Adjusting Data to Body Size: A Comparison of Methods as Applied to Quantitative Trait Loci Analysis of Musculoskeletal Phenotypes

Abstract

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 × DBA/2 (BXD) second generation (F 2 ) mice (n ‫ס‬ 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.

Bone microstructure and its associated genetic variability in 12 inbred mouse strains: μCT study and in silico genome scan

“…The association of chromosomal segments linked to both cortical bone parameters and weight (Table 2) is consistent with previous studies (Table 3) [24,30,33,51]. Body weight is one of the biggest intrinsic factors of bone strain [50], which is directly related to cortical area [33]. Based on these data, we postulate that the correlation between body weight and femur bone traits across strains may be at least partially due to skeletal loading.…”

“…There is considerable evidence that skeletal traits positively correlate with body size phenotypes [32,50,51]. We found a positive correlation between body weight and the midfemur cortical bone area (r 2 = 0.63) and distal femur trabecular BV/TV (r 2 = 0.45), but there was no correlation between body weight and trabecular BV/TV in the lumbar vertebra (Fig.…”

Choline Redistribution during Adaptation to Choline Deprivation

“…Total radioactivity in the whole tissues of brain, liver, intestine, kidney, heart, lung, and muscle, as well as plasma was measured. Total muscle weight was calculated according to the muscle/body weight index of mice (19). Total plasma volume was estimated as 2 ml in a 20-g mouse (20).…”

Fine Mapping Reveals Sex Bias in Quantitative Trait Loci Affecting Growth, Skeletal Size and Obesity-Related Traits on Mouse Chromosomes 2 and 11

“…FAT was highly correlated with 9WK in females but not in males (Figure 1), illustrating the sex-dependent relationship between these two traits. Several recent studies have suggested that due to the lack of equality in this relationship between the sexes (Stylianou et al 2006) and because of induced spurious correlation between the traits (Lang et al 2005), the use of ratios like adiposity index (fat pad mass/body weight 3 100) in QTL analysis may result in statistical artifacts. Therefore, to circumvent potential problems, we chose to adjust FAT (in the HG2DF 2 cross) and gonadal fat pad weight (GFP) (in the HG11F 2 cross) by including weight at sacrifice (WSAC) and WSAC 3 sex terms as additive covariates in both Model_A and Model_SI.…”