Ingrid David

Mass sperm motility is associated with fertility in sheep

The study was to focus on the relationship between wave motion (mass sperm motility, measured by a mass sperm motility score, manually assessed by artificial insemination (AI) center operators) and fertility in male sheep. A dataset of 711,562 artificial inseminations performed in seven breeds by five French AI centers during the 2001-2005 time period was used for the analysis. Factors influencing the outcome of the insemination, which is a binary response observed at lambing of either success (1) or failure (0), were studied using a joint model within each breed and AI center (eight separate analyses). The joint model is a multivariate model where all information related to the female, the male and the insemination process were included to improve the estimation of the factor effects. Results were consistent for all analyses. The male factors affecting AI results were the age of the ram and the mass motility. After correction for the other factors of variation, the lambing rate increased quasi linearly from three to more than ten points with the mass sperm motility score depending on the breed and the AI center. The consistency of the relationship for all breeds indicated that mass sperm motility is predictive of the fertility resulting when sperm are used from a specific ejaculate. Nonetheless, predictability could be improved if an objective measurement of mass sperm motility were available as a substitute for the subjective scoring currently in use in AI centers.

Genetic modeling of feed intake

With the development of automatic self-feeders and electronic identification, automated, repeated measurements of individual feed intake (FI) and BW are becoming available in more species. Consequently, genetic models for longitudinal data need to be applied to study FI or related traits. To handle this type of data, several flexible mixed-model approaches exist such as character process (CPr), structured antedependence (SAD), or random regression (RR) models. The objective of this study was to compare how these different approaches estimate both the covariance structure between successive measurements of FI and genetic parameters and their ability to predict future performances in 3 species (rabbits, ducks, and pigs). Results were consistent between species. It was found that the SAD and CPr models fit the data better than the RR models. Estimations of genetic and phenotypic correlation matrices were quite consistent between SAD and CPr models, whereas correlations estimated with the RR model were not. Structured antedependence and CPr models provided, as expected and in accordance with previous studies, a decrease of the correlations with the time interval between measurements. The changes in heritability with time showed the same trend for the SAD and RR models but not for the CPr model. Our results show that, in comparison with the CPr model, the SAD and RR models have the advantage of providing stable predictions of future phenotypes 1 wk forward whatever the number of observations used to estimate the parameters. Therefore, to study repeated measurements of FI, the SAD approach seems to be very appropriate in terms of genetic selection and real-time managements of animals.

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

BackgroundSome 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.MethodsThe single-trait SAD model assumes that a random effect at time

New objective measurements of semen wave motion are associated with fertility in sheep

t_{j}

Genetic Parameters for Resistance to Non-specific Diseases and Production Traits Measured in Challenging and Selection Environments; Application to a Rabbit Case

tj 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.ResultsFor 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).ConclusionsWe 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.

Genetic structured antedependence and random regression models applied to the longitudinal feed conversion ratio in growing Large White pigs1

Selection for disease resistance is a powerful way to improve the health status of herds and to reduce the use of antibiotics. The objectives of this study were to estimate 1) the genetic parameters for simple visually assessed disease syndromes and for a composite trait of resistance to infectious disease including all syndromes and 2) their genetic correlations with production traits in a rabbit population. Disease symptoms were recorded in the selection herds of 2 commercial paternal rabbit lines during weighing at the end of the test (63 and 70 d of age, respectively). Causes of mortality occurring before these dates were also recorded. Seven disease traits were analyzed: 3 elementary traits visually assessed by technicians on farm (diarrhea, various digestive syndromes, and respiratory syndromes), 2 composite traits (all digestive syndromes and all infectious syndromes), and 2 mortality traits (digestive mortality and infectious mortality). Each animal was assigned only 1 disease trait, corresponding to the main syndrome ( = 153,400). Four production traits were also recorded: live weight the day before the end of test on most animals ( = 137,860) and cold carcass weight, carcass yield, and perirenal fat percentage of the carcass on a subset of slaughtered animals ( = 13,765). Records on both lines were analyzed simultaneously using bivariate linear animal models after validation of consistency with threshold models applied to logit-transformed traits. The heritabilities were low for disease traits, from 0.01 ± 0.002 for various digestive syndromes to 0.04 ± 0.004 for infectious mortality, and moderate to high for production traits. The genetic correlations between digestive syndromes were high and positive, whereas digestive and respiratory syndromes were slightly negatively correlated. The genetic correlations between the composite infectious disease trait and digestive or respiratory syndromes were moderate. Genetic correlations between disease and production traits were favorable. Our results indicate that it is possible to select rabbits using visually assessed disease syndromes without the need for a trade-off between health and production traits. Using a composite criterion that includes all infectious syndromes is easy to implement and heritable and is, therefore, a promising way to improve the general disease resistance in livestock species.

Genetic heteroscedastic models for ordinal traits: application to sheep litter size

In sheep, wave motion in semen is currently used by AI centres to select ejaculates for insemination. Despite its low cost, convenience and established ability to predict fertility, the subjectivity of this assessment is a limiting factor for its applicability. The aims of the present study were to establish an objective method for the analysis of wave motion and to assess the associations of objective parameters with fertility after cervical insemination. Collective sperm motion in undiluted semen was observed by phase contrast microscopy at low magnification in a 100-µm deep glass chamber. Images of moving dark waves over a grey background were recorded and analysed by the optic flow method, producing several velocity-related parameters. Turbulence was assessed from the motion of fluorescent polystyrene beads. Among objective parameters, optical flow entropy and the average speed of beads were both able to discriminate ejaculates suitable for insemination. Two synthetic variables of optic flow and bead motion and a global objective variable were computed from linear combinations of individual parameters and compared with the subjective motion score for their predictive value. These were as efficient as the wave motion score for assessing fertility and can be proposed for the assessment of ram semen in routine AI procedures.

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

Type traits and mammary health traits are important to dairy ruminant breeding because they influence animal health, milking ability, and longevity, as well as the economic sustainability of farms. The availability of the genomic sequence and a single nucleotide polymorphism chip in goats has opened up new fields of investigation to better understand the genes and mechanisms that underlie such complex traits and to be able to select them. Our objective was to perform a genome-wide association study in dairy goats for 11 type traits and somatic cell count (SCC) as proxies for mastitis resistance. A genome-wide association study was implemented using a daughter design composed of 1,941 Alpine and Saanen goats sired by 20 artificial insemination bucks, genotyped with the Illumina GoatSNP50 BeadChip (Illumina Inc., San Diego, CA). This association study was based on both linkage analyses and linkage disequilibrium using QTLmap software (http://dga7.jouy.inra.fr/qtlmap/) interval mapping was performed with the likelihood ratio test using linear regressions. Breeds were analyzed together and separately. The study highlighted 37 chromosome-wide significant quantitative trait loci (QTL) with linkage analyses and 222 genome-wide significant QTL for linkage disequilibrium, for type and SCC traits in dairy goats. Genomic control of those traits was mostly polygenic and breed-specific, suggesting that within-breed selection would be favored for those traits. Of note, Capra hircus autosome (CHI) 19 appeared to be highly enriched in single nucleotide polymorphisms associated with type and SCC, with 2 highly significant regions in the Saanen breed. One region (33-42 Mb) was significantly associated with SCC and includes candidate genes associated with response to intramammary infections (RARA, STAT3, STAT5A, and STAT5B). Another region of the CHI 19 (24.5-27 Mb) exhibited an adverse pleiotropic effect on milk production (milk, fat yield, and protein yield) and udder traits (udder floor position and rear udder attachment) that agreed with the negative genetic correlations that exist between those 2 groups of traits. These QTL were not found in the Alpine breed. In Alpine, the 2 most significant regions were associated with chest depth on CHI 6 (45.8-46.0 Mb) and CHI 8 (80.7-81.1 Mb). These results will be helpful for goat selection in the future and could lead to identification of causal mutations.