Abstract

We herein report the genetic association between leg problems and bone quality traits in a random mating broiler control population. The leg problem traits were valgus (VL), varus (VR), and tibial dyschondroplasia (TD), and that of bone quality were shank weight (SW), shank length (SL), shank diameter (SDIAM), tibia weight (TW), tibia length (TL), tibia diameter (TDIAM), tibia density (TDEN), tibia breaking strength (TBS), tibia mineral density (TMD), tibia mineral content (TMC), and tibia ash content (TAC). A threshold-linear mixed model, implemented via a Bayesian approach, was employed for the joint analysis of the traits. Genetic correlations of leg problems with bone quality traits ranged from −0.06 to 0.11 suggesting that genetic relationship between leg problems and quality is weak, and management strategies could better alleviate leg problems than genetic improvement.

INTRODUCTION

Genetic selection for broiler traits has made relevant contribution to the production performance that characterizes the modern broiler chicken. There has been suggestion that selection for growth has resulted in high incidences of leg problems, such as tibial dyschondroplasia (TD) and varus–valgus deformities (VVD) (Julian, 2005). Although the precise etiology of these leg problems is not known, low-quality bones could aggravate leg problems by increasing the propensity to bone deformity, fragility, and risk of fractures (Shaw et al., 2010). The heritable nature of TD and VVD (Le Bihan-Duval et al., 1997; Kapell et al., 2012; Rekaya et al. 2013; González-Cerón et al. 2015b) and also bone quality traits (Kapell et al., 2012; González-Cerón et al. 2015a) have been reported. Heritability of leg problems ranged from 0.10 to 0.13 and that of bone quality ranged from 0.10 to 0.77. However, the relationship between leg problems and bone quality is scant. The objective of this study was to establish the genetic relationship between leg problems and bone quality traits in a broiler control population.

MATERIALS AND METHODS

Study Population and Husbandry

The present study used data from 2,301 pedigreed broiler chickens, produced in 8 consecutive hatches from mating 24 sires and 72 dams. The Arkansas random mating population, an unselected broiler control population, was used (Aggrey et al., 2010). The husbandry practices used to raise these birds and the phenotypic characteristics have been reported previously (González-Cerón et al. 2015a,b).

Data

The scoring of the legs for VVD and TD was performed at Weeks 4 and 6, respectively. Depending on the angle size of tibia-metatarsus, 4 categories of scores were defined: normal (score = 0), mild (10 to 25° angle; score = 1), intermediate (25 to 45° angle; score = 2), and severe (>45° angle; score = 3). Depending on the severity of the cartilage abnormality, 4 category scores for TD were defined: normal (score = 0), mild (score = 1), intermediate (score = 2), and severe (score = 3). Details of the scoring methods have been reported by Shim et al. (2012a). The bone quality traits measured were shank weight (SW), shank length (SL), shank diameter (SDIAM), tibia weight (TW), tibia length (TL), tibia diameter (TDIAM), tibia breaking strength (TBS), tibia density (TDEN), tibia mineral density (TMD), tibia mineral content (TMC), and tibia ash content (TAC). At 6 wk, chickens were processed, and both femurs were dislocated to remove the legs from the frame. Tibia diameters were measured at the narrowest and widest points, and then averaged. Assuming the tibia as a cylinder, TW, TL, and TDIAM measurements were used to derive the variable TDEN. TBS was measured with an Instron Materials Tester (Model 5500, Instron Corp., Canton, MA) with Automated Materials Test System software version 4.2. The deformation rate was 5 mm/min. Tracing of force was recorded at a constant rate and the graphs showed plateau curves of maximal force (kilograms) reached to measure of the energy stored in the bone. TMC and TMD were measured by dual-energy X-ray absorptiometry (DXA). DXA scans were performed by using a Lunar Prodigy densitometer (GE Medical Systems, Waukesha, WI). TMC and TMD measurements by DXA are defined as the amount of bone mineral in grams in the scan region, and the amount of TMC normalized to the scan region in centimeters squared, respectively (Cauley et al. 2005; Foutz et al., 2007). After DXA assessment, left tibia was used for determination of percentage of ash on a fat-free dry weight basis, according to AOAC International (2005; Method 932.16). Details on the measurement of the bone quality traits have been reported González-Cerón et al. (2015a).

Statistical Analysis

After removing outliers (individuals with values greater than 3 SD from the mean), there were 2,257 birds recorded for 5 leg problems traits: valgus in the right leg (VLR), valgus in the left leg (VLL), varus in the right leg (VRR), varus in the left leg (VRL), and TD. However, VLR and VLL were combined into valgus (VL), and VRR and VRL were merged into varus (VR) for the analysis. To ameliorate the grossly unequal representation of leg incidences in different hatch groups, and also to eliminate extreme case problems (all observations within a class of the fixed effects have the same discrete response) and improve the computational stability of the analyses, the leg data were reclassified as binary responses 0 (normal) and 1 (abnormal) for each leg. Number of birds with records for SW, SL, SDIAM, TW, TL, TDIAM, TDEN, TBS, TMD, TMC, and TAC ranged from 1,783 to 2,051. Descriptive statistics were obtained using PROC UNIVARIATE procedures of SAS version 9.1.3 (SAS Institute, 2006). A threshold-linear mixed model similar to the model used by Rekaya et al. (2013) was implemented for the joint analysis of leg problems (binary scale) and the continuous bone quality traits. The threshold-linear mixed model used was:  

(1)
\begin{equation} {\rm y}_{{\rm ijnk}} = {\rm H}_{\rm i} {\rm } + {\rm S}_{\rm j} {\rm + u}_{\rm n} {\rm + e}_{{\rm ijnk}} \end{equation}
where yijnk was the observed SW, SL, SDIAM, TW, TL, TDIAM, TDEN, TBS, TMD, TMC, and TAC, or the liabilities for the leg problems (VL, VR, TD) for bird n, Hi (i = 1,2,..8) was the fixed effect of the hatch class i, Sj was the fixed effect of sex j (j = 1,2), un was the random additive effect of bird n, and eijnk was the random residual term. Assuming normality conditionally on the model parameters, the joint distribution of the liability for binary scores and continuous traits is expressed in matrix notation as:  
(2)
\begin{equation} {\bf y}{\rm | }{\boldsymbol \beta }{\rm , u, R}_{\rm 0} \tilde{\rm N}({\boldsymbol X \boldsymbol \beta }{\rm + }{\bf Zu}{\rm , }{\bf R}_{\rm 0} \otimes {\bf I}{\rm )} \end{equation}
where y = (l1 ‘, l2 ‘, l3 ‘, y1 ‘, y2 ‘, …, y11 ‘)’ is the vector of liabilities (l) and continuous responses (yi); $${\boldsymbol \beta}$$ and u are vectors of systematic and random effects, respectively; and R0 is an 14 × 14 residual (co)variance matrix with the first 3 diagonal elements, corresponding to the categorical traits, fixed to 1. X and Z are known incidence matrices. Rekaya et al. (2013) stated that in the Bayesian implementation of a model like the observed in equation (1) via Monte Carlo–Markov chain methods, the direct sampling of R0 is not feasible due to the fixation of some of its diagonal elements. To overcome the problem of sampling of the residual (co)variance matrix and in order to perform the Bayesian implementation of the model via Gibbs sampling, the methods described by Rekaya et al. (2013) were used. The implementation of the model has been adequately described (Rekaya et al. 2013; González-Cerón et al. 2015a,b).

RESULTS

Heritability, genetic, and phenotypic correlations of leg problems and bone quality traits are shown in Table 1. At the phenotypic level, it appears that chickens affected by VL, VR, or TD have less mineralized tibias when evaluated on TAC. Individuals with either VL or VR problem had lighter and less dense tibias comparing to healthy individuals. Heritability of leg problems ranged from 0.10 to 0.13 (González-Cerón et al. 2015b), and those of bone quality traits ranged from 0.10 to 0.77 (González-Cerón et al. 2015a). Genetic correlations of leg problems with bone quality traits tended to be weak ranging from −0.06 to 0.11. Additionally, posterior standard deviations of these estimates were large (0.04 to 0.21). While genetic associations of shank traits and VVD followed an unfavorable trend (0.00 to 0.11), those with TD showed a slightly favorable tendency (−0.06 to 0.01). TW, TL, and TDIAM had unfavorable genetic associations with VVD (0.02 to 0.11) and close to zero with TD (−0.03 to 0.00). TDEN and TBS showed a slight favorable genetic correlation with VL (−0.04 and −0.05, respectively) and TD (−0.03 and −0.03, respectively). In contrast, the corresponding genetic associations of TDEN and TBS with VR were close to zero (0.00 and 0.02, respectively). Genetic correlations of VL and TD with mineralization-related traits (TMD, TMC, and TAC) tended to be slightly favorable (−0.06 to 0.00). On the contrary, VR showed an unfavorable genetic association with TMD, TMC, and TAC (0.00 to 0.09). Genetic correlations among bone quality traits varied from −0.91 to 0.99, and their posterior SD were low (≤0.03). Importantly, TBS was favorably associated to the other bone quality traits (0.07 to 0.95) except with TL and TDEN (−0.04 and −0.64, respectively). Likewise, TAC showed positive associations with SDIAM, TW, TDIAM, TMD, and TMC (0.10 to 0.52), but had negative correlations with SW, SL, TL, and TDEN (−0.53 to −0.09).

Table 1.

Heritability (diagonal), genetic (above diagonal), and phenotypic (below diagonal) correlations of leg problems and bone quality traits in the Arkansas random-bred chicken population.

Trait1 VL VR TD SW SL SDIAM TW TL TDIAM TDEN TBS TMD TMC TAC 
VL $${\hphantom{-}}$$0.10 −0.03 $${\hphantom{-}}$$0.02 $${\hphantom{-}}$$0.02 $${\hphantom{-}}$$0.04 $${\hphantom{-}}$$0.00 $${\hphantom{-}}$$0.02 $${\hphantom{-}}$$0.04 $${\hphantom{-}}$$0.04 −0.04 −0.05 −0.02 $${\hphantom{-}}$$0.00 −0.06 
VR −0.10a $${\hphantom{-}}$$0.12 $${\hphantom{-}}$$0.02 $${\hphantom{-}}$$0.11 $${\hphantom{-}}$$0.10 $${\hphantom{-}}$$0.09 $${\hphantom{-}}$$0.11 $${\hphantom{-}}$$0.10 $${\hphantom{-}}$$0.05 $${\hphantom{-}}$$0.00 $${\hphantom{-}}$$0.02 $${\hphantom{-}}$$0.03 $${\hphantom{-}}$$0.09 $${\hphantom{-}}$$0.00 
TD $${\hphantom{-}}$$0.04 −0.03 $${\hphantom{-}}$$0.13 −0.03 $${\hphantom{-}}$$0.01 −0.06 −0.03 $${\hphantom{-}}$$0.01 $${\hphantom{-}}$$0.00 −0.03 −0.03 −0.03 −0.04 −0.03 
SW $${\hphantom{-}}$$0.05b −0.06b $${\hphantom{-}}$$0.06b $${\hphantom{-}}$$0.56 $${\hphantom{-}}$$0.86 $${\hphantom{-}}$$0.87 $${\hphantom{-}}$$0.96 $${\hphantom{-}}$$0.84 $${\hphantom{-}}$$0.53 −0.17 $${\hphantom{-}}$$0.07 $${\hphantom{-}}$$0.29 $${\hphantom{-}}$$0.76 −0.09 
SL $${\hphantom{-}}$$0.08b −0.05b $${\hphantom{-}}$$0.11a $${\hphantom{-}}$$0.68a $${\hphantom{-}}$$0.47 $${\hphantom{-}}$$0.58 $${\hphantom{-}}$$0.92 $${\hphantom{-}}$$0.99 $${\hphantom{-}}$$0.51 −0.23 $${\hphantom{-}}$$0.12 $${\hphantom{-}}$$0.24 $${\hphantom{-}}$$0.70 −0.10 
SDIAM $${\hphantom{-}}$$0.11a $${\hphantom{-}}$$0.02 $${\hphantom{-}}$$0.08b $${\hphantom{-}}$$0.46a $${\hphantom{-}}$$0.62a $${\hphantom{-}}$$0.19 $${\hphantom{-}}$$0.83 $${\hphantom{-}}$$0.52 $${\hphantom{-}}$$0.70 −0.41 $${\hphantom{-}}$$0.29 $${\hphantom{-}}$$0.40 $${\hphantom{-}}$$0.79 $${\hphantom{-}}$$0.19 
TW −0.08b −0.12a $${\hphantom{-}}$$0.10a $${\hphantom{-}}$$0.78a $${\hphantom{-}}$$0.50a $${\hphantom{-}}$$0.16a $${\hphantom{-}}$$0.52 $${\hphantom{-}}$$0.87 $${\hphantom{-}}$$0.66 −0.32 $${\hphantom{-}}$$0.28 $${\hphantom{-}}$$0.37 $${\hphantom{-}}$$0.86 $${\hphantom{-}}$$0.10 
TL $${\hphantom{-}}$$0.02 −0.11a $${\hphantom{-}}$$0.10a $${\hphantom{-}}$$0.64a $${\hphantom{-}}$$0.71a $${\hphantom{-}}$$0.28a $${\hphantom{-}}$$0.69a $${\hphantom{-}}$$0.64 $${\hphantom{-}}$$0.39 −0.10 −0.04 $${\hphantom{-}}$$0.15 $${\hphantom{-}}$$0.59 −0.26 
TDIAM $${\hphantom{-}}$$0.05b −0.01 $${\hphantom{-}}$$0.05b $${\hphantom{-}}$$0.75a $${\hphantom{-}}$$0.59a $${\hphantom{-}}$$0.46a $${\hphantom{-}}$$0.68a $${\hphantom{-}}$$0.49a $${\hphantom{-}}$$0.77 −0.91 $${\hphantom{-}}$$0.66 $${\hphantom{-}}$$0.45 $${\hphantom{-}}$$0.76 $${\hphantom{-}}$$0.52 
TDEN −0.20a −0.12a $${\hphantom{-}}$$0.01 −0.20a −0.36a −0.51a $${\hphantom{-}}$$0.15a −0.07b −0.59a $${\hphantom{-}}$$0.68 −0.64 −0.34 −0.48 −0.53 
TBS −0.03 −0.07b $${\hphantom{-}}$$0.01 $${\hphantom{-}}$$0.38a $${\hphantom{-}}$$0.33a $${\hphantom{-}}$$0.30a $${\hphantom{-}}$$0.38a $${\hphantom{-}}$$0.24a $${\hphantom{-}}$$0.53a −0.29a $${\hphantom{-}}$$0.29 $${\hphantom{-}}$$0.50 $${\hphantom{-}}$$0.60 $${\hphantom{-}}$$0.95 
TMD $${\hphantom{-}}$$0.03 −0.05b $${\hphantom{-}}$$0.03 $${\hphantom{-}}$$0.58a $${\hphantom{-}}$$0.43a $${\hphantom{-}}$$0.38a $${\hphantom{-}}$$0.47a $${\hphantom{-}}$$0.31a $${\hphantom{-}}$$0.64a −0.33a $${\hphantom{-}}$$0.69a $${\hphantom{-}}$$0.17 $${\hphantom{-}}$$0.55 $${\hphantom{-}}$$0.46 
TMC $${\hphantom{-}}$$0.06b −0.02 $${\hphantom{-}}$$0.04 $${\hphantom{-}}$$0.75a $${\hphantom{-}}$$0.58a $${\hphantom{-}}$$0.44a $${\hphantom{-}}$$0.61a $${\hphantom{-}}$$0.51a $${\hphantom{-}}$$0.74a −0.35a $${\hphantom{-}}$$0.59a $${\hphantom{-}}$$0.90a $${\hphantom{-}}$$0.48 $${\hphantom{-}}$$0.47 
TAC −0.23b −0.08b −0.05b −0.12a $${\hphantom{-}}$$0.02 $${\hphantom{-}}$$0.07b −0.11a −0.02 −0.03 −0.08b $${\hphantom{-}}$$0.23a $${\hphantom{-}}$$0.24a $${\hphantom{-}}$$0.09b $${\hphantom{-}}$$0.10 
Trait1 VL VR TD SW SL SDIAM TW TL TDIAM TDEN TBS TMD TMC TAC 
VL $${\hphantom{-}}$$0.10 −0.03 $${\hphantom{-}}$$0.02 $${\hphantom{-}}$$0.02 $${\hphantom{-}}$$0.04 $${\hphantom{-}}$$0.00 $${\hphantom{-}}$$0.02 $${\hphantom{-}}$$0.04 $${\hphantom{-}}$$0.04 −0.04 −0.05 −0.02 $${\hphantom{-}}$$0.00 −0.06 
VR −0.10a $${\hphantom{-}}$$0.12 $${\hphantom{-}}$$0.02 $${\hphantom{-}}$$0.11 $${\hphantom{-}}$$0.10 $${\hphantom{-}}$$0.09 $${\hphantom{-}}$$0.11 $${\hphantom{-}}$$0.10 $${\hphantom{-}}$$0.05 $${\hphantom{-}}$$0.00 $${\hphantom{-}}$$0.02 $${\hphantom{-}}$$0.03 $${\hphantom{-}}$$0.09 $${\hphantom{-}}$$0.00 
TD $${\hphantom{-}}$$0.04 −0.03 $${\hphantom{-}}$$0.13 −0.03 $${\hphantom{-}}$$0.01 −0.06 −0.03 $${\hphantom{-}}$$0.01 $${\hphantom{-}}$$0.00 −0.03 −0.03 −0.03 −0.04 −0.03 
SW $${\hphantom{-}}$$0.05b −0.06b $${\hphantom{-}}$$0.06b $${\hphantom{-}}$$0.56 $${\hphantom{-}}$$0.86 $${\hphantom{-}}$$0.87 $${\hphantom{-}}$$0.96 $${\hphantom{-}}$$0.84 $${\hphantom{-}}$$0.53 −0.17 $${\hphantom{-}}$$0.07 $${\hphantom{-}}$$0.29 $${\hphantom{-}}$$0.76 −0.09 
SL $${\hphantom{-}}$$0.08b −0.05b $${\hphantom{-}}$$0.11a $${\hphantom{-}}$$0.68a $${\hphantom{-}}$$0.47 $${\hphantom{-}}$$0.58 $${\hphantom{-}}$$0.92 $${\hphantom{-}}$$0.99 $${\hphantom{-}}$$0.51 −0.23 $${\hphantom{-}}$$0.12 $${\hphantom{-}}$$0.24 $${\hphantom{-}}$$0.70 −0.10 
SDIAM $${\hphantom{-}}$$0.11a $${\hphantom{-}}$$0.02 $${\hphantom{-}}$$0.08b $${\hphantom{-}}$$0.46a $${\hphantom{-}}$$0.62a $${\hphantom{-}}$$0.19 $${\hphantom{-}}$$0.83 $${\hphantom{-}}$$0.52 $${\hphantom{-}}$$0.70 −0.41 $${\hphantom{-}}$$0.29 $${\hphantom{-}}$$0.40 $${\hphantom{-}}$$0.79 $${\hphantom{-}}$$0.19 
TW −0.08b −0.12a $${\hphantom{-}}$$0.10a $${\hphantom{-}}$$0.78a $${\hphantom{-}}$$0.50a $${\hphantom{-}}$$0.16a $${\hphantom{-}}$$0.52 $${\hphantom{-}}$$0.87 $${\hphantom{-}}$$0.66 −0.32 $${\hphantom{-}}$$0.28 $${\hphantom{-}}$$0.37 $${\hphantom{-}}$$0.86 $${\hphantom{-}}$$0.10 
TL $${\hphantom{-}}$$0.02 −0.11a $${\hphantom{-}}$$0.10a $${\hphantom{-}}$$0.64a $${\hphantom{-}}$$0.71a $${\hphantom{-}}$$0.28a $${\hphantom{-}}$$0.69a $${\hphantom{-}}$$0.64 $${\hphantom{-}}$$0.39 −0.10 −0.04 $${\hphantom{-}}$$0.15 $${\hphantom{-}}$$0.59 −0.26 
TDIAM $${\hphantom{-}}$$0.05b −0.01 $${\hphantom{-}}$$0.05b $${\hphantom{-}}$$0.75a $${\hphantom{-}}$$0.59a $${\hphantom{-}}$$0.46a $${\hphantom{-}}$$0.68a $${\hphantom{-}}$$0.49a $${\hphantom{-}}$$0.77 −0.91 $${\hphantom{-}}$$0.66 $${\hphantom{-}}$$0.45 $${\hphantom{-}}$$0.76 $${\hphantom{-}}$$0.52 
TDEN −0.20a −0.12a $${\hphantom{-}}$$0.01 −0.20a −0.36a −0.51a $${\hphantom{-}}$$0.15a −0.07b −0.59a $${\hphantom{-}}$$0.68 −0.64 −0.34 −0.48 −0.53 
TBS −0.03 −0.07b $${\hphantom{-}}$$0.01 $${\hphantom{-}}$$0.38a $${\hphantom{-}}$$0.33a $${\hphantom{-}}$$0.30a $${\hphantom{-}}$$0.38a $${\hphantom{-}}$$0.24a $${\hphantom{-}}$$0.53a −0.29a $${\hphantom{-}}$$0.29 $${\hphantom{-}}$$0.50 $${\hphantom{-}}$$0.60 $${\hphantom{-}}$$0.95 
TMD $${\hphantom{-}}$$0.03 −0.05b $${\hphantom{-}}$$0.03 $${\hphantom{-}}$$0.58a $${\hphantom{-}}$$0.43a $${\hphantom{-}}$$0.38a $${\hphantom{-}}$$0.47a $${\hphantom{-}}$$0.31a $${\hphantom{-}}$$0.64a −0.33a $${\hphantom{-}}$$0.69a $${\hphantom{-}}$$0.17 $${\hphantom{-}}$$0.55 $${\hphantom{-}}$$0.46 
TMC $${\hphantom{-}}$$0.06b −0.02 $${\hphantom{-}}$$0.04 $${\hphantom{-}}$$0.75a $${\hphantom{-}}$$0.58a $${\hphantom{-}}$$0.44a $${\hphantom{-}}$$0.61a $${\hphantom{-}}$$0.51a $${\hphantom{-}}$$0.74a −0.35a $${\hphantom{-}}$$0.59a $${\hphantom{-}}$$0.90a $${\hphantom{-}}$$0.48 $${\hphantom{-}}$$0.47 
TAC −0.23b −0.08b −0.05b −0.12a $${\hphantom{-}}$$0.02 $${\hphantom{-}}$$0.07b −0.11a −0.02 −0.03 −0.08b $${\hphantom{-}}$$0.23a $${\hphantom{-}}$$0.24a $${\hphantom{-}}$$0.09b $${\hphantom{-}}$$0.10 

1VL = Valgus (right and left legs combined); VR = Varus (right and left legs combined); TD = Tibial dyschondroplasia; SW = Shank weight; SL = Shank length; SDIAM = Shank diameter; TW = Tibia weight; TL = Tibia length; TDIAM = Tibia diameter; TDEN = Tibia density; TBS = Tibia breaking strength; TMD = Tibia mineral density; TMC = Tibia mineral content; TAC = Tibia ash content.

aP < 0.0001.

bP < 0.05.

DISCUSSION

At phenotypic level, VL, VR, and TD affected birds appeared to be associated to lower values of TAC which contradicted the reports from Edwards and Veltmann (1983), Leach and Lilburn (1992) and Leterrier et al. (1998) but was in concordance with others (Leterrier and Nys, 1992; Edwards, 2003; Whitehead, 2007). Conflicting reports on leg abnormalities in chickens may be due to several factors including the definition of the trait, incidence of the malady in the population studied, the population size, and the analytical method. However, it is clear that chickens affected by VL showed an important phenotypic association (r = −0.23; P < 0.05) with TAC. As stated by Turner (2006) bone integrity is a function of several traits; however, in the current study, TAC tend to have a major influence on bone quality.

Heritability estimates of leg problems and bone quality traits ranged from 0.01 to 0.77. The genetic architecture of leg problems and bone quality traits in the same population have already been discussed (González-Cerón et al. 2015a,b). From the genetic correlations, improvements in SL, SW, TL, and TW may contribute very little towards the development of VVD. Breeders can include these traits in their selection programs to reduce the incidence of VVD. However, both weights, SW, TL, and TW tend to have insignificant effect of TD. From the mineralization data, it appears that improvements in TMD, TMC, and TAC would contribute towards reductions in incidences of VL and TD. In general, there is a very low negative genetic relationship between bone traits and VL. As a result, improvement in bone quality may slightly reduce the incidence of VL. On the other hand, VR have a slightly positive genetic relationship in bone quality. This is expected since there is a very low negative genetic correlation between VL and VR. Even with low additive genetic factors that underlie both VL and VR, the genetic basis for these 2 leg abnormalities may be different. TAC was highly correlated genetically with TBS; therefore, tibia ash could be considered as a better indicator of bone quality than the in vitro measurement TMC, which had a genetic correlation of 0.6 with TBS. TAC has a low negative genetic correlation with VL and TD, suggesting that improving the ash content of bones could have some positive effects on reducing the incidences of these maladies.

Leg problems and bone quality traits have underlying additive genetic effects. Even though genetic correlations between bone quality and incidences of VL, TD, and VD are weak, improvements in bone quality could still have beneficial effects on leg problems. In addition to the magnitude of nongenetic factors influencing these traits, the accuracy of genetic parameter estimates are low. This could be due in part to inaccuracies in the measurement of these traits and their low incidence levels in the populations that have been studied thus far. The low incidence and unequal distribution of leg problem incidences in the different hatches may have contributed towards the low genetic correlations. However, the strength of the genetic correlations should be viewed in concert with the sensitivity of traits as bone problems. Although genetic improvement of the studied traits is still possible, efforts should be made to increase bone mineralization, as it seems to improve bone quality and bone breaking strength, which has welfare implications. The low heritability values of leg maladies and some bone quality traits suggest that their phenotypic expression is influenced mostly by nongenetic factors. Therefore, management strategies would be also necessary in order to observe their improvement.

We want to thank Nicholas Anthony, University of Arkansas, Fayetteville, AR, Mi Yeon Shim and Arthur Karnuah, Department of Poultry Science, University of Georgia for assisting in the collection of the data. We also want to thank Mr. Chris McKenzie of the Poultry Research Center for his technical assistance. Fernando González-Cerón was supported by the National Council for Science and Technology, Mexico.

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