Osteoporosis is the most common multifactorial metabolic bone disorder worldwide with a strong genetic component. In this review, the evidence for a genetic contribution to osteoporosis and related phenotypes is summa...Osteoporosis is the most common multifactorial metabolic bone disorder worldwide with a strong genetic component. In this review, the evidence for a genetic contribution to osteoporosis and related phenotypes is summarized alongside with methods used to identify osteoporosis susceptibility genes. The key biological pathways involved in the skeleton and bone development are discussed with a particular focus on master genes clustered in these pathways and their mode of action. Furthermore, the most studied single nucleotide polymorphisms （SNPs） analyzed for their importance as genetic markers of the disease are presented. New data generated by next- generation sequencing in conjunction with extensive meta-analyses should contribute to a better understanding of the genetic basis of osteoporosis and related phenotype variability. These data could be ultimately used for identifying at-risk patients for disease prevention by both controlling environmental factors and providing possible therapeutic targets.展开更多
文摘Osteoporosis is the most common multifactorial metabolic bone disorder worldwide with a strong genetic component. In this review, the evidence for a genetic contribution to osteoporosis and related phenotypes is summarized alongside with methods used to identify osteoporosis susceptibility genes. The key biological pathways involved in the skeleton and bone development are discussed with a particular focus on master genes clustered in these pathways and their mode of action. Furthermore, the most studied single nucleotide polymorphisms （SNPs） analyzed for their importance as genetic markers of the disease are presented. New data generated by next- generation sequencing in conjunction with extensive meta-analyses should contribute to a better understanding of the genetic basis of osteoporosis and related phenotype variability. These data could be ultimately used for identifying at-risk patients for disease prevention by both controlling environmental factors and providing possible therapeutic targets.
