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High-throughput and Cost-effective Chicken Genotyping Using Next-Generation Sequencing

Chicken genotyping is becoming common practice in conventional animal breeding improvement. Despite the power of high-throughput methods for genotyping, their high cost limits large scale use in animal breeding and selection. In the present paper we optimized the CornellGBS, an efficient and cost-ef...

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Autores principales: Pértille, Fábio, Guerrero-Bosagna, Carlos, Silva, Vinicius Henrique da, Boschiero, Clarissa, Nunes, José de Ribamar da Silva, Ledur, Mônica Corrêa, Jensen, Per, Coutinho, Luiz Lehmann
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4879531/
https://www.ncbi.nlm.nih.gov/pubmed/27220827
http://dx.doi.org/10.1038/srep26929
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author Pértille, Fábio
Guerrero-Bosagna, Carlos
Silva, Vinicius Henrique da
Boschiero, Clarissa
Nunes, José de Ribamar da Silva
Ledur, Mônica Corrêa
Jensen, Per
Coutinho, Luiz Lehmann
author_facet Pértille, Fábio
Guerrero-Bosagna, Carlos
Silva, Vinicius Henrique da
Boschiero, Clarissa
Nunes, José de Ribamar da Silva
Ledur, Mônica Corrêa
Jensen, Per
Coutinho, Luiz Lehmann
author_sort Pértille, Fábio
collection PubMed
description Chicken genotyping is becoming common practice in conventional animal breeding improvement. Despite the power of high-throughput methods for genotyping, their high cost limits large scale use in animal breeding and selection. In the present paper we optimized the CornellGBS, an efficient and cost-effective genotyping by sequence approach developed in plants, for its application in chickens. Here we describe the successful genotyping of a large number of chickens (462) using CornellGBS approach. Genomic DNA was cleaved with the PstI enzyme, ligated to adapters with barcodes identifying individual animals, and then sequenced on Illumina platform. After filtering parameters were applied, 134,528 SNPs were identified in our experimental population of chickens. Of these SNPs, 67,096 had a minimum taxon call rate of 90% and were considered ‘unique tags’. Interestingly, 20.7% of these unique tags have not been previously reported in the dbSNP. Moreover, 92.6% of these SNPs were concordant with a previous Whole Chicken-genome re-sequencing dataset used for validation purposes. The application of CornellGBS in chickens showed high performance to infer SNPs, particularly in exonic regions and microchromosomes. This approach represents a cost-effective (~US$50/sample) and powerful alternative to current genotyping methods, which has the potential to improve whole-genome selection (WGS), and genome-wide association studies (GWAS) in chicken production.
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spelling pubmed-48795312016-06-08 High-throughput and Cost-effective Chicken Genotyping Using Next-Generation Sequencing Pértille, Fábio Guerrero-Bosagna, Carlos Silva, Vinicius Henrique da Boschiero, Clarissa Nunes, José de Ribamar da Silva Ledur, Mônica Corrêa Jensen, Per Coutinho, Luiz Lehmann Sci Rep Article Chicken genotyping is becoming common practice in conventional animal breeding improvement. Despite the power of high-throughput methods for genotyping, their high cost limits large scale use in animal breeding and selection. In the present paper we optimized the CornellGBS, an efficient and cost-effective genotyping by sequence approach developed in plants, for its application in chickens. Here we describe the successful genotyping of a large number of chickens (462) using CornellGBS approach. Genomic DNA was cleaved with the PstI enzyme, ligated to adapters with barcodes identifying individual animals, and then sequenced on Illumina platform. After filtering parameters were applied, 134,528 SNPs were identified in our experimental population of chickens. Of these SNPs, 67,096 had a minimum taxon call rate of 90% and were considered ‘unique tags’. Interestingly, 20.7% of these unique tags have not been previously reported in the dbSNP. Moreover, 92.6% of these SNPs were concordant with a previous Whole Chicken-genome re-sequencing dataset used for validation purposes. The application of CornellGBS in chickens showed high performance to infer SNPs, particularly in exonic regions and microchromosomes. This approach represents a cost-effective (~US$50/sample) and powerful alternative to current genotyping methods, which has the potential to improve whole-genome selection (WGS), and genome-wide association studies (GWAS) in chicken production. Nature Publishing Group 2016-05-25 /pmc/articles/PMC4879531/ /pubmed/27220827 http://dx.doi.org/10.1038/srep26929 Text en Copyright © 2016, Macmillan Publishers Limited http://creativecommons.org/licenses/by/4.0/ This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/
spellingShingle Article
Pértille, Fábio
Guerrero-Bosagna, Carlos
Silva, Vinicius Henrique da
Boschiero, Clarissa
Nunes, José de Ribamar da Silva
Ledur, Mônica Corrêa
Jensen, Per
Coutinho, Luiz Lehmann
High-throughput and Cost-effective Chicken Genotyping Using Next-Generation Sequencing
title High-throughput and Cost-effective Chicken Genotyping Using Next-Generation Sequencing
title_full High-throughput and Cost-effective Chicken Genotyping Using Next-Generation Sequencing
title_fullStr High-throughput and Cost-effective Chicken Genotyping Using Next-Generation Sequencing
title_full_unstemmed High-throughput and Cost-effective Chicken Genotyping Using Next-Generation Sequencing
title_short High-throughput and Cost-effective Chicken Genotyping Using Next-Generation Sequencing
title_sort high-throughput and cost-effective chicken genotyping using next-generation sequencing
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4879531/
https://www.ncbi.nlm.nih.gov/pubmed/27220827
http://dx.doi.org/10.1038/srep26929
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