The Experts below are selected from a list of 113496 Experts worldwide ranked by ideXlab platform
Georgios Banos - One of the best experts on this subject based on the ideXlab platform.
-
Genetic Evaluation for bovine tuberculosis resistance in dairy cattle
Journal of Dairy Science, 2017Co-Authors: Georgios Banos, M Winters, R Mrode, Andrew Mitchell, Stephen Bishop, John Woolliams, M P CoffeyAbstract:ABSTRACT Genetic Evaluations for resistance to bovine tuberculosis (bTB) were calculated based on British national data including individual animal tuberculin skin test results, postmortem examination (presence of bTB lesions and bacteriological culture for Mycobacterium bovis ), animal movement and location information, production history, and pedigree records. Holstein cows with identified sires in herds with bTB breakdowns (new herd incidents) occurring between the years 2000 and 2014 were considered. In the first instance, cows with a positive reaction to the skin test and a positive postmortem examination were defined as infected. Values of 0 and 1 were assigned to healthy and infected animal records, respectively. Data were analyzed with mixed models. Linear and logit function heritability estimates were 0.092 and 0.172, respectively. In subsequent analyses, breakdowns were split into 2-mo intervals to better model time of exposure and infection in the contemporary group. Intervals with at least one infected individual were retained and multiple intervals within the same breakdown were included. Healthy animal records were assigned values of 0, and infected records a value of 1 in the interval of infection and values reflecting a diminishing probability of infection in the preceding intervals. Heritability and repeatability estimates were 0.115 and 0.699, respectively. Reliabilities and across time stability of the Genetic Evaluation were improved with the interval model. Subsequently, 2 more definitions of "infected" were analyzed with the interval model: (1) all positive skin test reactors regardless of postmortem examination, and (2) all positive skin test reactors plus nonreactors with positive postmortem examination. Estimated heritability was 0.085 and 0.089, respectively; corresponding repeatability estimates were 0.701 and 0.697. Genetic Evaluation reliabilities and across time stability did not change. Correlations of Genetic Evaluations for bTB with other traits in the current breeding goal were mostly not different from zero. Correlation with the UK Profitable Lifetime Index was moderate, significant, and favorable. Results demonstrated the feasibility of a national Genetic Evaluation for bTB resistance. Selection for enhanced resistance will have a positive effect on profitability and no antagonistic effects on current breeding goal traits. Official Genetic Evaluations are now based on the interval model and the last bTB trait definition.
-
An alarm firing system for national Genetic Evaluation quality control
Interbull Bulletin, 2004Co-Authors: Sotiris Diplaris, Andreas L. Symeonidis, Pericles A. Mitkas, Georgios Banos, Z. AbasAbstract:National Genetic Evaluation results form the basis of Interbull services. The current method for quality assurance is mainly determined by the consistency of consecutive Evaluation results and is based on thorough statistical examination (Klei et al., 2002). In a separate project, national Genetic Evaluation programs are being tested on simulated data sets with known properties (Taubert et al., 2002). Datamining (DM) offers an alternative way to examine data and extract valuable information (Han and Kamber, 2000), potentially leading to inference on data quality. In a recent progress report, the development of a DM algorithm for the analysis of national Genetic Evaluation results was presented (Banos et al., 2003). Data quality was assessed by subjective inspection of DM results. The present study introduces a method to evaluate DM application results with objective criteria leading, when necessary, to the automatic issuing of warnings or alarm signals.
-
Quality control of national Genetic Evaluation results using data-mining techniques; a progress report
Interbull Bulletin, 2003Co-Authors: Georgios Banos, Andreas L. Symeonidis, Pericles A. Mitkas, Z. Abas, G. Milis, U EmanuelsonAbstract:Data quality constitutes one of the most critical issues in Genetic Evaluations both at national and international level. International Genetic Evaluations computed by Interbull are based on the analysis of national Genetic Evaluation results. Therefore, the validity of international comparisons depends on the quality of the output of the various national Genetic Evaluation systems. The current method for data quality assurance is mainly determined by the consistency of consecutive Evaluation results and is based on thorough statistical examination (Klei et al., 2002). In a separate project, national Genetic Evaluation programs are being tested on simulated datasets with known properties (Taubert et al., 2002).
-
Impact of national Genetic Evaluation models on international comparisons
1999Co-Authors: U Emanuelson, W F Fikse, Georgios BanosAbstract:Data quality has always a profound effect on the result of statistical analyses, and international Genetic comparisons are no exceptions. Main factors affecting data quality in this context are completeness and accuracy of data recording and pedigree information, and methods of estimation of breeding values on the national level. Currently, there is large variation in the procedures applied in national Genetic Evaluations, both in terms of model specification and computational methods. Thus, breeding values used in the Interbull routine Evaluation of Holstein bulls in February 1999 originated from multiple-trait test-day animal models (AM) and lactational AM, as well as single-trait lactational repeatability AM and sire models, represented by two, three, fourteen, and two countries, respectively. Also, heritabilities used in the national Evaluations ranged from 0.23 to 0.42. Differences in national Genetic Evaluation models may be one source of variation contributing to differences between countries, indicated by Genetic correlations of less than unity in international comparisons. Such effects would, however, be difficult to isolate from "true" GxE interactions in the framework of MACE. Nevertheless, changes over time in correlations between countries could be used to study the impact of different models when concurrent changes in national Evaluations have occurred. Major changes in national Evaluation models during the last couple of years have, depending on the nature of the changes, been seen to both decrease and to increase correlations. Changes in national models do not only affect correlations with other countries, but also sire variance estimates and selection differentials within and across countries. Changing Genetic parameters may have considerable impact on the international ranking of bulls. For example, decreasing correlations of any country with the rest would result in more domestic bulls on the top of the local scale list, but less bulls from this country on the top of foreign scale lists. Such and other changes are used to illustrate the impact of national Genetic Evaluation changes on international comparisons.
Joel N. Hirschhorn - One of the best experts on this subject based on the ideXlab platform.
-
Genetic Evaluation of Short Stature
The Journal of clinical endocrinology and metabolism, 2014Co-Authors: Andrew Dauber, Ron G. Rosenfeld, Joel N. HirschhornAbstract:Context: Genetics plays a major role in determining an individual's height. Although there are many monogenic disorders that lead to perturbations in growth and result in short stature, there is still no consensus as to the role that Genetic diagnostics should play in the Evaluation of a child with short stature. Evidence Acquisition: A search of PubMed was performed, focusing on the Genetic diagnosis of short stature as well as on specific diagnostic subgroups included in this article. Consensus guidelines were reviewed. Evidence Synthesis: There are a multitude of rare Genetic causes of severe short stature. There is no high-quality evidence to define the optimal approach to the Genetic Evaluation of short stature. We review Genetic etiologies of a number of diagnostic subgroups and propose an algorithm for Genetic testing based on these subgroups. Conclusion: Advances in genomic technologies are revolutionizing the diagnostic approach to short stature. Endocrinologists must become facile with the use o...
Mira Irons - One of the best experts on this subject based on the ideXlab platform.
-
ACMG practice guideline: Genetic Evaluation of short stature
Genetics in Medicine, 2009Co-Authors: Laurie H Seaver, Mira IronsAbstract:Short stature is a common indication for Genetic Evaluation. The differential diagnosis is broad and includes both pathologic causes of short stature and nonpathologic causes. The purpose of Genetic Evaluation for short stature is to provide accurate diagnosis for medical management and to provide prognosis and recurrence risk counseling for the patient and family. There is no evidence-based data to guide the Geneticist in an efficient, cost-effective approach to the Evaluation of a patient with short stature. This guideline provides a rubric for the Evaluation of short stature Evaluation and summarizes common diagnoses and clinical testing available.
Denis Laloë - One of the best experts on this subject based on the ideXlab platform.
-
The origin and the development of the concepts of classical Genetic Evaluation
2011Co-Authors: Denis LaloëAbstract:Fundamental aspects of Genetic Evaluation are addressed through some milestones: Genesis of paradigmatic concepts of genotype and phenotype, The polygenic model of Fisher, and the covariance between relatives, Henderson's works, which, hand in hand with the dramatic increase of computing facilities, have enabled us to perform Genetic Evaluation at a population level. These concepts, models and methods have been developed within a pragmatic approach. They have been proved efficient thanks to the response to selection in most breeding programs.
-
interbeef Genetic Evaluation of charolais and limousine weaning weights
Interbull Bulletin, 2009Co-Authors: Eric Venot, Mn Fouilloux, Anders Fogh, T Pabiou, K L Moore, Ja Eriksson, Gilles Renand, Fabio Forabosco, Denis LaloëAbstract:New sets of Genetic parameters were estimated in 2009 for pure bred Charolais and Limousine weaning weights from Denmark, France, Ireland and Sweden (and United Kingdom only for Limousine) and used to run a new Interbeef test Genetic Evaluation. This article gives a general presentation of the Interbeef results.
-
A proposal of criteria of robustness analysis in Genetic Evaluation
Livestock Production Science, 2003Co-Authors: Denis Laloë, Florence PhocasAbstract:An assumption underlying the use of BLUP in the context of Genetic Evaluation is that the expectations of the true breeding values are null. The aim of this paper is to address the robustness of Genetic Evaluation, in terms of the prediction of Genetic trend and selection responses, when this assumption is violated. Animals that are likely to be Genetically different are the animals for whom the accuracy of comparison is low, because they are bred in different environments which are not highly connected. In such a case, the environment and Genetic effects are partially confounded and the Genetic differences between animals in different environments are underestimated. An analytical criterion of robustness is proposed: the lowest coefficient of determination (CD1) of a comparison between animals included in the Evaluation. Its relationship with bias in Genetic Evaluation is explained. A numerical application considers two kinds of planned progeny test designs for French AI sire Evaluation: the “reference sire design” and the “repeater sire design”. Using Monte-Carlo simulations, prediction of Genetic trend is shown to be in close relation with CD1. CD1 appears to be a good indicator of the robustness of the design and a measure of the part of Genetic trend that can be predicted. According to the number of progeny recorded per sire, the repeater sire design only accounted for 3 to 12% of the Genetic trend whereas the reference sire design accounted for 22 to 59% for a trait of h2=0.40. However, in terms of the selection response, the two designs are equivalent when Genetic trend was below 0.5σa, which is always the case in animal breeding programs.
-
Considerations on measures of precision and connectedness in mixed linear models of Genetic Evaluation
Genetics Selection Evolution, 1996Co-Authors: Denis Laloë, Florence Phocas, François MénissierAbstract:Three criteria for the quality of a Genetic Evaluation are compared: the prediction error variance (PEV); the loss of precision due to the estimation of the fixed effects (degree of connectedness) (IC); and a criterion related to the information brought by the Evaluation in terms of generalized coefficient and determination (CD) (precision). These criteria are introduced through simple examples based on an animal model. The main differences between them are the choice of the matrix studied (CD vs PEV, IC), the method used to account for the relationships (CD vs PEV), the use of a reference matrix or model (PEV vs CD, IC), and the data design (IC vs PEV, CD). IC is shown to favor designs with limited information provided by the data and another index is suggested, which minimizes this drawback. The behavior of IC and CD is studied in a hypothetical ’herd + sire’ model. The precision criteria set a balance between connectedness level and information provided by the data, whereas the connectedness criteria favor the model with minimum information and maximum connectedness level. Genetic relationships between animals decrease both PEV and Genetic variability. PEV considers only the favorable effects on PEV; CD accounts for both effects. CD sets a balance between the design and the information brought by the data, the PEV and the Genetic variability and is thus a method of choice for studying the quality of a Genetic Evaluation.
-
Precision and information in linear models of Genetic Evaluation
Genetics Selection Evolution, 1993Co-Authors: Denis LaloëAbstract:Some criteria for measuring the overall precision of a Genetic Evaluation using linear mixed-model methodology are presented. They are derived via an extension of the coefficient of determination to linear combinations of estimates and via the use of the Kullback information. A parallel is drawn between inestimability of fixed-effects contrasts and the zero coefficient of determination for contrasts of random effects. The procedure is illustrated with 2 minor hypothetical examples of Genetic Evaluation based on an animal model and on a sire model.
Andrew Dauber - One of the best experts on this subject based on the ideXlab platform.
-
Genetic Evaluation of Short Stature
The Journal of clinical endocrinology and metabolism, 2014Co-Authors: Andrew Dauber, Ron G. Rosenfeld, Joel N. HirschhornAbstract:Context: Genetics plays a major role in determining an individual's height. Although there are many monogenic disorders that lead to perturbations in growth and result in short stature, there is still no consensus as to the role that Genetic diagnostics should play in the Evaluation of a child with short stature. Evidence Acquisition: A search of PubMed was performed, focusing on the Genetic diagnosis of short stature as well as on specific diagnostic subgroups included in this article. Consensus guidelines were reviewed. Evidence Synthesis: There are a multitude of rare Genetic causes of severe short stature. There is no high-quality evidence to define the optimal approach to the Genetic Evaluation of short stature. We review Genetic etiologies of a number of diagnostic subgroups and propose an algorithm for Genetic testing based on these subgroups. Conclusion: Advances in genomic technologies are revolutionizing the diagnostic approach to short stature. Endocrinologists must become facile with the use o...