA. doi: 10.1002/9781118453926.ch44

Publication History

  1. Published Online: 19 JUL 2013
  2. Published Print: 19 AUG 2013

ISBN Information

Print ISBN: 9781118453889

Online ISBN: 9781118453926

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Keywords:

  • genome-wide association (GWA) studies;
  • osteoporosis;
  • prognostic models;
  • single-nucleotide polymorphisms (SNPs)

Summary

Genetic factors play an important role in osteoporosis and fracture risk. The genetic studies performed over the past few decades have provided valuable insights into the pathophysiology of osteoporosis. With the decreasing costs of genome-wide scanning, consortia have been able to pool much larger populations in genome-wide association (GWA) studies for complex diseases that overcome some of the statistical problems that have plagued reproducibility from earlier studies in osteoporosis. A major priority in osteoporosis research at present is to develop prognostic models for identifying individuals who have a high risk of fracture. Research into the genetic background of osteoporosis and identification of osteoporosis-related genes can advance our understanding of pathophysiologic mechanisms of fracture. With a rapid improvement in genotyping technology, the next generation of GWA studies will be adding more variants at a low frequency to cover as many single-nucleotide polymorphisms (SNPs) as possible.