Godfred L. Masinde

Quantitative Trait Loci for Bone Density in Mice: The Genes Determining Total Skeletal Density and Femur Density Show Little Overlap in F2 Mice

Bone mineral density variation is a highly heritable trait and is the best predictor of skeletal fragility. Total skeletal density was determined by PIXIMUS™, and femur density was determined by pQCT. The data were analyzed for quantitative trait loci (QTL) to determine if bone density at a specific skeletal site (femur) would identify new gene loci or the same gene loci as total body (PIXIMUS™). In order to show concordance and differences in QTL for total body bone density versus femur bone density, we performed a genome-wide scan from 633 (MRL × SJL) F2 mice. The bone mineral density (BMD) data from pQCT were used to identify nine QTL on chromosomes 1, 3, 4, 9, 12, 17, and 18, while nine QTL on chromosomes 1, 2, 4, 9, 11, 14, and 15 were identified by PIXIMUS™ data, accounting for 32.5% and 30.4% variation in F2 mice, respectively. QTL on chromosomes 1, 2, 3, 9, 11, 12, 14, 15, 17, and 18 are unique to our study, as they have never been described before. Chromosome 1 (D1Mit33 and D1Mit362) had similar QTL between pQCT and PIXIMUS™. Several QTL were identified for both femur and total body BMD but only two QTL were common for both of these phenotypes. This suggests that genes regulating bone density differ depending on the skeletal site analyzed.

Genetic control of the rate of wound healing in mice

There have been few studies of the inheritance of wound healing in mammals. In this study, we demonstrate that inbred strains of mice differ significantly in the rate of wound healing. Of the 20 strains tested, fast healers (MRL/MpJ-Faslpr and LG/J) healed wounds four times faster than slow healers (Balb/cByJ and SJL/J). The genetic basis underlying the difference in the healing capacity was analysed using F2 populations of two different crosses. We show that the wound healing is a polygenically determined quantitative trait with an average estimated heritability of 86%. The modes of gene action in these two crosses are different. In the (MRL/MpJ × SJL/J) cross, genes regulating fast healing in MRL/MpJ mice exhibited additive effects, whereas these effects were suppressed by a dominant repressor gene in CBA/J mice in the (MRL/MpJ-Faslpr × CBA/J) cross. Information gained from this investigation provides insight into further study of molecular mechanisms underlying the rate of wound healing in mammals.

Quantitative trait loci for periosteal circumference (PC): identification of single loci and epistatic effects in F2 MRL/SJL mice

To test the hypothesis that periosteal circumference (PC), which is associated with bone size through cross-sectional moment of inertia (CMI), has heritable components, we performed a linkage analysis using 633 MRL/SJL F(2) mice that have 14% difference in mean PC. PC was determined in femurs by use of peripheral quantitative computerized tomography (pQCT). The genome-wide scan identified nine QTL for PC adjusted by body weight on chromosomes 1 (2 QTL), 2 (2 QTL), 8, 11, 15, 17, and X, which accounted for 38.6% of phenotype variance. QTL on chromosomes 1 (D1Mit33), 8 (D8Mit125), 15 (D15Mit 62), 17 (D17Mit176), and X (DXMit208) were unique for PC adjusted by body weight and femur length, while the remaining PC QTL were shared with body weight but not femur length. Four epistatic interactions were identified which accounted for 37.6% of phenotype variance. There was also evidence of pleiotropic effects on chromosome 11 among four size phenotypes (PC, body length, body weight, bone mineral density, and muscle size), which may represent a common genetic mechanism that may regulate bone size and body size.

Mapping the dominant wound healing and soft tissue regeneration QTL in MRL × CAST

We have used a mouse ear punch model and the QTL (quantitative trait loci) mapping technique to identify genes that are responsible for soft tissue regeneration. In the early studies, we have identified several QTL and have shown that the inheritance of ear healing was additive in one cross (MRL × SJL), and recessive in another cross (DBA × 129). Because CAST mice are genetically distinct and have a different genetic background, CAST would facilitate the identification of common and novel QTL when crossed with common inbred lines. We made a cross between super healer MRL and poor healer CAST and collected ear punch phenotype and marker genotype data from F2. Ear punch healing exhibited a dominant mode of inheritance in this cross. There were three main QTL on Chromosomes 4, 9, and 17, and two suggestive QTL on Chromosomes 1 (new) and 7. Taken together, these QTL accounted for about 29% of total F2 variance of MRL × CAST. Compared with another study using the same cross, we found a totally different set of QTL. Two QTL interactions were identified by a full QTL model: Chromosomes 4 × 17 and 9 × 17; the latter reached to a statistical level at p < 0.05. These interactions explained about 4% of the F2 phenotypic variance. We conclude that soft tissue regeneration is controlled by multiple genes and locus vs. locus interactions.

Chromosomal regions harboring genes for the work to femur failure in mice

Abstract. The work to failure is defined as the maximum energy bone can absorb before breaking, and therefore is a direct test of the risk of fracture. To determine the genetic loci influencing work to failure, we have performed a high density genome-wide scan in 633 (MRL × SJL) F2 female mice. Five loci (P <0.005) with significant effects on work to failure were found on chromosomes 2, 7, 8, 9, and X, which collectively explained around 20% variance of work to femur failure in F2 mice. Of those, only the QTL on chromosome 9 was concordant with bone mineral density (BMD) QTLs. Eight significant interactions (P <0.01) between marker loci were identified, which accounted for an equivalent amount of F2 variance (23%) to combined single QTL effects. Our results demonstrate that most of the genetic loci regulating work to failure are different from those for BMD in the 7-week-old female mice. If this is also true in humans, this finding will challenge the predictive value of BMD for the risk of fracture.

Quantitative trait loci (QTL) for lean body mass and body length in MRL/MPJ and SJL/J F2 mice

Abstract. Studies on the genetic mechanisms involved in the regulation of lean body mass (LBM) in mammals are minimal, although LBM is associated with a competent immune system and an overall good (healthy) body functional status. In this study, we performed a high-density genome-wide scan using 633 (MRL/MPJ × SJL/J) F2 intercross to identify the quantitative trait loci (QTL) involved in the regulation of LBM. We hypothesized that additional QTL can be identified using a different mouse cross (MRL/SJL cross). Ten QTL were identified for LBM on chromosomes (chrs) 2, 6, 7, 9,13 and 14. Of those ten, QTL on chrs 6, 7 and 14 were exclusive to LBM, while QTL on chrs 4 and 11 were exclusively body length. LBM QTL on chrs 2 and 9 overlap with those of size. Altogether, the ten LBM QTL explained 41.2% of phenotypic variance in F2 mice. Five significantly interacting loci that may be involved in the regulation of LBM were identified and accounted for 24.4% of phenotypic variance explained by the QTL. Five epistatic interactions, contributing 22.9% of phenotypic variance, were identified for body length. Interacting loci on chr 2 may influence LBM by regulating body length. Therefore, epistatic interactions as well as single QTL effects play an important role in the regulation of LBM.

Quantitative trait loci that harbor genes regulating muscle size in (MRL/MPJ x SJL/J) F(2) mice.

Abstract. The genetic mechanisms that determine muscle size have not been elucidated, even though it is a key musculoskeletal parameter that reflects muscle strength. In this study, we performed a high-density genome-wide scan using 633 (MRL/MPJ × SJL/J) F2 intercross 7-week-old mice to identify quantitative trait loci (QTL) involved in the determination of muscle size. Significant QTL were identified for muscle size and body length. Muscle size (adjusted by body length) QTL were identified on chromosomes 7, 9, 11, 14 (two QTL) and 17, which together explained 19.2% of phenotypic variance in F2 mice, while body length QTL were located on chromosome 2 (two QTL), 9, 11 and 17 which accounted for 28.3% of phenotypic variance in F2 mice. Three significant epistatic interactions between different QTL positions from muscle size and body length were identified (P <0.01) on chromosomes 2, 9, 14 and 17, which explained 16.1% of the variance in F2 mice.

Identification of quantitative trait loci that regulate obesity and serum lipid levels in MRL/MpJ × SJL/J inbred mice

The total body fat mass and serum concentration of total cholesterol, HDL cholesterol, and triglyceride (TG) differ between standard diet-fed female inbred mouse strains MRL/MpJ (MRL) and SJL/J (SJL) by 38–120% (P < 0.01). To investigate genetic regulation of obesity and serum lipid levels, we performed a genome-wide linkage analysis in 621 MRL× SJL F2 female mice. Fat mass was affected by two significant loci, D11Mit36 [43.7 cM, logarithm of the odds ratio (LOD) 11.2] and D16Mit51 (50.3 cM, LOD 3.9), and one suggestive locus at D7Mit44 (50 cM, LOD 2.4). TG levels were affected by two novel loci at D1Mit43 (76 cM, LOD 3.8) and D12Mit201 (26 cM, LOD 4.1), and two suggestive loci on chromosomes 5 and 17. HDL and cholesterol concentrations were influenced by significant loci on chromosomes 1, 3, 5, 7, and 17 that were in the regions identified earlier for other strains of mice, except for a suggestive locus on chromosome 14 that was specific to the MRL × SJL cross. In summary, linkage analysis in MRL × SJL F2 mice disclosed novel loci affecting TG, HDL, and fat mass, a measure of obesity. Knowledge of the genes in these quantitative trait loci will enhance our understanding of obesity and lipid metabolism.

Detecting novel bone density and bone size quantitative trait loci using a cross of MRL/MpJ and CAST/EiJ inbred mice.

Most previous studies to identify loci involved in bone mineral density (BMD) regulation have used inbred strains with high and low BMD in generating F2 mice. However, differences in BMD may not be a requirement in selecting parental strains for BMD quantitative trait loci (QTL) studies. In this study, we intended to identify novel QTL using a cross of two strains, MRL/MpJ (MRL) and CAST/EiJ (CAST), both of which exhibit relatively high BMD when compared to previously used strains. In addition, CAST was genetically distinct. We generated 328 MRL × CAST F2 mice of both sexes and measured femur BMD and periosteal circumference (PC) using peripheral quantitative computed tomography. Whole-genome genotyping was performed with 86 microsatellite markers. A new BMD QTL on chromosome 10 and another suggestive one on chromosome 15 were identified. A significant femur PC QTL identified on chromosome 9 and a suggestive one on chromosome 2 were similar to those detected in MRL × SJL. QTL were also identified for other femur and forearm bone density and bone size phenotypes, some of which were colocalized within the same chromosomal positions as those for femur BMD and femur PC. This study demonstrates the utility of crosses involving inbred strains of mice which exhibit a similar phenotype in QTL identification.

A critical evaluation of the effect of population size and phenotypic measurement on QTL detection and localization using a large F2 murine mapping population

Population size and phenotypic measurement are two key factors determining the detection power of quantitative trait loci (QTL) mapping. We evaluated how these two controllable factors quantitatively affect the detection of QTL and their localization using a large F2 murine mapping population and found that three main points emerged from this study. One finding was that the sensitivity of QTL detection significantly decreased as the population size decreased. The decrease in the percentage logarithm of the odd score (LOD score, which is a statistical measure of the likelihood of two loci being lied near each other on a chromosome) can be estimated using the formula 1 - n/N, where n is the smaller and N the larger population size. This empirical formula has several practical implications in QTL mapping. We also found that a population size of 300 seems to be a threshold for the detection of QTL and their localization, which challenges the small population sizes commonly-used in published studies, in excess of 60% of which cite population sizes <300. In addition, it seems that the precision of phenotypic measurement has a limited capacity to affect detection power, which means that quantitative traits that cannot be measured precisely can also be used in QTL mapping for the detection of major QTL.