Effect of quality control, density and allele frequency of markers on the accuracy of genomic prediction for complex traits in Nellore cattle
Autor(a) principal: | |
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Data de Publicação: | 2017 |
Outros Autores: | , , , , , , , , , , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1071/AN16821 http://hdl.handle.net/11449/176526 |
Resumo: | This study was designed to test the impact of quality control, density and allele frequency of single nucleotide polymorphisms (SNP) markers on the accuracy of genomic predictions, using three traits with different heritabilities and two methods of prediction in a Nellore cattle population genotyped with the Illumina Bovine HD Assay. A total of 1756∼ 3150 and 3119 records of age at first calving (AFC)∼ weaning weight (WW) and yearling weight (YW), respectively, were used. Three scenarios with different exclusion thresholds for minor allele frequency (MAF), deviation from Hardy-Weinberg equilibrium (HWE) and correlation between SNP pairs (r2) were constructed for all traits: (1) high rigor (S1): call rate <0.98, MAF <0.05, HWE with P <10-5, and r2 >0.999∼ (2) Moderate rigor (S2): call rate <0.85 and MAF <0.01∼ (3) Low rigor (S3): only non-Autosomal SNP and those mapped on the same position were excluded. Additionally, to assess the prediction accuracy from different markers density, six panels (10K, 50K, 100K, 300K, 500K and 700K) were customised using the high-density genotyping assay as reference. Finally, from the markers available in high-density genotyping assay, six groups (G) with different minor allele frequency bins were defined to estimate the accuracy of genomic prediction. The range of MAF bins was approximately equal for the traits studied: G1 (0.000-0.009), G2 (0.010-0.064), G3 (0.065-0.174), G4 (0.175-0.325), G5 (0.326-0.500) and G6 (0.000-0.500). The Genomic Best Linear Unbiased Predictor and BayesCπ methods were used to estimate the SNP marker effects. Five-fold cross-validation was used to measure the accuracy of genomic prediction for all scenarios. There were no effects of genotypes quality control criteria on the accuracies of genomic predictions. For all traits, the higher density panel did not provide greater prediction accuracies than the low density one (10K panel). The groups of SNP with low MAF (MAF ≤0.007 for AFC, MAF ≤0.009 for WW and MAF ≤0.008 for YW) provided lower prediction accuracies than the groups with higher allele frequencies. |
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Repositório Institucional da UNESP |
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2946 |
spelling |
Effect of quality control, density and allele frequency of markers on the accuracy of genomic prediction for complex traits in Nellore cattleaccuracy of predictionbeef cattlemarker densitymarker editingmarker effects.This study was designed to test the impact of quality control, density and allele frequency of single nucleotide polymorphisms (SNP) markers on the accuracy of genomic predictions, using three traits with different heritabilities and two methods of prediction in a Nellore cattle population genotyped with the Illumina Bovine HD Assay. A total of 1756∼ 3150 and 3119 records of age at first calving (AFC)∼ weaning weight (WW) and yearling weight (YW), respectively, were used. Three scenarios with different exclusion thresholds for minor allele frequency (MAF), deviation from Hardy-Weinberg equilibrium (HWE) and correlation between SNP pairs (r2) were constructed for all traits: (1) high rigor (S1): call rate <0.98, MAF <0.05, HWE with P <10-5, and r2 >0.999∼ (2) Moderate rigor (S2): call rate <0.85 and MAF <0.01∼ (3) Low rigor (S3): only non-Autosomal SNP and those mapped on the same position were excluded. Additionally, to assess the prediction accuracy from different markers density, six panels (10K, 50K, 100K, 300K, 500K and 700K) were customised using the high-density genotyping assay as reference. Finally, from the markers available in high-density genotyping assay, six groups (G) with different minor allele frequency bins were defined to estimate the accuracy of genomic prediction. The range of MAF bins was approximately equal for the traits studied: G1 (0.000-0.009), G2 (0.010-0.064), G3 (0.065-0.174), G4 (0.175-0.325), G5 (0.326-0.500) and G6 (0.000-0.500). The Genomic Best Linear Unbiased Predictor and BayesCπ methods were used to estimate the SNP marker effects. Five-fold cross-validation was used to measure the accuracy of genomic prediction for all scenarios. There were no effects of genotypes quality control criteria on the accuracies of genomic predictions. For all traits, the higher density panel did not provide greater prediction accuracies than the low density one (10K panel). The groups of SNP with low MAF (MAF ≤0.007 for AFC, MAF ≤0.009 for WW and MAF ≤0.008 for YW) provided lower prediction accuracies than the groups with higher allele frequencies.Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Departamento de Zootecnia Universidade Estadual Paulista (Unesp) Faculdade de Ciências Agrárias e Veterinárias Jaboticabal, Via de acesso Prof. Paulo Donato Castellane, s/nDepartment of Animal Sciences University of Wisconsin, 436 Animal Science BuildingNational Counsel of Technological and Scientific Development CNPq SHIS QI 1 Conjunto B-Blocos A B C e DDepartamento de Zootecnia Universidade Estadual Paulista (Unesp) Faculdade de Ciências Agrárias e Veterinárias Jaboticabal, Via de acesso Prof. Paulo Donato Castellane, s/nUniversidade Estadual Paulista (Unesp)University of WisconsinC e DBresolin, Tiago [UNESP]Rosa, Guilherme Jordão De MagalhãesValente, Bruno DouradoEspigolan, Rafael [UNESP]Gordo, Daniel Gustavo Mansan [UNESP]Braz, Camila Urbano [UNESP]Fernandes, Gerardo Alves [UNESP]Magalhães, Ana Fabrícia Braga [UNESP]Garcia, Diogo Anastacio [UNESP]Frezarim, Gabriela Bonfá [UNESP]Leão, Guilherme Fonseca Carneiro [UNESP]Carvalheiro, Roberto [UNESP]Baldi, Fernando [UNESP]Nunes De Oliveira, Henrique [UNESP]Galvão De Albuquerque, Lucia [UNESP]2018-12-11T17:21:13Z2018-12-11T17:21:13Z2017-12-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttp://dx.doi.org/10.1071/AN16821Animal Production Science.1836-57871836-0939http://hdl.handle.net/11449/17652610.1071/AN168212-s2.0-85049235553Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengAnimal Production Science0,6370,637info:eu-repo/semantics/openAccess2021-10-23T16:57:51Zoai:repositorio.unesp.br:11449/176526Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462021-10-23T16:57:51Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Effect of quality control, density and allele frequency of markers on the accuracy of genomic prediction for complex traits in Nellore cattle |
title |
Effect of quality control, density and allele frequency of markers on the accuracy of genomic prediction for complex traits in Nellore cattle |
spellingShingle |
Effect of quality control, density and allele frequency of markers on the accuracy of genomic prediction for complex traits in Nellore cattle Bresolin, Tiago [UNESP] accuracy of prediction beef cattle marker density marker editing marker effects. |
title_short |
Effect of quality control, density and allele frequency of markers on the accuracy of genomic prediction for complex traits in Nellore cattle |
title_full |
Effect of quality control, density and allele frequency of markers on the accuracy of genomic prediction for complex traits in Nellore cattle |
title_fullStr |
Effect of quality control, density and allele frequency of markers on the accuracy of genomic prediction for complex traits in Nellore cattle |
title_full_unstemmed |
Effect of quality control, density and allele frequency of markers on the accuracy of genomic prediction for complex traits in Nellore cattle |
title_sort |
Effect of quality control, density and allele frequency of markers on the accuracy of genomic prediction for complex traits in Nellore cattle |
author |
Bresolin, Tiago [UNESP] |
author_facet |
Bresolin, Tiago [UNESP] Rosa, Guilherme Jordão De Magalhães Valente, Bruno Dourado Espigolan, Rafael [UNESP] Gordo, Daniel Gustavo Mansan [UNESP] Braz, Camila Urbano [UNESP] Fernandes, Gerardo Alves [UNESP] Magalhães, Ana Fabrícia Braga [UNESP] Garcia, Diogo Anastacio [UNESP] Frezarim, Gabriela Bonfá [UNESP] Leão, Guilherme Fonseca Carneiro [UNESP] Carvalheiro, Roberto [UNESP] Baldi, Fernando [UNESP] Nunes De Oliveira, Henrique [UNESP] Galvão De Albuquerque, Lucia [UNESP] |
author_role |
author |
author2 |
Rosa, Guilherme Jordão De Magalhães Valente, Bruno Dourado Espigolan, Rafael [UNESP] Gordo, Daniel Gustavo Mansan [UNESP] Braz, Camila Urbano [UNESP] Fernandes, Gerardo Alves [UNESP] Magalhães, Ana Fabrícia Braga [UNESP] Garcia, Diogo Anastacio [UNESP] Frezarim, Gabriela Bonfá [UNESP] Leão, Guilherme Fonseca Carneiro [UNESP] Carvalheiro, Roberto [UNESP] Baldi, Fernando [UNESP] Nunes De Oliveira, Henrique [UNESP] Galvão De Albuquerque, Lucia [UNESP] |
author2_role |
author author author author author author author author author author author author author author |
dc.contributor.none.fl_str_mv |
Universidade Estadual Paulista (Unesp) University of Wisconsin C e D |
dc.contributor.author.fl_str_mv |
Bresolin, Tiago [UNESP] Rosa, Guilherme Jordão De Magalhães Valente, Bruno Dourado Espigolan, Rafael [UNESP] Gordo, Daniel Gustavo Mansan [UNESP] Braz, Camila Urbano [UNESP] Fernandes, Gerardo Alves [UNESP] Magalhães, Ana Fabrícia Braga [UNESP] Garcia, Diogo Anastacio [UNESP] Frezarim, Gabriela Bonfá [UNESP] Leão, Guilherme Fonseca Carneiro [UNESP] Carvalheiro, Roberto [UNESP] Baldi, Fernando [UNESP] Nunes De Oliveira, Henrique [UNESP] Galvão De Albuquerque, Lucia [UNESP] |
dc.subject.por.fl_str_mv |
accuracy of prediction beef cattle marker density marker editing marker effects. |
topic |
accuracy of prediction beef cattle marker density marker editing marker effects. |
description |
This study was designed to test the impact of quality control, density and allele frequency of single nucleotide polymorphisms (SNP) markers on the accuracy of genomic predictions, using three traits with different heritabilities and two methods of prediction in a Nellore cattle population genotyped with the Illumina Bovine HD Assay. A total of 1756∼ 3150 and 3119 records of age at first calving (AFC)∼ weaning weight (WW) and yearling weight (YW), respectively, were used. Three scenarios with different exclusion thresholds for minor allele frequency (MAF), deviation from Hardy-Weinberg equilibrium (HWE) and correlation between SNP pairs (r2) were constructed for all traits: (1) high rigor (S1): call rate <0.98, MAF <0.05, HWE with P <10-5, and r2 >0.999∼ (2) Moderate rigor (S2): call rate <0.85 and MAF <0.01∼ (3) Low rigor (S3): only non-Autosomal SNP and those mapped on the same position were excluded. Additionally, to assess the prediction accuracy from different markers density, six panels (10K, 50K, 100K, 300K, 500K and 700K) were customised using the high-density genotyping assay as reference. Finally, from the markers available in high-density genotyping assay, six groups (G) with different minor allele frequency bins were defined to estimate the accuracy of genomic prediction. The range of MAF bins was approximately equal for the traits studied: G1 (0.000-0.009), G2 (0.010-0.064), G3 (0.065-0.174), G4 (0.175-0.325), G5 (0.326-0.500) and G6 (0.000-0.500). The Genomic Best Linear Unbiased Predictor and BayesCπ methods were used to estimate the SNP marker effects. Five-fold cross-validation was used to measure the accuracy of genomic prediction for all scenarios. There were no effects of genotypes quality control criteria on the accuracies of genomic predictions. For all traits, the higher density panel did not provide greater prediction accuracies than the low density one (10K panel). The groups of SNP with low MAF (MAF ≤0.007 for AFC, MAF ≤0.009 for WW and MAF ≤0.008 for YW) provided lower prediction accuracies than the groups with higher allele frequencies. |
publishDate |
2017 |
dc.date.none.fl_str_mv |
2017-12-01 2018-12-11T17:21:13Z 2018-12-11T17:21:13Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://dx.doi.org/10.1071/AN16821 Animal Production Science. 1836-5787 1836-0939 http://hdl.handle.net/11449/176526 10.1071/AN16821 2-s2.0-85049235553 |
url |
http://dx.doi.org/10.1071/AN16821 http://hdl.handle.net/11449/176526 |
identifier_str_mv |
Animal Production Science. 1836-5787 1836-0939 10.1071/AN16821 2-s2.0-85049235553 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Animal Production Science 0,637 0,637 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.source.none.fl_str_mv |
Scopus reponame:Repositório Institucional da UNESP instname:Universidade Estadual Paulista (UNESP) instacron:UNESP |
instname_str |
Universidade Estadual Paulista (UNESP) |
instacron_str |
UNESP |
institution |
UNESP |
reponame_str |
Repositório Institucional da UNESP |
collection |
Repositório Institucional da UNESP |
repository.name.fl_str_mv |
Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP) |
repository.mail.fl_str_mv |
|
_version_ |
1797789832134524928 |