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Le Touquet – Paris-Plage, France

Lund M.S.,University of Aarhus | De Roos A.P.,Crv Inc. | De Vries A.G.,Crv Inc. | Druet T.,University of Liege | And 10 more authors.
Genetics Selection Evolution | Year: 2011

Background: Size of the reference population and reliability of phenotypes are crucial factors influencing the reliability of genomic predictions. It is therefore useful to combine closely related populations. Increased accuracies of genomic predictions depend on the number of individuals added to the reference population, the reliability of their phenotypes, and the relatedness of the populations that are combined. Methods. This paper assesses the increase in reliability achieved when combining four Holstein reference populations of 4000 bulls each, from European breeding organizations, i.e. UNCEIA (France), VikingGenetics (Denmark, Sweden, Finland), DHV-VIT (Germany) and CRV (The Netherlands, Flanders). Each partner validated its own bulls using their national reference data and the combined data, respectively. Results: Combining the data significantly increased the reliability of genomic predictions for bulls in all four populations. Reliabilities increased by 10%, compared to reliabilities obtained with national reference populations alone, when they were averaged over countries and the traits evaluated. For different traits and countries, the increase in reliability ranged from 2% to 19%. Conclusions: Genomic selection programs benefit greatly from combining data from several closely related populations into a single large reference population. © 2011 Lund et al; licensee BioMed Central Ltd. Source

Dassonneville R.,French National Institute for Agricultural Research | Dassonneville R.,Institute Of Lelevage | Fritz S.,UNCEIA | Ducrocq V.,French National Institute for Agricultural Research | Boichard D.,French National Institute for Agricultural Research
Journal of Dairy Science | Year: 2012

Low-density chips are appealing alternative tools contributing to the reduction of genotyping costs. Imputation enables researchers to predict missing genotypes to recreate the denser coverage of the standard 50K (∼50,000) genotype. Two alternative in silico chips were defined in this study that included markers selected to optimize minor allele frequency and spacing. The objective of this study was to compare the imputation accuracy of these custom low-density chips with a commercially available 3K chip. Data consisted of genotypes of 4,037 Holstein bulls, 1,219 Montbéliarde bulls, and 991 Blonde d'Aquitaine bulls. Criteria to select markers to include in low-density marker panels are described. To mimic a low-density genotype, all markers except the markers present on the low-density panel were masked in the validation population. Imputation was performed using the Beagle software. Combining the directed acyclic graph obtained with Beagle with the PHASEBOOK algorithm provides fast and accurate imputation that is suitable for routine genomic evaluations based on imputed genotypes. Overall, 95 to 99% of alleles were correctly imputed depending on the breed and the low-density chip used. The alternative low-density chips gave better results than the commercially available 3K chip. A low-density chip with 6,000 markers is a valuable genotyping tool suitable for both dairy and beef breeds. Such a tool could be used for preselection of young animals or large-scale screening of the female population. © 2012 American Dairy Science Association. Source

Bruyere P.,VetAgro Sup | Baudot A.,University of Paris Descartes | Guyader-Joly C.,UNCEIA | Guerin P.,VetAgro Sup | And 2 more authors.
Theriogenology | Year: 2012

This study evaluates a new synthetic substitute (CRYO3, Ref. 5617, Stem Alpha, France) for animal-based products in bovine embryo cryopreservation solutions. During the experiment, fetal calf serum (FCS) and bovine serum albumin (BSA) were used as references. A combination of a thermodynamic approach using differential scanning calorimetry and a biological approach using in vitro-produced bovine embryo slow-freezing was used to characterize cryopreservation solutions containing CRYO3, FCS and BSA. The CRYO3 and fetal calf serum (FCS) slow-freezing solutions were made from Dulbecco's phosphate-buffered saline containing 1.5 m ethylene glycol, 0.1 m sucrose and 20% (v.v-1) of CRYO3 or FCS. The bovine serum albumin (BSA) solution was made by adding 0.1 m sucrose to a commercial solution containing 1.5 m ethylene glycol and 4 g L-1 BSA. These solutions were evaluated using three characteristics: the end of melting temperature, the enthalpy of crystallization (thermodynamic approach) and the embryo survival and hatching rates after in vitro culture (biological approach). The CRYO3 and FCS solutions had similar thermodynamic properties. In contrast, the thermodynamic characteristics of the BSA solution were different from those of the FCS and CRYO3 solutions. Nevertheless, the embryo survival and hatching rates obtained with the BSA and FCS solutions were not different. Similar biological properties can thus be obtained with slow freezing solutions that have different physical properties within a defined range. The embryo survival rate after 48 h of in vitro culture obtained with the CRYO3 solution (81.5%) was higher than that obtained with the BSA (42.2%, P = 0.000 12) and FCS solutions (58%, P = 0.016). Similarly, the embryo hatching rate after 72 h of in vitro culture was higher with the CRYO3 solution (61.1%) than with the BSA (31.1%, P = 0.0055) and FCS solutions (36%, P = 0.018). We conclude that CRYO3 can be used as a chemically defined substitute for animal-based products in in vitro-produced bovine embryo cryopreservation solutions. © 2012 Elsevier Inc.. Source

Van Den Berg I.,French National Institute for Agricultural Research | Van Den Berg I.,Agro ParisTech | Van Den Berg I.,University of Aarhus | Fritz S.,UNCEIA | And 2 more authors.
Genetics Selection Evolution | Year: 2013

Background: Accurate QTL mapping is a prerequisite in the search for causative mutations. Bayesian genomic selection models that analyse many markers simultaneously should provide more accurate QTL detection results than single-marker models. Our objectives were to (a) evaluate by simulation the influence of heritability, number of QTL and number of records on the accuracy of QTL mapping with Bayes Cπ and Bayes C; (b) estimate the QTL status (homozygous vs. heterozygous) of the individuals analysed. This study focussed on the ten largest detected QTL, assuming they are candidates for further characterization. Methods. Our simulations were based on a true dairy cattle population genotyped for 38 277 phased markers. Some of these markers were considered biallelic QTL and used to generate corresponding phenotypes. Different numbers of records (4387 and 1500), heritability values (0.1, 0.4 and 0.7) and numbers of QTL (10, 100 and 1000) were studied. QTL detection was based on the posterior inclusion probability for individual markers, or on the sum of the posterior inclusion probabilities for consecutive markers, estimated using Bayes C or Bayes Cπ. The QTL status of the individuals was derived from the contrast between the sums of the SNP allelic effects of their chromosomal segments. Results: The proportion of markers with null effect (π) frequently did not reach convergence, leading to poor results for Bayes Cπ in QTL detection. Fixing π led to better results. Detection of the largest QTL was most accurate for medium to high heritability, for low to moderate numbers of QTL, and with a large number of records. The QTL status was accurately inferred when the distribution of the contrast between chromosomal segment effects was bimodal. Conclusions: QTL detection is feasible with Bayes C. For QTL detection, it is recommended to use a large dataset and to focus on highly heritable traits and on the largest QTL. QTL statuses were inferred based on the distribution of the contrast between chromosomal segment effects. © 2013 van den Berg et al.; licensee BioMed Central Ltd. Source

Colombani C.,French National Institute for Agricultural Research | Croiseau P.,French National Institute for Agricultural Research | Fritz S.,UNCEIA | Guillaume F.,Institute Of Lelevage | And 3 more authors.
Journal of Dairy Science | Year: 2012

Genomic selection involves computing a prediction equation from the estimated effects of a large number of DNA markers based on a limited number of genotyped animals with phenotypes. The number of observations is much smaller than the number of independent variables, and the challenge is to find methods that perform well in this context. Partial least squares regression (PLS) and sparse PLS were used with a reference population of 3,940 genotyped and phenotyped French Holstein bulls and 39,738 polymorphic single nucleotide polymorphism markers. Partial least squares regression reduces the number of variables by projecting independent variables onto latent structures. Sparse PLS combines variable selection and modeling in a one-step procedure. Correlations between observed phenotypes and phenotypes predicted by PLS and sparse PLS were similar, but sparse PLS highlighted some genome regions more clearly. Both PLS and sparse PLS were more accurate than pedigree-based BLUP and generally provided lower correlations between observed and predicted phenotypes than did genomic BLUP. Furthermore, PLS and sparse PLS required similar computing time to genomic BLUP for the study of 6 traits. © 2012 American Dairy Science Association. Source

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