In this first chapter the history of animal breeding is presented. The importance of selection by nature and important aspects of the domestication process will be. Animal Breeding is the application of genetic principles to the improvement of farm animals. Garden peas do not seem to be a very good model for studying farm. Overview. •Changes to traditional animal breeding. •Using DNA in animal breeding. •What is a SNP?! •De-mystify genomic selection!!.
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This textbook contains teaching material on animal breeding and genetics for BSc students. The text book started as an initiative of the Dutch. animal breeding in organic agriculture, as well as an action plan to realise an appropriate Animal breeding, whether of cattle, pigs or poultry, is currently. PDF | On Aug 1, , Gurvinder Singh Brah and others published Animal Breeding: Principles and Applications.
Breeding stock[ edit ] Breeding stock is a group of animals used for the purpose of planned breeding. When individuals are looking to breed animals, they look for certain valuable traits in purebred animals, or may intend to use some type of crossbreeding to produce a new type of stock with different, and presumably super abilities in a given area of endeavor. For example, when breeding swine for meat, the "breeding stock should be sound, fast growing, muscular, lean, and reproductively efficient. Opposite to the practice of mating animals of different breeds, purebred breeding aims to establish and maintain stable traits, that animals will pass to the next generation. By "breeding the best to the best", employing a certain degree of inbreeding , considerable culling , and selection for "superior" qualities, one could develop a bloodline or " breed " superior in certain respects to the original base stock. The observable phenomenon of hybrid vigor stands in contrast to the notion of breed purity.
Performance usually includes a combination of multiple characteristics, or traits, most of which are quantitative in nature. The main criteria that are used to identify individuals to be used for breeding are estimates of their breeding values for the traits of interest.
The breeding value of an individual is defined as the sum of the additive effects of all loci that contribute to the trait quantitative trait loci or QTL , deviated from the population mean [ 1 ]. Under some assumptions, this is equivalent to two times the expected phenotype of progeny deviated from the population mean [ 1 ], which is what animal breeders aim to improve.
The factor two stems from the fact that progeny receive half of their alleles from their father and half from their mother.
To date, extensive data bases of recorded phenotypes for traits of interest, or for traits that are genetically correlated to traits of interest, have been used as the main source of information to estimate the breeding value of selection candidates. To this end, sophisticated statistical methods based on best linear unbiased prediction BLUP mixed linear model methodology [ 2 , 3 ] have been implemented.
These methods capitalize on information contained in the recorded phenotypes of not only the individual itself but also that of its relatives, in order to maximize the accuracy of the resulting estimated breeding value EBV. Here, accuracy is defined as the correlation between the true and estimated breeding value and is one of the main determinants of the rate of genetic improvement that can be achieved in a breeding program per unit of time.
Other determinants are the selection intensity, which is related to the proportion of individuals that are selected to be parents , and the age at which breeders are selected and reproduce, which defines the generation interval. The expected rate of improvement per unit of time is proportional to accuracy and selection intensity and inversely proportional to the generation interval [ 1 ].
Statistical models and selection theory used in animal breeding are based on the so-called infinitesimal genetic model of quantitative genetics [ 1 ].
The infinitesimal model assumes the trait is affected by a large infinite number of unlinked genes with very small and additive effects. Simulation studies have, however, shown that, at least in the short term, results from these models are rather robust to the true genetic architecture of traits [ 4 ]. As a result, specific knowledge of the genetic architecture is not essential for these phenotype-based methods to be effective. Although selection programs based on EBV estimated from phenotype have been very successful, they also face a number of limitations.
Cost of phenotype recording also plays an important role here.
Unfortunately, some traits of interest are only recorded late in life e. These phenotyping constraints limit the amount of genetic progress that can be made.
In order to overcome these limitations, animal breeders have a long history of investigating opportunities to get early measurements on selection candidates that can be used to increase the accuracy of EBV at a young age. Initial work focused on indicator traits, physiological measurements and blood markers.
One of the early successes and applications was the use of blood groups as a genetic marker to select for disease resistance in chickens [ 5 ]. An example of a physiological measure is serum IGF-1 measured at an early age in cattle and pigs as an indicator of efficient growth [ 6 ]. In general, however, the use of such indicator traits, and especially physiological indicator traits measured in blood, has been limited. Against this background, the purpose here is to review the impact that molecular genetics has had and is having on genetic improvement programs in livestock, in particular the recent availability of high-density SNP genotyping panels.
In addition, the knowledge that this is providing on the genetic architecture of traits of interest will be addressed, along with implications and opportunities for the future. The obvious first application of these methods was to discover the genetic basis and develop genetic tests for single gene defects. For quantitative traits, these advances promised the identification of QTL and the development of DNA tests that could be done on all selection candidates at an early age to help inform selection decision through marker-assisted selection MAS , i.
To this end, large numbers of candidate gene and QTL mapping studies were conducted [ 10 , 11 ].
This resulted in the discovery of substantial numbers of QTL and marker-phenotype associations and some causative mutations [ 12 ]. The implementation of this information in breeding programs, was however limited for various reasons [ 12 ], but primarily because, 1 most QTL studies were conducted in experimental crosses to create extensive linkage disequilibrium, rather than in the populations that are used for genetic improvement, 2 most effects discovered tended to explain only a limited amount of genetic variation for the trait, 3 many QTL and associations could not be replicated, and 4 the still high cost of routine genotyping of selection candidates for even a handful of genetic markers.
There are, however, some notable exceptions of the discovery of genes or markers with large effects that were repeatable and that were implemented in practical breeding programs [ 12 ]. To date, tens of thousands of dairy and beef bulls and cows have been genotyped using this platform. Similar SNP panels of 40 to 65 thousand SNP are now available for other livestock species, including pigs, poultry, sheep, and horse. Skip to main content.
Home Environment Wildlife, animals, biodiversity and ecosystems Animal and plant health. Guidance Lists of recognised breed societies and breeding operations. Published 1 November Last updated 5 March — see all updates. Guide to zootechnical rules and standards HTML. Defra lists recognised breed societies for bovine, ovine, porcine and caprine species.
It also lists recognised breeding operations for hybrid breeding pigs. EU zootechnical legislation supports intra-Community trade in: Maywood , , vol. Weimin, C. Wu, H. Online, , vol. Hyldig, S. Morgan, H. Rakyan, V. Vanyushin, B. CrossRef Google Scholar Lees-Murdock, D. Sasaki, H. Lanza, R. Palmieri, C.
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