High-throughput quantification of the mechanical competence of murine femora--a highly automated approach for large-scale genetic studies.

Document Type

Article

Publication Date

7-1-2013

Keywords

Animals, Automation, Biomechanical Phenomena, Crosses, Genetic, Elastic Modulus, Female, Femur, Finite Element Analysis, Linear Models, Male, Mice, Mice, Inbred C3H, Mice, Inbred C57BL, Phenotype, Quantitative Trait Loci, Reproducibility of Results, Weight-Bearing

JAX Source

Bone 2013 Jul; 55(1):216-21.

Volume

55

Issue

1

First Page

216

Last Page

221

ISSN

1873-2763

PMID

23486181

Abstract

Animal models are widely used to gain insight into the role of genetics on bone structure and function. One of the main strategies to map the genes regulating specific traits is called quantitative trait loci (QTL) analysis, which generally requires a very large number of animals (often more than 1000) to reach statistical significance. QTL analysis for mechanical traits has been mainly based on experimental mechanical testing, which, in view of the large number of animals, is time consuming. Hence, the goal of the present work was to introduce an automated method for large-scale high-throughput quantification of the mechanical properties of murine femora. Specifically, our aims were, first, to develop and validate an automated method to quantify murine femoral bone stiffness. Second, to test its high-throughput capabilities on murine femora from a large genetic study, more specifically, femora from two growth hormone (GH) deficient inbred strains of mice (B6-lit/lit and C3.B6-lit/lit) and their first (F1) and second (F2) filial offsprings. Automated routines were developed to convert micro-computed tomography (micro-CT) images of femora into micro-finite element (micro-FE) models. The method was experimentally validated on femora from C57BL/6J and C3H/HeJ mice: for both inbred strains the micro-FE models closely matched the experimentally measured bone stiffness when using a single tissue modulus of 13.06 GPa. The mechanical analysis of the entire dataset (n=1990) took approximately 44 CPU hours on a supercomputer. In conclusion, our approach, in combination with QTL analysis could help to locate genes directly involved in controlling bone mechanical competence. Bone 2013 Jul; 55(1):216-21.

Link to Full Text

Share

COinS