Adjusting data to body size: A comparison of methods as applied to quantitative trait loci analysis of musculoskeletal phenotypes

Dean H. Lang, Neil A. Sharkey, Arimantas Lionikas, Holly A. Mack, Lars Larsson, George P. Vogler, David J. Vandenbergh, David A. Blizard, Joseph T. Stout, Joseph P. Stitt, Gerald E. McClearn

Research output: Contribution to journalArticlepeer-review

42 Scopus citations


The aim of this study was to compare three methods of adjusting skeletal data for body size and examine their use in QTL analyses. It was found that dividing skeletal phenotypes by body mass index induced erroneous QTL results. The preferred method of body size adjustment was multiple regression. Introduction: Many skeletal studies have reported strong correlations between phenotypes for muscle, bone, and body size, and these correlations add to the difficulty in identifying genetic influence on skeletal traits that are not mediated through overall body size. Quantitative trait loci (QTL) identified for skeletal phenotypes often map to the same chromosome regions as QTLs for body size. The actions of a QTL identified as influencing BMD could therefore be mediated through the generalized actions of growth on body size or muscle mass. Materials and Methods: Three methods of adjusting skeletal phenotypes to body size were performed on morphologic, structural, and compositional measurements of the femur and tibia in 200-day-old C57BL/6J x DBA/2 (BXD) second generation (F2) mice (n = 400). A common method of removing the size effect has been through the use of ratios. This technique and two alternative techniques using simple and multiple regression were performed on muscle and skeletal data before QTL analyses, and the differences in QTL results were examined. Results and Conclusions: The use of ratios to remove the size effect was shown to increase the size effect by inducing spurious correlations, thereby leading to inaccurate QTL results. Adjustments for body size using multiple regression eliminated these problems. Multiple regression should be used to remove the variance of co-factors related to skeletal phenotypes to allow for the study of genetic influence independent of correlated phenotypes. However, to better understand the genetic influence, adjusted and unadjusted skeletal QTL results should be compared. Additional insight can be gained by observing the difference in LOD score between the adjusted and nonadjusted phenotypes. Identifying QTLs that exert their effects on skeletal phenotypes through body size-related pathways as well as those having a more direct and independent influence on bone are equally important in deciphering the complex physiologic pathways responsible for the maintenance of bone health.

Original languageEnglish (US)
Pages (from-to)748-757
Number of pages10
JournalJournal of Bone and Mineral Research
Issue number5
StatePublished - May 2005

All Science Journal Classification (ASJC) codes

  • Endocrinology, Diabetes and Metabolism
  • Orthopedics and Sports Medicine


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