J. Anim Sci. 2007. 85:632-640. doi:10.2527/jas.2006-372
© 2007 American Society of Animal Science
Associations of polymorphisms of the ovine prion protein gene with growth, carcass, and computerized tomography traits in Scottish Blackface lambs1
R. M. Sawalha*,
S. Brotherstone*,
,
W. Y. N. Man*,
J. Conington*,
L. Bünger*,
G. Simm* and
B. Villanueva*,2
* Scottish Agricultural College, West Mains Road, Edinburgh EH9 3JG, United Kingdom; and
and
School of Biological Sciences, University of Edinburgh, West Mains Road, Edinburgh EH9 3JT, United Kingdom
 |
Abstract
|
|---|
The objective of this study was to investigate and estimate the associations of the ovine prion protein (PrP) genotypes with a wide range of performance traits in Scottish Blackface lambs. Performance records of up to 7,138 sheep of known PrP genotypes born from 1999 to 2004 in 2 experimental farms were utilized. Performance traits studied were BW at birth, marking (when the sheep were identified with permanent ear tags at an average age of 52 d), and weaning (average age of 107 d); slaughter traits (BW at slaughter, slaughter age, carcass weight, and carcass conformation); ultrasonic muscle and fat depths; and computerized tomography-predicted carcass composition and carcass yield at weaning. Different linear mixed models, including random, direct animal effect, and up to 3 maternal effects (genetic, permanent, and temporary environmental) were used for the different traits. The PrP genotype was included in the model as a fixed effect, along with other fixed factors with significant effects (P < 0.05). Five separate analyses were carried out for each trait, differing in the method of PrP genotypic classification. The first analysis was based on classifying the sheep into categories according to all 9 available PrP genotypes. In the other 4 analyses, sheep were categorized according to the number of each PrP allele carried. Results showed that there were no significant differences between PrP genotypes for any of the performance traits studied when all 9 genotypes were compared (first analysis). Similarly, performance of the lambs did not significantly differ between genotypes with different numbers of ARR copies. However, there were significant variations in a few traits with respect to the number of ARQ, AHQ, and VRQ alleles carried. Heterozygous lambs for the AHQ or the ARQ allele were significantly heavier at some ages than lambs of the other genotypes. Lambs carrying the VRQ allele required approximately 10 d longer finishing time (P = 0.01) and yielded carcasses approximately 0.5 kg heavier (P = 0.03) compared with noncarriers. The few significant associations found do not have a negative influence on performance when selecting against the most susceptible PrP allele (VRQ) or in favor of the most resistant one (ARR). Overall, there were no major associations of PrP genotypes with most lamb performance traits in Scottish Blackface sheep.
Key Words: computerized tomography • growth • prion protein gene • PrP • scrapie • sheep
|
INTRODUCTION
|
|---|
There has been growing concern about the prevalence of transmissible spongiform encephalopathies, including scrapie, the sheep form of the disease, with numerous countries implementing national eradication programs. Resistance and susceptibility to clinical scrapie is well documented to be largely associated with polymorphisms of the prion protein (PrP) gene (Hunter et al., 1996
; Hunter, 1997). Generally, the eradication programs rely on restricting the use of sheep with susceptible PrP alleles or selecting in favor of the sheep with the more resistant alleles, or both. However, there is a possibility that the PrP gene has a pleiotropic effect or is linked with genes affecting other important production or welfare traits. Hence, selective breeding based on PrP genotypes may be at odds with the genetic improvement of sheep.
Several studies investigating the potential associations of the commonly recorded lamb performance traits and PrP genotypes have been published recently. Except for 2 studies (Man et al., 2006; Moore et al., 2006), most studies used phenotypic data sets with less than 1,000 PrP genotypes per breed (e.g., Alexander et al., 2005; Vitezica et al., 2005; Isler et al., 2006) or predicted breeding values (Prokopová et al., 2002; Brandsma et al., 2004). The PrP genotypes in many of these studies were obtained for a selected subset of the sheep, which was shown to have different performance than nongenotyped sheep (de Vries et al., 2005). The objective of this study was to evaluate possible associations between PrP genotypes and several traits in Scottish Blackface sheep using large (up to 7,000) nonselectively obtained PrP genotypic information. A wide range of performance traits, including, for the first time, computerized tomography (CT)-predicted traits, was analyzed.
|
MATERIALS AND METHODS
|
|---|
All procedures involving sheep were determined by an animal ethics committee at Scottish Agricultural College and were carried out under the United Kingdom Home Office license, following the regulations of the Animals Act 1986.
Farms and Management
The data used in this study were for lambs born from 1999 to 2004 at 2 experimental Scottish Blackface sheep farms of the Scottish Agricultural College, UK. One of the farms is located in the Pentland Hills in Midlothian, Scotland, with an average annual rainfall of 800 mm, average altitude of ca. 350 m above sea level, and a combination of wet and dry heath vegetation. The other farm has harsher environmental conditions and is located in West Perthshire, Scotland, with an average annual rainfall of 2,900 mm, average altitude of ca. 600 m above sea level, and acidic grassland vegetation.
The sheep were raised under typical environmental conditions and management practices of Scottish hill sheep farming. The sheep were bred from mid November to mid December, and the lambing season was from mid April to mid May. The grazing areas of both farms were divided, and ewes were assigned to different areas based first on pregnancy scan results and later according to the number of lambs reared. Therefore, the lambs were assigned a code (grazing paddock) based on the area in which they were kept with their dams to allow for subsequent adjustment of the performance records.
The sheep belonged to 3 genetic lines that were initiated in 1997. A selection and a control line were established based on a multiple trait, selection index that included lamb and ewe traits (Conington et al., 2001). An industry line, used for extension purposes, was created by purchasing rams, selected via visual appraisal only, from other commercial farms. A summary of the performance of these lines is described by Conington et al. (2006). The sheep in the 2 farms were genetically connected through participation in a Scottish Blackface sire referencing scheme. Each year, 40 ewes in each farm were serviced with semen from 2 referencing rams selected from participating Scottish Blackface flocks.
Data and Traits
The traits considered were BW (at birth, marking, and weaning), slaughter traits (BW at slaughter, slaughter age, carcass weight, and carcass conformation), ultrasonic measurements (muscle and fat depths), and CT-predicted carcass traits [weights of muscle, carcass fat, internal fat, bone, total carcass, and carcass yield (killing out percentage)]. There were over 7,000 records with both performance and PrP genotypic information available for early BW traits, over 2,600 records for slaughter traits, and 411 records for CT-predicted traits. The number of available records, averages, age at recording, CV, and number of sires are presented in Table 1.
Birth weight was recorded within 24 h of lambing. Subsequent lamb BW were recorded at marking (mid-lactation, at an average age of 52 d) and at weaning (at an average age of 107 d). The slaughter traits considered were mostly available for male lambs; therefore records of females were discarded in the analyses of these traits. Carcass traits were recorded at approximately 24 h after slaughter.
Carcasses were visually appraised for carcass conformation by a trained Meat and Livestock Commission representative. Carcass conformation is mainly a measure of the shape of the legs and the shoulders and is categorized as E, U, R, O, or P, with E being the best and P the worst. The distribution of records among different carcass conformation scores was 0.11, 14.9, 55.4, 28.2, and 1.35% for E, U, R, O, and P, respectively. The EUROP carcass conformation data were converted into SD units based on the underlying standard normal distribution.
Fatness score was also recorded on slaughtered sheep to be used in the analyses of the other slaughter traits as an adjustment factor of the end point (criterion for slaughtering). A fatness score was assigned for carcasses based on the estimated external fat percentage. The fatness data were scored based on 7 grades (1, 2, 3L, 3H, 4L, 4H, and 5) corresponding to estimated external fat percentage of 4, 8, 11, 13, 15, 17, and 20%, respectively (Kempster et al., 1986). Animal slaughter criterion was based on achieving a fatness score of 3L (subjectively predicted in live sheep by BCS) and to a lesser degree on BW.
Ultrasonic muscle and fat depths were measured at the third lumbar vertebra around weaning age. The data of ultrasonic fat depth were found to be skewed to the right, based on the normal probability plots, and were therefore square root transformed. The CT scanning data around weaning time were available for male and female lambs born in 2003 and 2004 at both farms. Lambs were scanned at 3 key reference positions: the caudal ischium, the fifth lumbar vertebra, and the eighth thoracic vertebra. The CT images and BW at the time of scanning were used to predict the weights of muscle, carcass fat, internal fat, and bone tissues. The CT-predicted total carcass weight was calculated as the sum of all predicted tissue weights. The prediction equations used were developed previously with multiple regression, using CT scanning data, BW, and dissection data from Scottish Blackface lambs (Young et al., 2001; Lambe et al., 2006). The carcass yield (killing out percentage) was calculated as the ratio of the total predicted carcass weight to BW at time of scanning.
PrP Genotypes
The PrP genotypes were obtained, using proprietary SNP technology by CBS Technologies, through Orchid Cellmark Europe Ltd. (Oxfordshire, UK). This technique allows for detecting polymorphisms at the 136, 154, and 171 codons of the PrP gene and thus for the distinction among ARR, ARQ, AHQ, ARH, and VRQ alleles. All available live lambs born from 1999 to 2004 were genotyped using DNA extracted from blood samples collected around weaning. Moreover, genotypes for lambs born from 2002 to 2004 that died before the blood sampling were obtained from DNA extracted from ear tissue samples. The genotypes were checked for consistency between parents and progeny, and those that showed disagreement were removed from the data set.
The PrP allelic and genotypic frequencies were estimated using the SAS/Genetics program (SAS Inst. Inc., Cary, NC). The SEM frequencies were estimated by bootstrap sampling. A test for Hardy-Weinberg equilibrium was carried out using the permutation version of the exact test, as described by Guo and Thompson (1992), and the levels of significance were calculated by the Monte Carlo permutation procedure.
Statistical Analyses and Model Selection
A linear mixed model with random direct and maternal additive genetic effects and maternal permanent (between litters) and temporary (within litters) environmental effects was compared with 8 other reduced model forms. The reduced model forms were constructed using different combinations of the maternal random effects in addition to the direct additive genetic effects. In addition to the substitution of different maternal components, a model with all maternal effects summed in a single common maternal term was tested and compared with the previous models. The linear mixed model equation of the complete model was
 |
where y is the vector of observations for a particular trait, ß is the vector of fixed effects, d is the vector of direct additive genetic effects, m is the vector of maternal additive genetic effects, p is the vector of maternal permanent environmental effects, t is the vector of maternal temporary environmental effects, e is the vector of random residual effects, and X, Z1, Z2, Z3, and Z4 are incidence matrices relating the fixed and random effects to the observations. The expectation of y was assumed to be E[y] = Xß, and the variances and covariances of the random effects were assumed to be V(d) = AN
, V(m) = AN
, V(p) = Im
, V(t) = Il
, V(e) = In
, and Cov(d,m) = AN
dm, where AN is the numerator relationship matrix of order N (number of sheep in the pedigree); Im, Il, and In are identity matrices of order m (number of ewes with lambing records), l (number of litters), and n (number of records), respectively; 2d, , , , and are the variances of direct additive genetic effects, maternal additive genetic effects, maternal permanent environmental effects, maternal temporary environmental effects, and residual effects, respectively; and dm is the covariance between direct and maternal additive genetic effects. All other covariances were assumed to be zero.
The best fit model for each trait was selected based on the Akaike’s information criterion (Akaike, 1974). The complete model mentioned above, with the assumption of 0 covariance between direct and maternal additive genetic effects, had the best fit to the data of pre-slaughter BW and ultrasonic traits. The available data for preslaughter BW and ultrasonic traits were large enough and also were recorded on parents and progeny to allow for accurate estimation of all of these effects in the model with reasonable SE. On the other hand, the most suitable model for slaughter and carcass weight and CT traits included direct additive genetic and common maternal effects. For carcass conformation, a model with direct and maternal additive genetic effects, assuming a null covariance between the 2 effects, had the best fit to the data, compared with the alternative models. Age at slaughter was analyzed with a model that included correlated direct and maternal additive genetic effects and maternal temporary environmental effect.
Several fixed effects were tested for every trait and were included in the analyses if they had a significant effect. In addition to the PrP genotypes (described below), fixed effects tested included sex, type of birth and rearing, age of dam, farm, year of birth, genetic line, grazing paddock, age at recording, and date of birth when biologically appropriate. All 2-way interactions between the fixed effects for different traits were also tested using the same criteria as for the main effects. Table 2 shows the factors used in the final model for different traits, except for CT-predicted weights. The selected final model for CT traits included effects of sex, type of rearing, age of dam, farm, year, and the age at recording (as a linear covariate). Sex was not included in the model for CT-predicted carcass fat because it did not have an effect (P > 0.05) on this trait. Similarly, carcass yield was not affected (P > 0.05) by year of birth and age at recording. The data were analyzed using the ASReml program, Release 1.1 (Gilmour et al., 2002).
View this table:
[in this window]
[in a new window]
|
Table 2. Tests of significance of differences (P-values) for fixed effects in the model to analyze different traits
|
|
Testing of Associations of PrP Genotype with Performance
The potential associations of the PrP genotypes with performance traits were assessed by including the genotype as a fixed effect in the model. Each trait was analyzed 5 times according to different PrP genotypic classifications. Sheep were first classified based on the 9 PrP genotypes per se (analysis 1). The other 4 classifications were based on the number of each of the 4 PrP alleles carried. For example, based on the number of ARR alleles carried, the sheep were classified as ARR/ARR, ARR/xxx, and xxx/xxx, where xxx represents any PrP allele other than the ARR. A similar classification criterion was applied for the other PrP alleles (AHQ, ARQ, and VRQ). It should be noted that there were no homozygous VRQ sheep because only noncarrier rams were used as sires. Likewise, the number of homozygous AHQ sheep was small (0.69%), and therefore, they were excluded from the analysis based on the AHQ allele classification. Least squares means of traits affected by PrP genotypes (P < 0.05) were compared using Scheffé’s multiple range test (Scheffé, 1953).
|
RESULTS
|
|---|
PrP Gene Frequency
Genotypic and allelic (by year of birth) frequencies of the PrP gene are presented in Tables 3 and 4, respectively. There were small variations of the PrP allele frequencies over the period of the study (i.e., 1999 to 2004). The ARQ allele was the most frequent (ca. 60%), whereas the VRQ allele was the least (ca. 1%). The ARH allele was not present in these flocks, which is also the case with the national UK Scottish Blackface population (Eglin et al., 2005). In general, the frequencies of the most common PrP alleles in these flocks were in close agreement with those in commercial flocks at the national level (Eglin et al., 2005). However, the VRQ allele was less frequent in the current study compared with the national average (2.6%), which may be due to avoidance of the use of rams carrying this allele in the 2 farms in this study. Four genotypes (VRQ heterozygous and AHQ homozygous) had frequency of less than or equal to 1%. On the other hand, each of the ARR/ARQ and ARQ/ARQ genotypes had a frequency of at least 35%. The population was found to be in Hardy-Weinberg equilibrium for the PrP gene (P = 0.56) despite the practiced selection against the VRQ allele.
Associations of PrP Genotypes with Performance Traits
Table 5 shows P-values for effect of the PrP gene based on different genotypic classifications on different performance traits. The first association analysis comparing the level of performance by the 9 different genotypes showed no differences (P > 0.05) for any of the studied traits (Table 5).
View this table:
[in this window]
[in a new window]
|
Table 5. Tests of significance of differences (P-values) for traits and analyses differing in genotype classifications
|
|
Least squares means and SE for each trait from analyses 2, 3, 4, and 5 for PrP genotypic classifications based on ARR, AHQ, ARQ, and VRQ alleles are shown in Tables 6, 7, 8, and 9, respectively. There was no effect (P > 0.05) of the PrP genotypes on any of the studied traits when the sheep were classified based on the number of ARR alleles carried (Tables 5
and 6). Thus, the performance of sheep with the most scrapie resistant PrP genotype (i.e., ARR/ARR) does not differ from the performance of the sheep with the other genotypes. On the contrary, some lamb performance traits were affected (P < 0.05) by the number of AHQ, ARQ, and VRQ alleles carried (Table 5). Lambs heterozygous for the AHQ allele were heavier (P = 0.02) at slaughter time (about 0.5 kg) and yielded heavier (P = 0.03) carcasses (about 0.2 kg) than the lambs that did not carry the allele (Tables 5 and 7). Similarly, lambs heterozygous for the ARQ allele were heavier than the homozygous ARQ lambs, and the differences were statistically significant for BW at birth and at slaughter (Tables 5 and 8).
The analyses comparing VRQ carrier and noncarrier lambs (Tables 5 and 9) showed that the VRQ carriers needed longer (P = 0.01) to finish at the target carcass fatness score (more than 10 d); meanwhile, they yielded 0.5 kg heavier (P = 0.03) carcasses compared with the lambs without the VRQ allele. Also, the VRQ carriers had lower ultrasonic muscle and fat depths and lighter CT-predicted tissue weights than the nonVRQ carriers though the differences were not statistically significant.
No associations (P > 0.05) were found between PrP genotypes and ultrasonic muscle and fat depths or CT-predicted carcass composition or carcass yield. The data for the ultrasonic muscle and fat depths were reanalyzed by adjusting for variations in BW at the time of scanning (instead of including age at recording in the model) to compare the ultrasonic measurements at a constant BW (data not shown). The results were the same as with the original model suggesting that the PrP genotypes do not influence the back fat and muscle depths at constant BW.
|
DISCUSSION
|
|---|
This study has investigated potential associations of the PrP genotypes with a wide range of lamb performance traits. Some of the traits considered here (CT-predicted carcass composition traits) have not been analyzed previously in any sheep breed in terms of their association with PrP genotypes. Except for CT data, the performance data used in this study were large for all traits (in the order of thousands) with all the sheep being nonselectively PrP genotyped. There was no significant effect of PrP genotypes on any of the lamb performance traits studied when all the 9 PrP genotypes were included in the model. This analysis was repeated after discarding records of the genotypic classes with frequencies equal to or less than 1% (i.e., sheep with the AHQ/AHQ, ARR/VRQ, AHQ/VRQ, and ARQ/VRQ genotypes). The latter analyses showed that the PrP genotype had a significant effect on slaughter and carcass weights (data not shown). Sheep with the AHQ/ARQ genotype had the greatest weight at slaughter and yielded the heaviest carcasses, and they were significantly different from lambs with the ARQ/ARQ genotype (data not shown). These results correspond with the results obtained from analysis 4, comparing the sheep based on the number of the ARQ alleles carried. The low frequency of some of the PrP genotypic classes could have affected the power of the overall statistical test in the analysis fitting all 9 genotypes. Hence, analyses 2 to 5 grouped the records according to the number of each allele carried, which is expected to improve the statistical power of the test with the utilization of all available records instead of removing the less frequent genotypes.
There were few significant differences between different PrP genotypic classes in the analyses based on AHQ, ARQ, and VRQ allele classifications. The significantly and the nonsignificantly affected traits were generally consistent where the differences between PrP genotypes were in the same direction. For example, slaughter and carcass weights were significantly greater for AHQ carriers than for noncarriers and in the same way lambs with 1 copy of the AHQ allele were always heavier and had heavier CT-predicted tissue weights than the noncarriers though the differences were not statistically significant for these traits. The VRQ carriers needed longer finishing time, which suggests that the VRQ carriers had a slower growth rate to attain the target carcass fatness score to be slaughtered. There were 61 sheep carrying the VRQ allele, and they were distributed among several fixed effect classes and came from different sires and dams, which may preclude attributing the observed differences to sampling error. None of the few other significantly affected traits implied that the most scrapie susceptible genotypes are linked with greater performance. Notably, the number of copies of the ARR allele, which is mostly associated with the highest scrapie resistance, did not influence any of the traits studied.
Results from previous studies investigating associations of PrP genotypes with lamb performance traits are highly variable and generally inconsistent among the different breeds. Published studies have used different PrP genotypic and allelic classifications. Many previous studies had low frequencies in many PrP classes because of generally small datasets and therefore did not show conclusive results about possible association of PrP genotypes and performance. Some of the association studies were based only on the polymorphisms of the PrP gene at the single 171 codon and ignored the highly susceptible VRQ allele, which is determined by the polymorphism at codon 136 (Roden et al., 2001; Prokopová et al., 2002; Alexander et al., 2005).
Most studies relevant to our study did not find evidence of significant associations of the lamb performance traits with PrP genotype of the sheep (Prokopová et al., 2002; de Vries et al., 2004; Vitezica et al., 2005; Man et al., 2006) or the dams (Vitezica et al., 2006). However, other studies found some significant associations. Brandsma et al. (2005) found that ARQ heterozygous Texel lambs were significantly heavier at 10 d of age than homozygous lambs, which may correspond with our results for increased birth weights using the ARQ allele classification. However, other results (ARR/ARR lambs having significantly lighter 135-d BW than lambs with the other genotype) found by the same authors in a previous study (Brandsma et al., 2004) do not agree with the findings in our study. Thus, there is no consistent pattern of associations between the PrP genotypes and performance of various sheep breeds. However, comparison of results from different studies is often not straightforward because of different criteria used for classifying the PrP genotypes and variation in the traits studied.
The data used in this study from CT scans are unique because they have allowed us to evaluate the potential associations between PrP genotypes and accurate predictors of carcass composition for the first time. Computerized tomography is a noninvasive method and has been shown to be very accurate in predicting carcass composition in live sheep (Lambe et al., 2003). The accuracy of CT for predicting muscle and fat tissue weights was found to be close to 100% (96 to 98%), and that for predicting bone weight was also high (89%) for Suffolk lambs (Young et al., 1999). Therefore, CT predictions are considered to be more reliable for assessing body composition than ultrasonic predictions or postslaughter subjective carcass scoring (Young et al., 2001). The use of CT-predicted data is also of particular value; a previous study has reported an association of PrP genotypes with ultrasonic muscle depth (de Vries et al., 2004). Similarly, Isler et al. (2006) found significant associations of PrP alleles with carcass length, rump width, and marbling in the Romanov breed. In our study, there were no significant differences in carcass conformation, ultrasonic traits, or CT-predicted traits with any of the PrP genotypic classifications. This corresponds with results of Moore et al. (2006) for ultrasonic traits in Charollais sheep.
In this study, the potential associations of PrP genotypes with 15 lamb performance traits were tested 5 times and resulted in 6 statistically significant (P < 0.05) tests. Based on a 0.05 critical significance threshold for 75 statistical tests (15 x 5), between 3 to 4 tests (0.05 x 75) are expected to be false positives. Consequently, the 6 tests that were declared to be statistically significant and the consistency in the differences in the nonsignificantly affected traits are still greater than the probability of getting such results by chance. There are several possible explanations for the few significant associations of PrP genotypes with performance traits found here. One of the possible explanations may be the hypothesis that the PrP gene has a pleiotropic effect on performance traits besides its known effect on resistance or susceptibility to scrapie (Hunter et al., 1996; Hunter, 1997). The association is also possible through linkage between the PrP gene and other genes that influence the performance traits. However, it is not possible to disentangle the exact cause of the observed associations in this study. Founder effect can cause false positive associations though this is less likely with the current data set. This is because of the large size of the data set and the fact that the pedigree goes back 14 yr to 1991 and the fact that most sires were used for 1 yr only. The observed associations could not be attributed to selective genotyping because all the available live sheep around weaning time and some of the dead sheep were PrP genotyped. The 2 farms involved are known to have always been scrapie free. Consequently, the differences in performance may not be attributed to the possible effect of clinical or subclinical scrapie. The large number of available records provides strong credibility for the results found here. The large size of the data may have also contributed to revealing some associations that would not otherwise be discovered.
|
IMPLICATIONS
|
|---|
This study showed that most lamb performance traits in Scottish Blackface sheep may be considered not to be significantly affected by PrP genotypes. The few significant associations found between lamb performance (mostly at slaughter age) and the PrP genotypes do not imply unfavorable consequences of scrapie eradication programs aiming at reducing the frequency of the VRQ allele or at increasing the frequency of the ARR allele.
|
Footnotes
|
|---|
1 This work was funded primarily by DEFRA (Department for Environment, Food and Rural Affairs). The authors are grateful to S. Bishop and K. Boulton for their comments about the manuscript, to A. McLaren, M. Steel, and N. Lambe for collecting the data and to D. White for assisting in formatting the manuscript. 
2 Corresponding author: Beatriz.Villanueva{at}sac.ac.uk
Received for publication June 8, 2006.
Accepted for publication September 26, 2006.
|
LITERATURE CITED
|
|---|
Akaike, H. 1974. A New Look at the Statistical Identification Model. IEEE T. Automat. Contr. 19:716–723.[CrossRef]
Alexander, B. M., R. H. Stobart, W. C. Russell, K. I. O’Rourke, G. S. Lewis, J. R. Logan, J. V. Duncan, and G. E. Moss. 2005. The incidence of genotypes at codon 171 of the prion protein gene (PRNP) in five breeds of sheep and production traits of ewes associated with those genotypes. J. Anim. Sci. 83:455–459.[Abstract/Free Full Text]
Brandsma, J. H., L. L. G. Janss, and A. H. Visscher. 2004. Association between PrP genotypes and littersize and 135 days weight in Texel sheep. Livest. Prod. Sci. 85:59–64.[CrossRef]
Brandsma, J. H., L. L. G. Janss, and A. H. Visscher. 2005. Association between PrP genotypes and performance traits in an experimental Dutch Texel herd. Livest. Prod. Sci. 95:89–94.[CrossRef]
Conington, J., S. C. Bishop, B. Grundy, A. Waterhouse, and G. Simm. 2001. Multi-trait selection indexes for sustainable UK hill sheep production. Anim. Sci. 73:413–423.
Conington, J., S. C. Bishop, N. R. Lambe, L. Bünger, and G. Simm. 2006. Testing selection indices for sustainable hill sheep production - lamb growth and carcass traits. Anim. Sci. 82:445–454.[CrossRef]
de Vries, F., N. Borchers, H. Hamann, C. Drögemüller, S. Reinecke, W. Lüpping, and O. Distl. 2004. Associations between the prion protein genotype and performance traits of meat breeds of sheep. Vet. Rec. 155:140–143.[Abstract/Free Full Text]
de Vries, F., H. Hamann, C. Drogenmuller, M. Ganter, and O. Distl. 2005. Analysis of associations between the prion protein genotypes and production traits in East Friesian milk sheep. J. Dairy Sci. 88:392–398.[Abstract/Free Full Text]
Eglin, R. D., R. Warner, S. Gubbins, S. K. Sivam, and M. Dawson. 2005. Frequencies of PrP genotypes in 38 breeds of sheep sampled in the National Scrapie Plan for Great Britain. Vet. Rec. 156:433–437.[Abstract/Free Full Text]
Gilmour, A. R., B. J. Gogel, B. R. Cullis, S. J. Welham, and R. Thompson. 2002. ASReml User Guide Release 1.0. VSN Int. Ltd., Hemel Hempstead, UK.
Guo, S. W., and E. A. Thompson. 1992. Performing the exact test of Hardy-Weinberg proportion for multiple alleles. Biometrics 48:361–372.[CrossRef][Medline]
Hunter, N. 1997. PrP genetics in sheep and the implications for scrapie and BSE. Trends Microbiol. 5:331–334.[CrossRef][Medline]
Hunter, N., J. D. Foster, W. Goldmann, M. J. Stear, J. Hope, and C. Bostock. 1996. Natural scrapie in a closed flock of Cheviot sheep occurs only in specific PrP genotypes. Arch. Virol. 141:809–824.[CrossRef][Medline]
Isler, B. J., B. A. Freking, R. M. Thallman, M. P. Heaton, and K. A. Leymaster. 2006. Evaluation of associations between prion haplotypes and growth, carcass, and meat quality traits in a Dorset x Romanov sheep population. J. Anim. Sci. 84:783–788.[Abstract/Free Full Text]
Kempster, A. J., G. L. Cook, and M. Grantley-Smith. 1986. National estimates of body composition of British cattle, sheep and pigs with special reference to trends in fatness. A review. Meat Sci. 17:107–138.
Lambe, N. R., J. Conington, K. A. McLean, E. A. Navajas, A. V. Fisher, and L. Bünger. 2006. In vivo prediction of internal fat weight in Scottish Blackface lambs, using computerized tomography. J. Anim. Breed. Genet. 123:105–113.[CrossRef][Medline]
Lambe, N. R., M. J. Young, K. A. McLean, J. Conington, and G. Simm. 2003. Prediction of total body tissue weights in Scottish Blackface ewes using computed tomography scanning. Anim. Sci. 76:191–197.
Man, W. Y. N., S. Brotherstone, B. G. Merrell, W. A. Murray, and B. Villanueva. 2006. Associations of PrP genotypes with BW and slaughter traits in an experimental flock of Swaledale sheep in Great Britain. Anim. Sci. 82:607–614.
Moore, R. C., K. Boulton, and S. C. Bishop. 2006. Investigating the effect of PrP genotype on production traits in Charollais sheep. Page 1 in Proc. Br. Soc. Anim. Sci., York, UK.
Prokopová, L., R. M. Lewis, W. S. Dingwall, and G. Simm. 2002. Scrapie genotype: A correlation with lean growth rate? Proc. 7th World Congr. Genet. Appl. Livest. Prod., Montpellier, France. CD-ROM Commun. No 13–44.
Roden, J. A., W. Haresign, and J. M. L. Anderson. 2001. Analysis of PrP genotype in relation to performance traits in Suffolk sheep. Page 45 in Proc. Br. Soc. Anim. Sci., Scarborough, UK.
Scheffé, H. 1953. A method for judging all contrasts in the analysis of variance. Biometrika 40:87–104.[Abstract/Free Full Text]
Vitezica, Z. G., C. R. Moreno, L. Bodin, D. François, F. Barillet, J. C. Brunel, and J. M. Elsen. 2006. No associations between PrP genotypes and reproduction traits in INRA 401 sheep. J. Anim. Sci. 84:1317–1322.[Abstract/Free Full Text]
Vitezica, Z. G., C. R. Moreno, J. Bouix, F. Barillet, G. Perret, and J. M. Elsen. 2005. A study on associations between PrP genotypes and meat traits in French sheep breeds. Anim. Sci. 81:325–330.[CrossRef]
Young, M. J., R. M. Lewis, K. A. McLean, N. A. A. Robson, J. Fraser, J. Fitzsimons, J. Donbavand, and G. Simm. 1999. Prediction of carcass composition in meat breeds of sheep using computerized tomography. Page 43 in Proc. Br. Soc. Anim. Sci., Scarborough, UK.
Young, M. J., G. Simm, and C. A. Glasbey. 2001. Computerised tomography for carcass analysis. Pages 250–254 in Proc. Br. Soc. Anim. Sci., Scarborough, UK.
Top
Abstract
INTRODUCTION
