Genomic prediction for tuberculosis resistance in dairy cattle.

<h4>Background</h4>The increasing prevalence of bovine tuberculosis (bTB) in the UK and the limitations of the currently available diagnostic and control methods require the development of complementary approaches to assist in the sustainable control of the disease. One potential approac...

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Autores principales:	Smaragda Tsairidou, John A Woolliams, Adrian R Allen, Robin A Skuce, Stewart H McBride, David M Wright, Mairead L Bermingham, Ricardo Pong-Wong, Oswald Matika, Stanley W J McDowell, Elizabeth J Glass, Stephen C Bishop
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2014
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/0c77d985098149fcb168b48599da2075
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spelling	oai:doaj.org-article:0c77d985098149fcb168b48599da20752021-11-18T08:20:12ZGenomic prediction for tuberculosis resistance in dairy cattle.1932-620310.1371/journal.pone.0096728https://doaj.org/article/0c77d985098149fcb168b48599da20752014-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/24809715/pdf/?tool=EBIhttps://doaj.org/toc/1932-6203<h4>Background</h4>The increasing prevalence of bovine tuberculosis (bTB) in the UK and the limitations of the currently available diagnostic and control methods require the development of complementary approaches to assist in the sustainable control of the disease. One potential approach is the identification of animals that are genetically more resistant to bTB, to enable breeding of animals with enhanced resistance. This paper focuses on prediction of resistance to bTB. We explore estimation of direct genomic estimated breeding values (DGVs) for bTB resistance in UK dairy cattle, using dense SNP chip data, and test these genomic predictions for situations when disease phenotypes are not available on selection candidates.<h4>Methodology/principal findings</h4>We estimated DGVs using genomic best linear unbiased prediction methodology, and assessed their predictive accuracies with a cross validation procedure and receiver operator characteristic (ROC) curves. Furthermore, these results were compared with theoretical expectations for prediction accuracy and area-under-the-ROC-curve (AUC). The dataset comprised 1151 Holstein-Friesian cows (bTB cases or controls). All individuals (592 cases and 559 controls) were genotyped for 727,252 loci (Illumina Bead Chip). The estimated observed heritability of bTB resistance was 0.23±0.06 (0.34 on the liability scale) and five-fold cross validation, replicated six times, provided a prediction accuracy of 0.33 (95% C.I.: 0.26, 0.40). ROC curves, and the resulting AUC, gave a probability of 0.58, averaged across six replicates, of correctly classifying cows as diseased or as healthy based on SNP chip genotype alone using these data.<h4>Conclusions/significance</h4>These results provide a first step in the investigation of the potential feasibility of genomic selection for bTB resistance using SNP data. Specifically, they demonstrate that genomic selection is possible, even in populations with no pedigree data and on animals lacking bTB phenotypes. However, a larger training population will be required to improve prediction accuracies.Smaragda TsairidouJohn A WoolliamsAdrian R AllenRobin A SkuceStewart H McBrideDavid M WrightMairead L BerminghamRicardo Pong-WongOswald MatikaStanley W J McDowellElizabeth J GlassStephen C BishopPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 9, Iss 5, p e96728 (2014)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Smaragda Tsairidou John A Woolliams Adrian R Allen Robin A Skuce Stewart H McBride David M Wright Mairead L Bermingham Ricardo Pong-Wong Oswald Matika Stanley W J McDowell Elizabeth J Glass Stephen C Bishop Genomic prediction for tuberculosis resistance in dairy cattle.
description	<h4>Background</h4>The increasing prevalence of bovine tuberculosis (bTB) in the UK and the limitations of the currently available diagnostic and control methods require the development of complementary approaches to assist in the sustainable control of the disease. One potential approach is the identification of animals that are genetically more resistant to bTB, to enable breeding of animals with enhanced resistance. This paper focuses on prediction of resistance to bTB. We explore estimation of direct genomic estimated breeding values (DGVs) for bTB resistance in UK dairy cattle, using dense SNP chip data, and test these genomic predictions for situations when disease phenotypes are not available on selection candidates.<h4>Methodology/principal findings</h4>We estimated DGVs using genomic best linear unbiased prediction methodology, and assessed their predictive accuracies with a cross validation procedure and receiver operator characteristic (ROC) curves. Furthermore, these results were compared with theoretical expectations for prediction accuracy and area-under-the-ROC-curve (AUC). The dataset comprised 1151 Holstein-Friesian cows (bTB cases or controls). All individuals (592 cases and 559 controls) were genotyped for 727,252 loci (Illumina Bead Chip). The estimated observed heritability of bTB resistance was 0.23±0.06 (0.34 on the liability scale) and five-fold cross validation, replicated six times, provided a prediction accuracy of 0.33 (95% C.I.: 0.26, 0.40). ROC curves, and the resulting AUC, gave a probability of 0.58, averaged across six replicates, of correctly classifying cows as diseased or as healthy based on SNP chip genotype alone using these data.<h4>Conclusions/significance</h4>These results provide a first step in the investigation of the potential feasibility of genomic selection for bTB resistance using SNP data. Specifically, they demonstrate that genomic selection is possible, even in populations with no pedigree data and on animals lacking bTB phenotypes. However, a larger training population will be required to improve prediction accuracies.
format	article
author	Smaragda Tsairidou John A Woolliams Adrian R Allen Robin A Skuce Stewart H McBride David M Wright Mairead L Bermingham Ricardo Pong-Wong Oswald Matika Stanley W J McDowell Elizabeth J Glass Stephen C Bishop
author_facet	Smaragda Tsairidou John A Woolliams Adrian R Allen Robin A Skuce Stewart H McBride David M Wright Mairead L Bermingham Ricardo Pong-Wong Oswald Matika Stanley W J McDowell Elizabeth J Glass Stephen C Bishop
author_sort	Smaragda Tsairidou
title	Genomic prediction for tuberculosis resistance in dairy cattle.
title_short	Genomic prediction for tuberculosis resistance in dairy cattle.
title_full	Genomic prediction for tuberculosis resistance in dairy cattle.
title_fullStr	Genomic prediction for tuberculosis resistance in dairy cattle.
title_full_unstemmed	Genomic prediction for tuberculosis resistance in dairy cattle.
title_sort	genomic prediction for tuberculosis resistance in dairy cattle.
publisher	Public Library of Science (PLoS)
publishDate	2014
url	https://doaj.org/article/0c77d985098149fcb168b48599da2075
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Genomic prediction for tuberculosis resistance in dairy cattle.

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