Genetic improvement of laying hens viability using survival analysis

The survival of about eight generations of a large strain of laying hens was analysed separating the rearing period (RP) from the production period (PP), after hens were housed. For RP (respectively PP), 97.8% (resp., 94.1% ) of the 109 160 (resp., 100 665) female records were censored after 106 days (resp., 313 days) on the average. A Cox proportional hazards model stratified by flock (= season) and including a hatch-within-flock (HWF) fixed effect seemed to reasonably fit the RP data. For PP, this model could be further simplified to a non-stratified Weibull model. The extension of these models to sire-dam frailty (mixed) models permitted the estimation of the sire genetic variances at 0.261 ± 0.026 and 0.088 ± 0.010 for RP and PP, respectively. Heritabilities on the log scale were equal to 0.48 and 0.19. Non-additive genetic effects could not be detected. Selection was simulated by evaluating all sires and dams, after excluding all records from the last generation. Then, actual parents of this last generation were distributed into four groups according to their own pedigree index. Raw survivor curves of the progeny of extreme parental groups substantially differed (e.g., by 1.7% at 300 days for PP), suggesting that selection based on solutions from the frailty models could be efficient, despite the very large proportion of censored records

Validation of an approximate approach to compute genetic correlations between longevity and linear traits

The estimation of genetic correlations between a nonlinear trait such as longevity and linear traits is computationally difficult on large datasets. A two-step approach was proposed and was checked via simulation. First, univariate analyses were performed to get genetic variance estimates and to compute pseudo-records and their associated weights. These pseudo-records were virtual performances free of all environmental effects that can be used in a BLUP animal model, leading to the same breeding values as in the (possibly nonlinear) initial analyses. By combining these pseudo-records in a multiple trait model and fixing the genetic and residual variances to their values computed during the first step, we obtained correlation estimates by AI-REML and approximate MT-BLUP predicted breeding values that blend direct and indirect information on longevity. Mean genetic correlations and reliabilities obtained on simulated data confirmed the suitability of this approach in a wide range of situations. When nonzero residual correlations exist between traits, a sire model gave nearly unbiased estimates of genetic correlations, while the animal model estimates were biased upwards. Finally, when an incorrect genetic trend was simulated to lead to biased pseudo-records, a joint analysis including a time effect could adequately correct for this bias

Estimation of genetic parameters for test day records of dairy traits in the first three lactations

Application of test-day models for the genetic evaluation of dairy populations requires the solution of large mixed model equations. The size of the (co)variance matrices required with such models can be reduced through the use of its first eigenvectors. Here, the first two eigenvectors of (co)variance matrices estimated for dairy traits in first lactation were used as covariables to jointly estimate genetic parameters of the first three lactations. These eigenvectors appear to be similar across traits and have a biological interpretation, one being related to the level of production and the other to persistency. Furthermore, they explain more than 95% of the total genetic variation. Variances and heritabilities obtained with this model were consistent with previous studies. High correlations were found among production levels in different lactations. Persistency measures were less correlated. Genetic correlations between second and third lactations were close to one, indicating that these can be considered as the same trait. Genetic correlations within lactation were high except between extreme parts of the lactation. This study shows that the use of eigenvectors can reduce the rank of (co)variance matrices for the test-day model and can provide consistent genetic parameters

Effects of a national genomic preselection on the international genetic evaluations

AbstractGenomic preselection of young bulls is now widely implemented in dairy breeding schemes, especially in the Holstein breed. However, if this step is not accounted for in genetic evaluation models, the national breeding values of bulls retained by a genomic preselection and of their progeny are estimated with bias. It follows that countries participating in international genetic evaluations will provide a selected and possibly biased set of data to the Interbull Centre (Swedish University of Agricultural Sciences, Uppsala, Sweden). The objective of the study was to show evidence of bias at the international level due to a genomic preselection step in national breeding schemes. The consequence of a genomic preselection for the international evaluations (i.e., using selected and biased national estimated breeding values) was simulated using actual national estimated breeding values as a proxy for genomically enhanced breeding values. Data were provided for 3 countries with a large population of Holstein bulls. International breeding values from simulated scenarios were compared with international breeding values using all available data, assumed to be complete and unbiased. Bias was measured among young bulls retained by a genomic preselection and their contemporaries in other countries. The results were analyzed by traits measured within each country and by country of origin of the young bulls. It turned out that sending preselected data, though based on genomic information, created bias in international evaluations, penalizing young bulls from the country sending the incorrect data. It also had an effect on the young bulls from the other countries. Sending biased data further affected the quality of international evaluations. This study underlines the importance of accounting for genomic preselection at the national level first. Moreover, submitting all available data appeared essential to maintain the quality of the international genetic evaluations after implementation of a genomic preselection step

The cases of June 2000, November 2002 and September 2002 as examples of Mediterranean floods

International audienceFour flood events that affected three different countries are here described in terms of meteorological genesis and in terms of consequences on the population and on the territory. Each event is a good representative of a class of phenomena that produce important effects on the urban and extra-urban tissue and that must be taken into account in an optic of civil protection and risk evaluation. This is the subject of the HYDROPTIMET project, part of the Interreg IIIB program, which is collocated in the framework of the prevention of natural hazards and, in particular, those related to severe meteo-hydrological events. This paper aims at being a general introduction of the four events which are the subject of more detailed studies, already published or under submission

Relationships between type and longevity in the Holstein breed

The relationship between type traits and longevity was studied in the French Holstein breed using a survival analysis model. In this model, the phenotypic value adjusted for systematic fixed effects, the estimated breeding value, or the residual value (defined as the difference between the adjusted phenotypic value and the estimated breeding value) of the cow for each type trait was included as a risk factor. This was done separately for two subpopulations (registered and nonregistered herds) and with or without adjustment for production traits, i.e., considering true or functional longevity. For both types of herds, udder traits (and above all, udder depth) clearly influenced the length of productive life. There seemed to be a more pronounced voluntary culling on type traits in registered herds. The correction for the within herd-year class of production traits, as a way to approximate functional longevity, increased the importance of udder traits and decreased the weight of capacity traits. The same results were obtained when the phenotypic value of the cow for type was replaced by her estimated breeding value, whereas residuals had little impact. The relationship between longevity and type traits was most often nonlinear, in particular for udder traits, but in this study, no trait with a clear intermediate optimum was found

An approximate multitrait model for genetic evaluation in dairy cattle with a robust estimation of genetic trends (Open Access publication)

In a stochastic simulation study of a dairy cattle population three multitrait models for estimation of genetic parameters and prediction of breeding values were compared. The first model was an approximate multitrait model using a two-step procedure. The first step was a single trait model for all traits. The solutions for fixed effects from these analyses were subtracted from the phenotypes. A multitrait model only containing an overall mean, an additive genetic and a residual term was applied on these preadjusted data. The second model was similar to the first model, but the multitrait model also contained a year effect. The third model was a full multitrait model. Genetic trends for total merit and for the individual traits in the breeding goal were compared for the three scenarios to rank the models. The full multitrait model gave the highest genetic response, but was not significantly better than the approximate multitrait model including a year effect. The inclusion of a year effect into the second step of the approximate multitrait model significantly improved the genetic trend for total merit. In this study, estimation of genetic parameters for breeding value estimation using models corresponding to the ones used for prediction of breeding values increased the accuracy on the breeding values and thereby the genetic progress

An approximate multitrait model for genetic evaluation in dairy cattle with a robust estimation of genetic trends

In a stochastic simulation study of a dairy cattle population three multitrait models for estimation of genetic parameters and prediction of breeding values were compared. The first model was an approximate multitrait model using a two-step procedure. The first step was a single trait model for all traits. The solutions for fixed effects from these analyses were subtracted from the phenotypes. A multitrait model only containing an overall mean, an additive genetic and a residual term was applied on these preadjusted data. The second model was similar to the first model, but the multitrait model also contained a year effect. The third model was a full multitrait model. Genetic trends for total merit and for the individual traits in the breeding goal were compared for the three scenarios to rank the models. The full multitrait model gave the highest genetic response, but was not significantly better than the approximate multitrait model including a year effect. The inclusion of a year effect into the second step of the approximate multitrait model significantly improved the genetic trend for total merit. In this study, estimation of genetic parameters for breeding value estimation using models corresponding to the ones used for prediction of breeding values increased the accuracy on the breeding values and thereby the genetic progress

Data transformation for rank reduction in multi-trait MACE model for international bull comparison

Since many countries use multiple lactation random regression test day models in national evaluations for milk production traits, a random regression multiple across-country evaluation (MACE) model permitting a variable number of correlated traits per country should be used in international dairy evaluations. In order to reduce the number of within country traits for international comparison, three different MACE models were implemented based on German daughter yield deviation data and compared to the random regression MACE. The multiple lactation MACE model analysed daughter yield deviations on a lactation basis reducing the rank from nine random regression coefficients to three lactations. The lactation breeding values were very accurate for old bulls, but not for the youngest bulls with daughters with short lactations. The other two models applied principal component analysis as the dimension reduction technique: one based on eigenvalues of a genetic correlation matrix and the other on eigenvalues of a combined lactation matrix. The first one showed that German data can be transformed from nine traits to five eigenfunctions without losing much accuracy in any of the estimated random regression coefficients. The second one allowed performing rank reductions to three eigenfunctions without having the problem of young bulls with daughters with short lactations