Genetics of litter size in three maternal lines of rabbits: Repeatability versus multiple-trait models

Partial diallel cross for assessing genetic merit of local rabbit breed

This study was carried out to estimate general combining ability (GCA) and specific combining ability (SCA) of pre-and post-weaning traits from a partial diallel cross in three rabbit breeds: Local rabbit (L), Flemish Giant (F), and Rex (R). Body weight at 0 (BW0), 30 (BW30), 42 (BW42), and 63 (BW63) days of age, average daily gains from 0 to 30 d of age (ADG0-30), from 30 to 42 d of age (ADG30-42), from 42 to 63 d of age (ADG42-63), and from 30 to 63 d of age (ADG30-63), litter size at birth (LS0) and at weaning (LS42), and mortality at weaning (MR42) were studied in crossing LL, FF, RR, LF, LR, and FR. Local breed had the highest GCA for BW0, BW30, BW42, and average daily gain before weaning (ADG0-30, and ADG30-42) compared to Flemish Giant and Rex, while GCA of Local breed for average daily gain after weaning (ADG42-63), litter size (LS0, and LS42), and mortality (MR42) was higher than for Rex and similar to that of Flemish Giant. Crossing LF and LR showed higher SCA for BW30, BW42, BW63, ADG0-30, ADG42-63 and ADG30-63 than FR. In conclusion, based on GCA and SCA, the Indonesian Local breed has a high genetic potential in the crossing with Flemish Giant and Rex breeds.

Rabbits Divergently Selected for Total Body Fat Content: Changes in Proximate Composition and Fatty Acids of Different Meat Portions

The present research studied the potential of a four-generation divergent selection (Pannon maternal rabbit lines) based on the total body fat content to create two rabbit lines with different meat attributes: a Fat line to deliver greater amounts of healthy fatty acids for newborns and infants, and a Lean line intended to provide lean meat for everyday consumption. Selection was based on the fat index calculated in 10-week-old live rabbits by means of computed tomography (CT). For each generation, 60 rabbits/line were fed ad libitum with commercial pellet from weaning (5 weeks) to slaughter (11 weeks). A total of 15 rabbits/line were randomly selected for meat quality evaluations: the longissimus thoracis et lumborum muscles (LTLs), hind legs (HLs), forelegs (FLs) and abdominal wall (AW) were analyzed for their proximate composition and fatty acid (FA) profile. FA contents were also calculated. Results highlighted that it was possible to obtain leaner meat for everyday consumption in most meat portions starting from generation 4 (7.93 vs. 11.9, 5.10 vs. 5.98 and 7.26 vs. 10.9 g of lipids/100 g of meat in Lean and Fat groups for the FLs, HLs and AW, respectively). The sole exception was the LTLs, which were not affected by the divergent selection. The total PUFA amount increased in FL and AW (p < 0.05) portions of the Fat line only, attributable to a greater n-3 amount (151 vs. 216 and 73 vs. 143 mg/100 g of meat in Lean and Fat groups for the FLs and AW, respectively).

Genetic and nongenetic factors influencing ewe prolificacy and lamb body weight in a closed Romanov flock

The U.S. Meat Animal Research Center was the first entity in the United States to import the Romanov breed and it has been maintained as a closed flock for over 30 yr. Incorporating this super-prolific breed into crossbred and composite populations has resulted in large improvements in ewe productivity. However, few have quantified factors contributing to genetic and nongenetic variation in ewe reproduction and lamb growth within purebred Romanov populations, which were the objectives of this study. The pedigree contained a total of 8,683 lambs born to 218 and 1,600 unique sires and dams, respectively. Number of lambs born on a per ewe exposed (NLBE) and lambing (NLBL) basis were analyzed in univariate repeatability animal models. As expected, the proportion of phenotypic variance (σP2) in litter size attributable to additive genetic (0.06 to 0.08) and permanent environmental (0.05 to 0.07) effects of the ewe was low. The service sire permanent environmental effect contributed to a small but significant amount of σP2 in NLBE (0.03) but not NLBL. However, the service sire additive genetic effect did not influence σP2 in NLBE or NLBL. Lamb body weight was recorded at birth (BWB) and upon weaning from either milk replacer (~30 d; BWW-N) or their dam (~60 d; BWW-D) and were analyzed in a three-trait model with random additive direct and maternal effects. Estimated direct heritabilities were low for all body weight (BW) traits (0.07 to 0.10). Maternal heritability was moderate for BWB (0.34) but low for weaning BW (0.11 to 0.18). This was the first to report direct and maternal genetic correlations between BW of nursery- and dam-reared lambs, and both were estimated to be moderate (0.43 to 0.47). Additionally, the direct and maternal effects of BWB were more strongly correlated with BWW-N (0.74 to 0.82) than BWW-D (0.17 to 0.33). Despite inbreeding coefficients having increased at a rate of 0.33% per birth year (1986 to 2019) in this flock, they were not consistently associated with reductions in ewe or lamb performance. Parameter estimates generally agreed with those from less-prolific breeds, and results indicate that selection can be an effective means of improving subcomponents of ewe productivity.

Estimation of genetic parameters for four Peruvian guinea pig lines

This study aimed to estimate genetic parameters for four guinea pig lines of a crossbreeding scheme. Two paternal lines are selected for growth rate (P1) and feed conversion rate (P2), whereas two maternal lines are selected for growth rate of litter (M1) and litter size at birth (M2). The heritabilities and genetic correlations were estimated with animal linear models employing multivariate analyses with REML. The heritabilities for birth weight (BW) were 0.21±0.02 and 0.23±0.03 for P1 and P2, respectively, and for weaning weight (WW), the heritability was 0.28±0.03 for P2. The estimates for weight at 60 days of age (W60) were 0.34±0.01 and 0.47±0.04 for P1 and P2, respectively, and for partial feed conversion rate was 0.46±0.03 for P2. Heritabilities for litter weight at birth (LW) were 0.09±0.03 and 0.10±0.03 for P1 and M1, respectively. For litter weight at 10 days of age (LW10), the heritability was 0.15±0.03 for M1. Heritabilities for litter size (LS) were 0.17±0.03, 0.20±0.03 and 0.11±0.03, and for number of pups born alive (BA) were 0.09±0.03, 0.14±0.03 and 0.09±0.03 for P1, M1 and M2, respectively. Similarly, high genetic correlations were found between BW, WW and W60 and between LW, LS, LW10 and BA. The genetic correlation between BW direct and maternal was moderately negative (- 0.24 ± 0.10) for P1. These results show the genetic status for all four guinea pig lines, which is essential for the further improvement of the currently implemented breeding programme and also indicate an opportunity for genetic improvement.

Genetic parameters of resistance to pasteurellosis using novel response traits in rabbits

Background

Pasteurellosis (Pasteurella infection) is one of the most common bacterial infections in rabbits on commercial farms and in laboratory facilities. Curative treatments using antibiotics are only partly efficient, with frequent relapses. Breeding rabbits for improved genetic resistance to pasteurellosis is a sustainable alternative approach. In this study, we infected 964 crossbred rabbits from six sire lines experimentally with Pasteurella multocida. After post-mortem examination and bacteriological analyses, abscess, bacteria, and resistance scores were derived for each rabbit based on the extent of lesions and bacterial dissemination in the body. This is the first study to use such an experimental design and response traits to measure resistance to pasteurellosis in a rabbit population. We investigated the genetic variation of these traits in order to identify potential selection criteria. We also estimated genetic correlations of resistance to pasteurellosis in the experimental population with traits that are under selection in the breeding populations (number of kits born alive and weaning weight).

Results

Heritability estimates for the novel response traits, abscess, bacteria, and resistance scores, ranged from 0.08 (± 0.05) to 0.16 (± 0.06). The resistance score showed very strong negative genetic correlation estimates with abscess (− 0.99 ± 0.05) and bacteria scores (− 0.98 ± 0.07). A very high positive genetic correlation of 0.99 ± 0.16 was estimated between abscess and bacteria scores. Estimates of genetic correlations of the resistance score with average daily gain traits for the first and second week after inoculation were 0.98 (± 0.06) and 0.70 (± 0.14), respectively. Estimates of genetic correlations of the disease-related traits with average daily gain pre-inoculation were favorable but with high standard errors. Estimates of genetic and phenotypic correlations of the disease-related traits with commercial selection traits were not significantly different from zero.

Conclusions

Disease response traits are heritable and are highly correlated with each other, but do not show any significant genetic correlations with commercial selection traits. Thus, the prevalence of pasteurellosis could be decreased by selecting more resistant rabbits on any one of the disease response traits with a limited impact on the selection traits, which would allow implementation of a breeding program to improve resistance to pasteurellosis in rabbits.

Genetic correlations for reproductive and growth traits in rabbits

The objective of this study was to obtain heritability estimates for reproductive (litter size at birth, number born alive, litter size at weaning) and growth traits (individual weaning weight, individual weight at the end of the fattening period), then determine the genetic correlation between them in a synthetic rabbit line. A total of 805 females, 3242 parities, and 18 472 growth records were measured from 2006 to 2017. A pentavariate animal model was used with reproductive and growth traits. Heritability ranged from 0.025 to 0.126 for reproductive traits and from 0.033 to 0.059 for growth traits. These traits showed a large coefficient of variation (from 32% to 56% for reproductive traits and from 21% to 28% for growth traits). The repeatability of reproductive traits was low and the common litter effect for growth traits was the most important component of total variance. The genetic and phenotypic correlations between reproductive and growth traits were high and negative, especially with weight at weaning (−0.848, −0.922, and −0.854 for litter size at birth, number born alive, and litter size at weaning, respectively). In conclusion, because of the high negative correlation between reproductive and growth traits, both reproductive and growth traits should be selected in independent lines and the response to selection should be due mainly to the high coefficient of variation of the traits.

Identification of functional mutations associated with environmental variance of litter size in rabbits

Background

Environmental variance (V_E) is partly under genetic control and has recently been proposed as a measure of resilience. Unravelling the genetic background of the V_E of complex traits could help to improve resilience of livestock and stabilize their production across farming systems. The objective of this study was to identify genes and functional mutations associated with variation in V_E of litter size (LS) in rabbits. To achieve this, we combined the results of a genome-wide association study (GWAS) and a whole-genome sequencing (WGS) analysis using data from two divergently selected rabbit lines for high and low V_E of LS. These lines differ in terms of biomarkers of immune response and mortality. Moreover, rabbits with a lower V_E of LS were found to be more resilient to infections than animals with a higher V_E of LS.

Results

By using two GWAS approaches (single-marker regression and Bayesian multiple-marker regression), we identified four genomic regions associated with V_E of LS, on chromosomes 3, 7, 10, and 14. We detected 38 genes in the associated genomic regions and, using WGS, we identified 129 variants in the splicing, UTR, and coding (missense and frameshift effects) regions of 16 of these 38 genes. These genes were related to the immune system, the development of sensory structures, and stress responses. All of these variants (except one) segregated in one of the rabbit lines and were absent (n = 91) or fixed in the other one (n = 37). The fixed variants were in the HDAC9, ITGB8, MIS18A, ENSOCUG00000021276 and URB1 genes. We also identified a 1-bp deletion in the 3′UTR region of the HUNK gene that was fixed in the low V_E line and absent in the high V_E line.

Conclusions

This is the first study that combines GWAS and WGS analyses to study the genetic basis of V_E. The new candidate genes and functional mutations identified in this study suggest that the V_E of LS is under the control of functions related to the immune system, stress response, and the nervous system. These findings could also explain differences in resilience between rabbits with homogeneous and heterogeneous V_E of litter size.

Genetic parameters of sow feed efficiency during lactation and its underlying traits in a Duroc population

As a result of the genetic selection for prolificacy and the improvements in the environment and farms management, litter size has increased in the last few years so that energy requirements of the lactating sow are greater. In addition, selection for feed efficiency of growing pigs is also conducted in maternal lines, and this has led to a decrease in appetite and feed intake that is extended to the lactation period, so the females are not able to obtain the necessary energy and nutrients for milk production and they mobilize their energetic reserves. When this mobilization is excessive, reproductive and health problems occur which ends up in an early sow culling. In this context, it has been suggested to improve feed efficiency at lactation through genetic selection. The aim of this study is to know, in a Duroc population, the genetic determinism of sow feed efficiency during lactation and traits involved in its definition, as well as genetic and environmental associations between them. The studied traits are daily lactation feed intake (dLFI), daily sow weight balance (dSWB), backfat thickness balance (BFTB), daily litter weight gain (dLWG), sow residual feed intake (RFI) and sow restricted residual feed intake (RRFI) during lactation. Data corresponded to 851 parities from 581 Duroc sows. A Bayesian analysis was performed using Gibbs sampling. A four-trait repeatability animal model was implemented including the systematic factors of batch and parity order, the standardized covariates of sow weight (SWf) and litter weight (LWs) at farrowing for all traits and lactation length for BFTB. The posterior mean (posterior SD) of heritabilities were: 0.09 (0.03) for dLFI, 0.37 (0.07) for dSWB, 0.09 (0.03) for BFTB, 0.22 (0.05) for dLWG, 0.04 (0.02) for RFI and null for RRFI. The genetic correlation between dLFI and dSWB was high and positive (0.74 (0.11)) and null between dLFI and BFTB. Genetic correlation was favourable between RFI and dLFI and BFTB (0.71 (0.16) and -0.69 (0.18)), respectively. The other genetic correlations were not statistically different from zero. The phenotypic correlations were low and positive between dLFI and dSWB (0.27 (0.03), dSWB and BFTB (0.25 (0.04)), and between dLFI and dLWG (0.16 (0.03)). Therefore, in the population under study, the improvement of the lactation feed efficiency would be possible either using RFI, which would not have unfavourable correlated effects, or through an index including its component traits.

A genomewide association study in divergently selected lines in rabbits reveals novel genomic regions associated with litter size traits

Uterine capacity (UC), defined as the total number of kits from unilaterally ovariectomized does at birth, has a high genetic correlation with litter size. The aim of our research was to identify genomic regions associated with litter size traits through a genomewide association study using rabbits from a divergent selection experiment for UC. A high-density SNP array (200K) was used to genotype 181 does from a control population, high and low UC lines. Traits included total number born (TNB), number born alive (NBA), number born dead, ovulation rate (OR), implanted embryos (IE) and embryo, foetal and prenatal survivals at second parity. We implemented the Bayes B method and the associations were tested by Bayes factors and the percentage of genomic variance (GV) explained by windows. Different genomic regions associated with TNB, NBA, IE and OR were found. These regions explained 7.36%, 1.27%, 15.87% and 3.95% of GV, respectively. Two consecutive windows on chromosome 17 were associated with TNB, NBA and IE. This genomic region accounted for 6.32% of GV of TNB. In this region, we found the BMP4, PTDGR, PTGER2, STYX and CDKN3 candidate genes which presented functional annotations linked to some reproductive processes. Our findings suggest that a genomic region on chromosome 17 has an important effect on litter size traits. However, further analyses are needed to validate this region in other maternal rabbit lines.

Correlated response to selection for litter size environmental variability in rabbits' resilience

Resilience is the ability of an animal to return soon to its initial productivity after facing diverse environmental challenges. This trait is directly related to animal welfare and it plays a key role in fluctuations of livestock productivity. A divergent selection experiment for environmental variance of litter size has been performed successfully in rabbits over ten generations. The objective of this study was to analyse resilience indicators of stress and disease in the divergent lines of this experiment. The high line showed a lower survival rate at birth than the low line (-4.1%). After correcting by litter size, the difference was -3.2%. Involuntary culling rate was higher in the high than in the low line (+12.4%). Before vaccination against viral haemorrhagic disease or myxomatosis, concentration of lymphocytes, C-reactive protein (CRP), complement C3, serum bilirubin, triglycerides and cholesterol were higher in the high line than in the low line (difference between lines +4.5%, +5.6 µg/ml, +4.6 mg/ml, +7.9 mmol/l, +0.3 mmol/l and +0.4 mmol/l). Immunological and biochemical responses to the two vaccines were similar. After vaccination, the percentage of lymphocytes and CRP concentration were higher in the low line than in the high one (difference between lines +4.0% and +13.1 µg/ml). The low line also showed a higher increment in bilirubin and triglycerides than the high line (+14.2 v. +8.7 mmol/l for bilirubin and +0.11 v. +0.01 mmol/l for triglycerides); these results would agree with the protective role of bilirubin and triglycerides against the larger inflammatory response found in this line. In relation to stress, the high line had higher basal concentration of cortisol than the low line (+0.2ng/ml); the difference between lines increased more than threefold after the injection of ACTH 1 to 24, the increase being greater in the high line (+0.9 ng/ml) than in the low line (+0.4 ng/ml). Selection for divergent environmental variability of litter size leads to dams with different culling rate for reproductive causes and different kits' neonatal survival. These associations suggest that the observed fitness differences are related to differences in the inflammatory response and the corticotrope response to stress, which are two important components of physiological adaptation to environmental aggressions.

Interaction of direct and social genetic effects with feeding regime in growing rabbits

Background

Most rabbit production farms apply feed restriction at fattening because of its protective effect against digestive diseases that affect growing rabbits. However, it leads to competitive behaviour between cage mates, which is not observed when animals are fed ad libitum. Our aim was to estimate the contribution of direct (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$d$$\end{document}d) and social (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$s$$\end{document}s) genetic effects (also known as indirect genetic effects) to total heritable variance of average daily gain (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{ADG}}$$\end{document}ADG) in rabbits on different feeding regimens (FR), and the magnitude of the interaction between genotype and FR (G × FR).

Methods

A total of 6264 contemporary kits were housed in cages of eight individuals and raised on full (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$F$$\end{document}F) or restricted (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$R$$\end{document}R) feeding to 75% of the ad libitum intake. A Bayesian analysis of weekly records of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{ADG}}$$\end{document}ADG (from 32 to 60 days of age) in rabbits on \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$F$$\end{document}F and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$R$$\end{document}R was performed with a two-trait model including \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$d$$\end{document}d and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$s$$\end{document}s.

Results

The ratio between total heritable variance and phenotypic variance (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$T^{2}$$\end{document}T2) was low (<0.10) and did not differ significantly between FR. However, the ratio between \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$h^{2}$$\end{document}h2 (i.e. variance of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$d$$\end{document}d relative to phenotypic variance) and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$T^{2}$$\end{document}T2 was ~0.52 and 0.86 for animals on \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$R$$\end{document}R and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$F$$\end{document}F, respectively, thus \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$s$$\end{document}s contributed more to the heritable variance of animals on \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$R$$\end{document}R than on \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$F$$\end{document}F. Feeding regimen also affected the sign and magnitude of the correlation between \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$d$$\end{document}d and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$s$$\end{document}s, i.e. −0.5 and ~0 for animals on \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$R$$\end{document}R and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$F$$\end{document}F, respectively. The posterior mean (posterior sd) of the correlation between estimated total breeding values (ETBV) of animals on \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$R$$\end{document}R and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$F$$\end{document}F was 0.26 (0.20), indicating very strong G × FR interactions. The correlations between \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$d$$\end{document}d and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$s$$\end{document}s in rabbits on \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$F$$\end{document}F and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$R$$\end{document}R ranged from −0.47 (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$d$$\end{document}d on \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$F$$\end{document}F and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$s$$\end{document}s on \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$R$$\end{document}R) to 0.64.

Conclusions

Our results suggest that selection of rabbits for \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{ADG}}$$\end{document}ADG under \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$F$$\end{document}F may completely fail to improve \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{ADG}}$$\end{document}ADG in rabbits on \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$R$$\end{document}R. Social genetic effects contribute substantially to ETBV of rabbits on \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$R$$\end{document}R but not on \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$F$$\end{document}F. Selection for \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{ADG}}$$\end{document}ADG should be performed under production conditions regarding the FR, by accounting for \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$s$$\end{document}s if the amount of food is limited.

Correlated response in litter size components in rabbits selected for litter size variability

A divergent selection experiment for the environmental variability of litter size (Ve) over seven generations was carried out in rabbits at the University Miguel Hernández of Elche. The Ve was estimated as the phenotypic variance within the female, after correcting for year-season and parity-lactation status. The aim of this study was to analyse the correlated responses to selection in litter size components. The ovulation rate (OR) and number of implanted embryos (IE) in females were measured by laparoscopy at 12 day of the second gestation. At the end of the second gestation, the total number of kits born was measured (TB). Embryonic (ES), foetal (FS) and prenatal (PS) survival were computed as IE/OR, TB/IE and TB/OR, respectively. A total of 405 laparoscopies were performed. Data were analysed using Bayesian methodology. The correlated response to selection for litter size environmental variability in terms of the litter size components was estimated as either genetic trends, estimated by computing the average estimated breeding values for each generation and each line, or the phenotypic differences between lines. The OR was similar in both lines. However, after seven generations of selection, the homogenous line showed more IE (1.09 embryos for genetic means and 1.23 embryos for phenotypic means) and higher ES than the heterogeneous one (0.07 for genetic means and 0.08 for phenotypic means). The probability of the phenotypic differences between lines being higher than zero (p) was 1.00 and .99, respectively. A higher uterine overcrowding of embryos in the homogeneous line did not penalize FS; as a result, this line continued to show a greater TB (1.01 kits for genetic means and 1.30 kits for phenotypic means, p = .99, in the seventh generation). In conclusion, a decrease in litter size variability showed a favourable effect on ES and led to a higher litter size at birth.

Selection for environmental variance of litter size in rabbits

Background

In recent years, there has been an increasing interest in the genetic determination of environmental variance. In the case of litter size, environmental variance can be related to the capacity of animals to adapt to new environmental conditions, which can improve animal welfare.

Results

We developed a ten-generation divergent selection experiment on environmental variance. We selected one line of rabbits for litter size homogeneity and one line for litter size heterogeneity by measuring intra-doe phenotypic variance. We proved that environmental variance of litter size is genetically determined and can be modified by selection. Response to selection was 4.5% of the original environmental variance per generation. Litter size was consistently higher in the Low line than in the High line during the entire experiment.

Conclusions

We conclude that environmental variance of litter size is genetically determined based on the results of our divergent selection experiment. This has implications for animal welfare, since animals that cope better with their environment have better welfare than more sensitive animals. We also conclude that selection for reduced environmental variance of litter size does not depress litter size.

Multiple-trait structured antedependence model to study the relationship between litter size and birth weight in pigs and rabbits

BACKGROUND

Some genetic studies need to take into account correlations between traits that are repeatedly measured over time. Multiple-trait random regression models are commonly used to analyze repeated traits but suffer from several major drawbacks. In the present study, we developed a multiple-trait extension of the structured antedependence model (SAD) to overcome this issue and validated its usefulness by modeling the association between litter size (LS) and average birth weight (ABW) over parities in pigs and rabbits.

METHODS

The single-trait SAD model assumes that a random effect at time [Formula: see text] can be explained by the previous values of the random effect (i.e. at previous times). The proposed multiple-trait extension of the SAD model consists in adding a cross-antedependence parameter to the single-trait SAD model. This model can be easily fitted using ASReml and the OWN Fortran program that we have developed. In comparison with the random regression model, we used our multiple-trait SAD model to analyze the LS and ABW of 4345 litters from 1817 Large White sows and 8706 litters from 2286 L-1777 does over a maximum of five successive parities.

RESULTS

For both species, the multiple-trait SAD fitted the data better than the random regression model. The difference between AIC of the two models (AIC_random regression-AIC_SAD) were equal to 7 and 227 for pigs and rabbits, respectively. A similar pattern of heritability and correlation estimates was obtained for both species. Heritabilities were lower for LS (ranging from 0.09 to 0.29) than for ABW (ranging from 0.23 to 0.39). The general trend was a decrease of the genetic correlation for a given trait between more distant parities. Estimates of genetic correlations between LS and ABW were negative and ranged from -0.03 to -0.52 across parities. No correlation was observed between the permanent environmental effects, except between the permanent environmental effects of LS and ABW of the same parity, for which the estimate of the correlation was strongly negative (ranging from -0.57 to -0.67).

CONCLUSIONS

We demonstrated that application of our multiple-trait SAD model is feasible for studying several traits with repeated measurements and showed that it provided a better fit to the data than the random regression model.

Multi-trait and repeatability models for genetic evaluation of litter traits in pigs considering different farrowings

SUMMARY We aimed to compare multi-trait and repeatability models to estimate genetic parameters for the traits number of piglets born alive (NBA) and alive at 3 week of age (NP3), litter weight at birth (LW0) and at 3 week of age (LW3), and mean piglet weight at birth (MW0) and at 3 week of age (MW3), considering the first three farrowings of Landrace sows. Heritability (h2) estimates showed an increasing pattern up to the third farrowing for LW0 and MW3. For NBA, NP3, LW3, and MW0 h2 increased from the first to the second and decreased from the second to the third farrowing. In general, heritability estimated in the repeatability model was lower than the mean of the estimates in the multi-trait model. The traits LWO, MW0, and MW3 presented high genetic correlation among different farrowings (0.961–0.997), while NBA, NP3, and LW3 (0.092–0.986) presented irregular values among farrowings. The corrected Akaike information criterion shows that the repeatability model is not indicated for almost all of the studied traits. These results indicate that the multi-trait model is recommended for genetic evaluation of the traits number of piglets born alive and alive at 3 week of age, litter weight and mean piglet weight at birth and 3 week of age, in different farrowings, as different traits.

Isoflurane: An Ideal Anesthetic for Rodent Orthotopic Liver Transplantation Surgery?

PURPOSE

Because the choice of anesthetic affects the rodent orthotopic liver transplantation (OLT) model, we compared the effects of isoflurane, ketamine, chloral hydrate, and pentobarbital on the OLT model.

BASIC PROCEDURES

OLT was performed using the two-cuff technique. Two hundred male rats were randomly divided into five groups: control, isoflurane, ketamine, chloral hydrate, and pentobarbital groups. Rectal temperatures, respiratory rates, arterial blood values (pH, PaCO₂, PaO₂, and SatO₂), liver function tests and histopathology, recovery times, and anhepatic stage mortality rates were assessed.

MAIN FINDINGS

Compared with controls, respiratory rates decreased by 20% in the isoflurane group, and decreased by 40%-50% in the ketamine, chloral hydrate, and pentobarbital groups. The PaO₂, SatO₂, and pH levels in the ketamine, chloral hydrate, and pentobarbital groups were significantly lower than those in the isoflurane and control groups (P < .05). Only the pentobarbital group displayed significant liver histopathologic changes along with significantly higher levels of serum alanine aminotransferase and total bilirubin, but a significantly lower level of serum albumin, compared with the control group (P < .05). The isoflurane group had a 0% anhepatic stage mortality rate compared with rates of 30%-40% in the other anesthetic groups.

PRINCIPAL CONCLUSIONS

Isoflurane should be the preferred anesthetic for rodent OLT surgery due to its minimal respiratory and hepatic physiological effects as well as its low anhepatic phase mortality rate. Secondary to isoflurane, ketamine and chloral hydrate may be administered as donor anesthetics. Pentobarbital use should be avoided entirely in rodent OLT surgery due to its significant hepatotoxic effects.

Crossbreeding effects on rabbit reproduction from four maternal lines of rabbits

Litter size is essential for an efficient production of rabbit meat. A diallel cross between four maternal lines was carried out and the analysis of the components of litter size has been already done. This paper presents the analysis of litter size traits themselves (total born (TB), number born alive (NBA), number weaned (NW)) and kindling interval (KI), that complete the analysis of the reproductive performance. The 16 genetic groups were distributed in four Spanish farms. The V line was present in all farms in order to be used as reference group. A total of 34 546 parities from 7111 does, were analysed. The crossbreeding parameters were estimated according to Dickerson model. The differences between lines performance were of low magnitude and not significant for litter size traits. The LP line showed the shortest KI followed by H respect to lines A and V. These differences reflected the differences between direct and maternal genetic effects. The differences between the average of all crosses and line V were found to be significant and seemed to be important, being 0.46 for TB, 0.56 for NBA, 0.75 for NW and -2.21 days for KI. The differences between reciprocal crosses for litter size were of low magnitude and non-significant, which indicate that the maternal effects are not important between these lines. In general, the lines did not show significant differences in direct and maternal genetic effects for TB, NBA and NW but there were some significant differences for KI, which ranged from 1.54 to 6.85 days in direct effects and from 0.63 to 3.38 days for maternal effects. A positive and, in some cases, relevant heterosis was found. The largest heterosis was for TB in the HV cross (1.05 rabbits), followed by the AH (0.74 rabbits), AV (0.57 rabbits) and LH (0.55 rabbits) crosses. For NBA, significant heterosis was found in HV (1.11 rabbits) and AV (0.49 rabbits) and for NW in AV (0.90 rabbits), LH (0.70 rabbits) and LV (0.58 rabbits). Favourable and significant heterosis for KI was found in AV and LV crosses, whereas it was unfavourable in AL and in LH. The more recommended crosses were AV and LH, showing the greatest performances on NW, while the cross HV, which shows the greatest performances an NBA, could be highly recommended when cross-fostering is practised to equalise litter sizes after birth, in this situation the lower NW performance observed could be improved.

Expected responses to different strategies of selection to increase heat tolerance assessed by changes in litter size in rabbit

Response on litter size (LS) to selection based on EBV of the own trait and several indexes including EBV of 1 or more parameters of a reaction norm model (RNM) was evaluated by simulation. The RNM described animal's performance under different environmental temperatures assuming that this has an animal-specific effect on LS beyond an animal-specific temperature threshold (i.e., it had 3 parameters: intercept [Int], slope [Slp], and threshold [Thr]). Litter size data were generated assuming this model under 2 environmental conditions (comfort [CC] and comfort-to-heat [CtHC]). Variance components for Int, Slp, Thr, and LS were taken from literature. The initial base population consisted of 125 females and 25 males. Ten generations of selection were conducted keeping constant the population size. Eight different selection criteria were considered, depending on both the evaluation model and the index combining EBV for 1 or more parameters of the RNM. In 1 case selection was based on EBV of the own trait predicted by using a repeatability animal model. In the other 7 cases the genetic evaluation was conducted using the same model as that used in the simulation. For each scenario 25 replicates were conducted and response to selection was assessed within replicate as the difference between generations in the average of trait. Results indicate that, under the studied conditions and for the used genetic parameters, selection based on the observed trait seems to have the same effect as selection based on some index, including EBV of the RNM parameters. In addition, response to selection could be greater under CtHC rather than CC. Animals selected exclusively for EBV of the Slp and Thr are not expected to have good performance under CC. Under CtHC, selection for LS has a major response on those parameters, whereas response on Int was very small. Under CC, response to selection on LS is mainly determined by a change in Int, whereas Slp does not change and Thr slightly increases. Selection based on EBV of Slp, Thr, or on an index including both seems to have the same effect on the trait and it was doubly effective in modifying the shape of the RNM under CtHC than under CC. Selection based on EBV of Thr and Slp does not seem to lead to any response in LS. Selection based on EBV of Int seems to have no effect on the trait or on animal's tolerance to heat under CtHC, but it would lead to a positive response in LS under CC.

Feasibility of selection for male contribution to embryo survival as a way of improving male reproductive performance and semen quality in rabbits

The objective of this research was assess the male contribution to the number of implanted embryos (IE) and embryo survival (ES) estimated as the rate of variance due to male genetic and permanent environmental effects regarding total variance. In prolific species, the number of embryos and ES at early stages of gestation could be considered as fertility measurements because it indicates the number and rate of fertilized ova which are able to initiate the embryo development. Analyzed traits were ovulation rate (OR) estimated as the number of corpora lutea in both ovaries, IE estimated as the number of implantation sites, and ES calculated as IE/OR. A total of 1477 records from 900 females were used to analyze OR, whereas 1081 records from 855 females and 201 males were used to analyze IE and ES. The number of animals in the pedigree was 1107. The model included the systematic effects of year-season, parity order, lactation, and the random effects of additive genetics and permanent environmental effects due to the female (for OR) or both sexes (for IE and ES). Gibbs sampling was used to estimate posterior distributions of model parameters. The heritabilities of the male contribution to IE and ES were low (0.05 [0.01, 0.10] and 0.07 [0.02, 0.12]) but these estimates are probably biased downward since laparoscopy was only performed on those does that were pregnant at d 12 of gestation, instead of on all mated does. The genetic correlations between all analyzed traits and also between male and female genetic components of IE and ES were inaccurate, and it was not possible to draw any conclusion about them. The proportion of variation due to the male nonadditive genetic plus permanent environmental effects for IE and ES was almost negligible (0.027 [0.001, 0.058] and 0.031 [0.002, 0.068] for IE and ES, respectively), being the repeatability for male contribution on IE and ES around 8 and 10%, respectively.

Implementation of genetic evaluation and mating designs for the endangered local pig breed 'Bunte Bentheimer'

A pedigree including 1538 individuals of the endangered pig breed 'Bunte Bentheimer' and 3008 records of the fertility traits 'number of piglets born alive' (NBA) and 'number of piglets weaned' (NW) were used to i) characterize the population structure, ii) to estimate genetic (co)variance components and estimated breeding values (EBVs) and iii) to use EBVs for the application of the concept of optimal genetic contributions. The average coefficient of inbreeding increased from F = 0.103 to F = 0.121 within the two recent cohorts. Average rate of inbreeding amounted to 1.66%, which resulted in an effective population size of Ne  = 30 animals in the recent cohort. Average generation interval was 3.07 years considering the whole pedigree, and in total, only 612 sows and boars generated offspring. Estimated heritabilities for both traits NBA and NW were 0.12, and the estimated genetic correlation between both traits was 0.96. The variance component due to the service sire was higher than in commercial pig breeds, presumably due to the widespread use of natural service boars. The EBVs for NBA from 333 selection candidates (63 boars and 270 sows) were used to determine optimal genetic contributions. Based on selected animals and their optimal genetic contributions, specific mating designs were evaluated to minimize inbreeding in the next generation. Best results were achieved when using a simulated annealing algorithm and allowing artificial insemination.

The contribution of dominance and inbreeding depression in estimating variance components for litter size in Pannon White rabbits

In a synthetic closed population of Pannon White rabbits, additive (VA ), dominance (VD ) and permanent environmental (VPe ) variance components as well as doe (bF d ) and litter (bF l ) inbreeding depression were estimated for the number of kits born alive (NBA), number of kits born dead (NBD) and total number of kits born (TNB). The data set consisted of 18,398 kindling records of 3883 does collected from 1992 to 2009. Six models were used to estimate dominance and inbreeding effects. The most complete model estimated VA and VD to contribute 5.5 ± 1.1% and 4.8 ± 2.4%, respectively, to total phenotypic variance (VP ) for NBA; the corresponding values for NBD were 1.9 ± 0.6% and 5.3 ± 2.4%, for TNB, 6.2 ± 1.0% and 8.1 ± 3.2% respectively. These results indicate the presence of considerable VD . Including dominance in the model generally reduced VA and VPe estimates, and had only a very small effect on inbreeding depression estimates. Including inbreeding covariates did not affect estimates of any variance component. A 10% increase in doe inbreeding significantly increased NBD (bF d  = 0.18 ± 0.07), while a 10% increase in litter inbreeding significantly reduced NBA (bF l  = -0.41 ± 0.11) and TNB (bF l  = -0.34 ± 0.10). These findings argue for including dominance effects in models of litter size traits in populations that exhibit significant dominance relationships.

[Progress on breeding for best-hybridized crossing of meat rabbits]

Best-hybridized crossing should ideally result in optimal exploitation of heterosis of lines and capitalize on expressed heterosis. Rabbit breeding is heading in the direction of breeding for best-hybridized crossing of meat rabbits. Most special sire lines are selected for post-weaning average daily gain and marketing weight. Post-weaning growth has a negative and favorable genetic correlation with the feed conversion ratio, which is used in indirect selection for feed conversion ratio. The most common selection criteria for special maternal lines are related to litter size at birth or at weaning. Since the heritability of most reproductive traits is low, we must collect as many individual and relative records as possible in the genetic evaluation of rabbits. The BLUP procedure under an animal repeatability model is the most common procedure used for evaluation of animals in selection programs for special lines of meat rabbits. Direct selection for litter size is less efficient than selection for post-weaning growth, but the estimation of heterosis is generally higher for litter size than that for the post-weaning growth. Evaluation of heterosis could be performed by estimating crossbreeding parameters in the cross or comparing contemporary productivity among purebreds and crossbreds. Here, we reviewed breeding of special lines, exploitation of heterosis of crossbreds, and establishment of crossbreeding system of hybrid meat rabbits and summarized the methodologies of breeding special lines, criteria in selection programs, and the result of heterosis estimates.

Using the product threshold model for estimating separately the effect of temperature on male and female fertility

Animals under environmental thermal stress conditions have reduced fertility due to impairment of some mechanisms involved in their reproductive performance that are different in males and females. As a consequence, the most sensitive periods of time and the magnitude of effect of temperature on fertility can differ between sexes. The objective of this study was to estimate separately the effect of temperature in different periods around the insemination time on male and on female fertility by using the product threshold model. This model assumes that an observed reproduction outcome is the result of the product of 2 unobserved variables corresponding to the unobserved fertilities of the 2 individuals involved in the mating. A total of 7,625 AI records from rabbits belonging to a line selected for growth rate and indoor daily temperature records were used. The average maximum daily temperature and the proportion of days in which the maximum temperature was greater than 25°C were used as temperature descriptors. These descriptors were calculated for several periods around the day of AI. In the case of males, 4 periods of time covered different stages of the spermatogenesis, the transit through the epididymus of the sperm, and the day of AI. For females, 5 periods of time covered the phases of preovulatory follicular maturation including day of AI and ovulation, fertilization and peri-implantational stage of the embryos, embryonic and early fetal periods of gestation, and finally, late gestation until birth. The effect of the different temperature descriptors was estimated in the corresponding male and female liabilities in a set of threshold product models. The temperature of the day of AI seems to be the most relevant temperature descriptor affecting male fertility because greater temperature records on the day of AI caused a decrease in male fertility (-6% in male fertility rate with respect to thermoneutrality). Departures from the thermal zone in temperature descriptors covering several periods before AI until early gestation had a negative effect on female fertility, with the pre- and peri-implantational period of the embryos being especially sensitive (from -5 to -6% in female fertility rate with respect to thermoneutrality). The latest period of gestation was unaffected by the temperature. Overall, magnitude and persistency of the temperatures reached in the conditions of this study do not seem to be great enough to have a large effect on male and female rabbit fertility.

Line and birth season effects on plasma testosterone and oxidative stress parameters in testis of maturing rabbits

Oxidative stress plays a key role in the male reproductive function. Differences between rabbit breeds have been found for testis size, seminiferous tubule diameter, number and size of interstitial and germ cells, etc. Traits related to the redox system could also be affected by genetic factors. It is likely that differences between breeds for these traits would lead to differences in reproductive maturation and fertility. We have investigated in the present paper the age-related changes of the plasma testosterone (TST) concentration, superoxide anion (O(2)(-)) radical formation, superoxide dismutase (SOD) activity, catalase (CAT) activity and thiobarbituric acid-reactive substances (TBARs) level in testis of rabbits in two breeds selected for different aptitudes (Caldes for growth rate and Prat for litter size). The effect of birth season for these traits was also assessed. Major changes in parameters related to oxidative stress were observed at an early age and most probably can be explained by the concomitant changes in testicular structure and function. Both lines showed similar developmental profiles and levels for all the variables studied. There was no interaction between line and birth season, consequently environmental conditions affected both lines in the same manner. Significant differences between males born in different seasons were found for O(2)(-) (4.84+/-0.19RLU/mg tissuemin versus 5.67+/-0.19RLU/mg tissuemin), SOD (6.12+/-0.11U/mg protein versus 7.09+/-0.11U/mg protein) and CAT (0.058+/-0.002K/mg protein versus 0.040+/-0.002K/mg protein). Future studies should take into account differences between seasons for a more precise analysis.

Reliable computing in estimation of variance components

The purpose of this study is to present guidelines in selection of statistical and computing algorithms for variance components estimation when computing involves software packages. For this purpose two major methods are to be considered: residual maximal likelihood (REML) and Bayesian via Gibbs sampling. Expectation-Maximization (EM) REML is regarded as a very stable algorithm that is able to converge when covariance matrices are close to singular, however it is slow. However, convergence problems can occur with random regression models, especially if the starting values are much lower than those at convergence. Average Information (AI) REML is much faster for common problems but it relies on heuristics for convergence, and it may be very slow or even diverge for complex models. REML algorithms for general models become unstable with larger number of traits. REML by canonical transformation is stable in such cases but can support only a limited class of models. In general, REML algorithms are difficult to program. Bayesian methods via Gibbs sampling are much easier to program than REML, especially for complex models, and they can support much larger datasets; however, the termination criterion can be hard to determine, and the quality of estimates depends on a number of details. Computing speed varies with computing optimizations, with which some large data sets and complex models can be supported in a reasonable time; however, optimizations increase complexity of programming and restrict the types of models applicable. Several examples from past research are discussed to illustrate the fact that different problems required different methods.