The structural integrity of bone in any mechanical loading environment is an integrative function of a multitude of complex and interrelated characteristics of bone at the macro-, micro- and ultrastructural levels of bone's structural organization. Bone fragility and increased fracture risk result from multiple, distinct combinations of scores of bone traits. A dynamic process of co-adaptation of traits provides for redundant combinations of traits through which structures are produced that provide adequate functionality under """"""""normal"""""""" loading conditions. However, some of these combinations of traits can result in structures that are suboptimal when subjected to atypical or traumatic loads, such as those encountered in a fall. The dominant study design in skeletal genetics and biomechanics focuses on the role of one or a limited set of morphological and/or compositional factors in bone fragility. This approach is effective for identifying discrete traits that contribute to bone fracture resistance, but we cannot get a complete picture of the mechanobiological processes underlying fracture risk without a more comprehensive study design that captures variation at each of bone's hierarchical levels, since all of these traits work synergistically to control fracture risk. We propose a multi-disciplinary, integrative approach that is a major departure from traditional approaches to skeletal biomechanics and genetics. Our study is designed to identify composite traits comprising uncorrelated expression patterns of specific measures of bone quality and density that are linked to bone structural performance, to estimate the heritability (h2) of these composite traits, and to prioritize genes and gene networks most likely to affect fracture risk. Specifically, we aim to 1) measure a thorough suite of bone traits in the femurs of 100 pedigreed baboons, then use variable reduction methods to distill the multitude of interrelated, highly correlated traits down to a small set of uncorrelated descriptors of variation in bone morphology and composition. Hypothesis: There is a set of uncorrelated, composite traits that efficiently disentangles the elaborate network of compositional and morphological traits responsible for population-level normal variation in bone biomechanical behavior. 2) Characterize age and sex effects on these composite traits, 3) Assess femoral apparent biomechanical properties under normal and non-habitual loading conditions. Hypothesis: Differential expression of these traits in individuals results in structures that support normal functional musculoskeletal activities, a subset of which perform poorly when loaded in a non-habitual manner. 4) Detect and quantify the proportion of variation in each composite descriptor that is due to the additive effects of genes (h2), and 5) Identify genes and networks that are differentially active in bone tissue from strong for size vs. weak for size femurs. Such fundamental knowledge would allow for development of vastly improved preventative and therapeutic strategies for osteoporosis-related fractures.

Public Health Relevance

Osteoporosis is an age-related health problem of immediate public health concern that results in 1.5 million fractures in the U.S. each year. Great strides have been made in identifying a vast number of bone composition and shape traits that affect fracture risk, but traditional study designs limit our ability to understand how these traits work together to result in strong vs. weak bones. We will employ a more comprehensive study design that captures variation at each of bone's hierarchical levels to get a more complete picture of the mechanobiological processes underlying fracture risk, and to discover genes that mediate fracture risk. Such fundamental knowledge will speed development of vastly improved preventative and therapeutic strategies.

National Institute of Health (NIH)
National Institute of Arthritis and Musculoskeletal and Skin Diseases (NIAMS)
Research Project (R01)
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Skeletal Biology Structure and Regeneration Study Section (SBSR)
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Sharrock, William J
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Texas Biomedical Research Institute
San Antonio
United States
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Havill, L M; Coan, H B; Mahaney, M C et al. (2014) Characterization of complex, co-adapted skeletal biomechanics phenotypes: a needed paradigm shift in the genetics of bone structure and function. Curr Osteoporos Rep 12:174-80
Chan, Kwai S; Nicolella, Daniel P (2012) Micromechanical modeling of R-curve behaviors in human cortical bone. J Mech Behav Biomed Mater 16:136-52