Genetic parameters for first lactation dairy traits in the Alpine and Saanen goat breeds using a random regression test-day model

International audienceAbstractBackgroundRandom regression models (RRM) are widely used to analyze longitudinal data in genetic evaluation systems because they can better account for time-course changes in environmental effects and additive genetic values of animals by fitting the test-day (TD) specific effects. Our objective was to implement a random regression model for the evaluation of dairy production traits in French goats.ResultsThe data consisted of milk TD records from 30,186 and 32,256 first lactations of Saanen and Alpine goats. Milk yield, fat yield, protein yield, fat content and protein content were considered. Splines were used to model the environmental factors. The genetic and permanent environmental effects were modeled by the same Legendre polynomials. The goodness-of-fit and the genetic parameters derived from functions of the polynomials of orders 0 to 4 were tested. Results were also compared to those from a lactation model with total milk yield calculated over 250 days and to those of a multiple-trait model that considers performance in six periods throughout lactation as different traits. Genetic parameters were consistent between models. Models with fourth-order Legendre polynomials led to the best fit of the data. In order to reduce complexity, computing time, and interpretation, a rank reduction of the variance covariance matrix was performed using eigenvalue decomposition. With a reduction to rank 2, the first two principal components correctly summarized the genetic variability of milk yield level and persistency, with a correlation close to 0 between them.ConclusionsA random regression model was implemented in France to evaluate and select goats for yield traits and persistency, which are independent i.e. no genetic correlation between them, in first lactation

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

The performance of routine ultrasonographic screening of pregnancies in the Eurofetus Study

OBJECTIVES: The purpose of the Eurofetus Study was to evaluate the accuracy of the antenatal detection of malformations by routine ultrasonographic examination in unselected populations. STUDY DESIGN: All ultrasonographic diagnoses of malformations and the outcomes of the fetuses were prospectively recorded in 61 European obstetric units over a 3-year period (1990-1993). Also recorded were all cases of malformation diagnosed after abortion or birth for the mothers who underwent follow-up in these centers. RESULTS: Of 3685 malformed fetuses, 2262 had received diagnoses during pregnancy (sensitivity, 61.4%). Of a total number of 4615 malformations, 2593 were detected (sensitivity, 56.2%). The detection sensitivity was higher for the major than for the minor abnormalities (73.7% vs 45.7%), and the diagnosis was made earlier in the pregnancy (24.2 weeks vs 27.6, P < .01). Overall, 55% of the major abnormalities were detected within 24 gestational weeks. Within each severity group the accuracy of detection depended on the system. For the major abnormalities it was better for the central nervous system (88.3%) and urinary tract (84.8%) but lower for the heart and great vessels (38.8%). Detection of minor abnormalities was also effective for the urinary tract (89.1%) but not for the heart and great vessels (20.8%) or the musculoskeletal system (18%). Detection of abnormalities had an influence on the rate of termination of pregnancy. The rate of live births for the mothers bearing fetuses with major abnormalities was lower than that for the mothers in whom no abnormalities were detected, mainly because of the higher rate of elective terminations of pregnancy in the former group. CONCLUSION: Systematic ultrasonographic screening during pregnancy can now detect a large proportion of fetal malformations, although some still escape detection.SCOPUS: cp.jinfo:eu-repo/semantics/publishe

Weighted single-step genomic BLUP improves accuracy of genomic breeding values for protein content in French dairy goats: a quantitative trait influenced by a major gene

Abstract Background In 2017, genomic selection was implemented in French dairy goats using the single-step genomic best linear unbiased prediction (ssGBLUP) method, which assumes that all single nucleotide polymorphisms explain the same fraction of genetic variance. However, ssGBLUP is not suitable for protein content, which is controlled by a major gene, i.e. α s 1 casein. This gene explains about 40% of the genetic variation in protein content. In this study, we evaluated the accuracy of genomic prediction using different genomic methods to include the effect of the α s 1 casein gene. Methods Genomic evaluation for protein content was performed with data from the official genetic evaluation on 2955 animals genotyped with the Illumina goat SNP50 BeadChip, 7202 animals genotyped at the α s 1 casein gene and 6,767,490 phenotyped females. Pedigree-based BLUP was compared with regular unweighted ssGBLUP and with three weighted ssGBLUP methods (WssGBLUP, WssGBLUPMax and WssGBLUPSum), which give weights to SNPs according to their effect on protein content. Two other methods were also used: trait-specific marker-derived relationship matrix (TABLUP) using pre-selected SNPs associated with protein content and gene content based on a multiple-trait genomic model that includes α s 1 casein genotypes. We estimated accuracies of predicted genomic estimated breeding values (GEBV) in two populations of goats (Alpine and Saanen). Results Accuracies of GEBV with ssGBLUP improved by + 5 to + 7 percent points over accuracies from the pedigree-based BLUP model. With the WssGBLUP methods, SNPs that are located close to the α s 1 casein gene had the biggest weights and contributed substantially to the capture of signals from quantitative trait loci. Improvement in accuracy of genomic predictions using the three weighted ssGBLUP methods delivered up to + 6 percent points of accuracy over ssGBLUP. A similar accuracy was obtained for ssGBLUP and TABLUP considering the 20,000 most important SNPs. Incorporating information on the α s 1 casein genotypes based on the gene content method gave similar results as ssGBLUP. Conclusions The three weighted ssGBLUP methods were efficient for detecting SNPs associated with protein content and for a better prediction of genomic breeding values than ssGBLUP. They also combined fast computing, simplicity and required ssGBLUP to be run only twice

Detection of chromosomal abnormalities, an outcome of ultrasound screening

Chromosomal abnormalities were recorded from all the fetuses of women who benefited from sonographic examinations in the Eurofetus centers, excluding those for whom karyotyping was motivated by age or personal history. Among the 378 chromosomal abnormalities recorded, 210 were detected before birth (sensitivity = 55.6%). Down syndrome (trisomy 21) represented 197 cases, of which 68 were detected before birth (sensitivity = 34.5%). Eighty-two of the cases of Down syndrome had associated structural abnormalities; the sensitivity in these cases increased to 57%. Among the 115 cases of Down syndrome without structural abnormalities, 21 (18.3%) had associated abnormal ultrasound findings that led to prenatal detection. Sensitivity of prenatal detection was 58.1% for trisomy 13 and 79% for trisomy 18. For the abnormalities detected before birth, spontaneous fetal death occurred in 27% of cases, and an early termination of pregnancy was decided in 53% of cases.SCOPUS: cp.kFLWINinfo:eu-repo/semantics/publishe

Genetic parameters for milk production and type traits in North American and European Alpine and Saanen dairy goat populations

The development of an across-country genomic evaluation scheme is a promising alternative for enlarging reference populations and successfully implementing genomic selection in small ruminant populations. However, the feasibility of such evaluations depends on the genetic similarity among the populations, and therefore, high connectedness and high genetic correlations between the traits recorded in different countries or populations are needed. In this study, we evaluated the feasibility of performing an across-country genomic evaluation for milk production and type traits in Alpine and Saanen goats from Canada, France, Italy, and Switzerland. Variance components and genetic parameters, including genetic correlations between traits recorded in different countries, were calculated using combined phenotypes, genotypes, and pedigree datasets. The (co)variance component analyses were performed within breed, either based only on pedigree information or also incorporating genomic information. Across-country genetic parameters were calculated for 3 representative traits (i.e., milk yield, fat content, and rear udder attachment). The heritability estimates ranged from 0.10 to 0.50, which are consistent with previous estimates reported in the literature. The genetic correlations for rear udder attachment ranged from 0.75 (between France and Italy, for the Alpine breed without genomic information) to 0.95 (between Canada and France, for the Saanen breed with genomic information), whereas for fat content, between France and Italy, they ranged from 0.75 in the Alpine breed without genomic information to 0.78 in the Alpine breed with genomic information. However, genetic correlations for milk yield were only estimable between France and Italy, with a moderate value of 0.45 for the Alpine breed with or without genomic information, and of 0.22 and 0.26 in the Saanen breed with and without genomic information, respectively. These low genetic correlations for milk yield could be due to several factors, including the trait definition in each country and genotype-by-environment interactions (GxE). The high genetic correlations found for fat content and rear udder attachment indicate that these traits might be more standardized across countries and less affected by GxE effects. Thus, an international genomic evaluation for these traits might be feasible. Further studies should be performed to understand the surprisingly lower genetic correlations between milk yield across countries. Furthermore, additional efforts should be made to increase the genetic connection among the Alpine and Saanen goat populations in the 4 countries included in the analyses

Using sequence variants of a QTL region improves the accuracy of genomic evaluation in French Saanen goats

International audienc

Use of genetic algorithm on mid-infrared spectrometric data: application to estimate the fatty acids profile of goat milk

To know and to control the fine milk composition is an important concern in the dairy industry. The mid-infrared (MIR) spectrometry method appears to be a good, fast and cheap method for assessing milk fatty acid profile with accuracy. Although partial least squares (PLS) regression is a very useful and powerful method to determine fine milk composition from spectra, the estimations are often less accurate on new samples coming from different spectrometers. Therefore a genetic algorithm (GA) combined with a PLS was used to produce models with a reduced number of wavelengths and a better accuracy. Number of wavelengths to consider is reduced substantially by 5 or 10 according the number of steps in the genetic algorithm. The accuracy is increased on average by 9% for fatty acids of interest