Cargando…

Comparison of alternative approaches to single-trait genomic prediction using genotyped and non-genotyped Hanwoo beef cattle

BACKGROUND: Genomic predictions from BayesA and BayesB use training data that include animals with both phenotypes and genotypes. Single-step methodologies allow additional information from non-genotyped relatives to be included in the analysis. The single-step genomic best linear unbiased predictio...

Descripción completa

Detalles Bibliográficos
Autores principales: Lee, Joonho, Cheng, Hao, Garrick, Dorian, Golden, Bruce, Dekkers, Jack, Park, Kyungdo, Lee, Deukhwan, Fernando, Rohan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5240330/
https://www.ncbi.nlm.nih.gov/pubmed/28093065
http://dx.doi.org/10.1186/s12711-016-0279-9
_version_ 1782496047925297152
author Lee, Joonho
Cheng, Hao
Garrick, Dorian
Golden, Bruce
Dekkers, Jack
Park, Kyungdo
Lee, Deukhwan
Fernando, Rohan
author_facet Lee, Joonho
Cheng, Hao
Garrick, Dorian
Golden, Bruce
Dekkers, Jack
Park, Kyungdo
Lee, Deukhwan
Fernando, Rohan
author_sort Lee, Joonho
collection PubMed
description BACKGROUND: Genomic predictions from BayesA and BayesB use training data that include animals with both phenotypes and genotypes. Single-step methodologies allow additional information from non-genotyped relatives to be included in the analysis. The single-step genomic best linear unbiased prediction (SSGBLUP) method uses a relationship matrix computed from marker and pedigree information, in which missing genotypes are imputed implicitly. Single-step Bayesian regression (SSBR) extends SSGBLUP to BayesB-like models using explicitly imputed genotypes for non-genotyped individuals. METHODS: Carcass records included 988 genotyped Hanwoo steers with 35,882 SNPs and 1438 non-genotyped steers that were measured for back-fat thickness (BFT), carcass weight (CWT), eye-muscle area, and marbling score (MAR). Single-trait pedigree-based BLUP, Bayesian methods using only genotyped individuals, SSGBLUP and SSBR methods were compared using cross-validation. RESULTS: Methods using genomic information always outperformed pedigree-based BLUP when the same phenotypic data were modeled from either genotyped individuals only or both genotyped and non-genotyped individuals. For BFT and MAR, accuracies were higher with single-step methods than with BayesB, BayesC and BayesCπ. Gains in accuracy with the single-step methods ranged from +0.06 to +0.09 for BFT and from +0.05 to +0.07 for MAR. For CWT, SSBR always outperformed the corresponding Bayesian methods that used only genotyped individuals. However, although SSGBLUP incorporated information from non-genotyped individuals, prediction accuracies were lower with SSGBLUP than with BayesC (π = 0.9999) and BayesB (π = 0.98) for CWT because, for this particular trait, there was a benefit from the mixture priors of the effects of the single nucleotide polymorphisms. CONCLUSIONS: Single-step methods are the preferred approaches for prediction combining genotyped and non-genotyped animals. Alternative priors allow SSBR to outperform SSGBLUP in some cases.
format Online
Article
Text
id pubmed-5240330
institution National Center for Biotechnology Information
language English
publishDate 2017
publisher BioMed Central
record_format MEDLINE/PubMed
spelling pubmed-52403302017-01-19 Comparison of alternative approaches to single-trait genomic prediction using genotyped and non-genotyped Hanwoo beef cattle Lee, Joonho Cheng, Hao Garrick, Dorian Golden, Bruce Dekkers, Jack Park, Kyungdo Lee, Deukhwan Fernando, Rohan Genet Sel Evol Research Article BACKGROUND: Genomic predictions from BayesA and BayesB use training data that include animals with both phenotypes and genotypes. Single-step methodologies allow additional information from non-genotyped relatives to be included in the analysis. The single-step genomic best linear unbiased prediction (SSGBLUP) method uses a relationship matrix computed from marker and pedigree information, in which missing genotypes are imputed implicitly. Single-step Bayesian regression (SSBR) extends SSGBLUP to BayesB-like models using explicitly imputed genotypes for non-genotyped individuals. METHODS: Carcass records included 988 genotyped Hanwoo steers with 35,882 SNPs and 1438 non-genotyped steers that were measured for back-fat thickness (BFT), carcass weight (CWT), eye-muscle area, and marbling score (MAR). Single-trait pedigree-based BLUP, Bayesian methods using only genotyped individuals, SSGBLUP and SSBR methods were compared using cross-validation. RESULTS: Methods using genomic information always outperformed pedigree-based BLUP when the same phenotypic data were modeled from either genotyped individuals only or both genotyped and non-genotyped individuals. For BFT and MAR, accuracies were higher with single-step methods than with BayesB, BayesC and BayesCπ. Gains in accuracy with the single-step methods ranged from +0.06 to +0.09 for BFT and from +0.05 to +0.07 for MAR. For CWT, SSBR always outperformed the corresponding Bayesian methods that used only genotyped individuals. However, although SSGBLUP incorporated information from non-genotyped individuals, prediction accuracies were lower with SSGBLUP than with BayesC (π = 0.9999) and BayesB (π = 0.98) for CWT because, for this particular trait, there was a benefit from the mixture priors of the effects of the single nucleotide polymorphisms. CONCLUSIONS: Single-step methods are the preferred approaches for prediction combining genotyped and non-genotyped animals. Alternative priors allow SSBR to outperform SSGBLUP in some cases. BioMed Central 2017-01-04 /pmc/articles/PMC5240330/ /pubmed/28093065 http://dx.doi.org/10.1186/s12711-016-0279-9 Text en © The Author(s) 2017 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research Article
Lee, Joonho
Cheng, Hao
Garrick, Dorian
Golden, Bruce
Dekkers, Jack
Park, Kyungdo
Lee, Deukhwan
Fernando, Rohan
Comparison of alternative approaches to single-trait genomic prediction using genotyped and non-genotyped Hanwoo beef cattle
title Comparison of alternative approaches to single-trait genomic prediction using genotyped and non-genotyped Hanwoo beef cattle
title_full Comparison of alternative approaches to single-trait genomic prediction using genotyped and non-genotyped Hanwoo beef cattle
title_fullStr Comparison of alternative approaches to single-trait genomic prediction using genotyped and non-genotyped Hanwoo beef cattle
title_full_unstemmed Comparison of alternative approaches to single-trait genomic prediction using genotyped and non-genotyped Hanwoo beef cattle
title_short Comparison of alternative approaches to single-trait genomic prediction using genotyped and non-genotyped Hanwoo beef cattle
title_sort comparison of alternative approaches to single-trait genomic prediction using genotyped and non-genotyped hanwoo beef cattle
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5240330/
https://www.ncbi.nlm.nih.gov/pubmed/28093065
http://dx.doi.org/10.1186/s12711-016-0279-9
work_keys_str_mv AT leejoonho comparisonofalternativeapproachestosingletraitgenomicpredictionusinggenotypedandnongenotypedhanwoobeefcattle
AT chenghao comparisonofalternativeapproachestosingletraitgenomicpredictionusinggenotypedandnongenotypedhanwoobeefcattle
AT garrickdorian comparisonofalternativeapproachestosingletraitgenomicpredictionusinggenotypedandnongenotypedhanwoobeefcattle
AT goldenbruce comparisonofalternativeapproachestosingletraitgenomicpredictionusinggenotypedandnongenotypedhanwoobeefcattle
AT dekkersjack comparisonofalternativeapproachestosingletraitgenomicpredictionusinggenotypedandnongenotypedhanwoobeefcattle
AT parkkyungdo comparisonofalternativeapproachestosingletraitgenomicpredictionusinggenotypedandnongenotypedhanwoobeefcattle
AT leedeukhwan comparisonofalternativeapproachestosingletraitgenomicpredictionusinggenotypedandnongenotypedhanwoobeefcattle
AT fernandorohan comparisonofalternativeapproachestosingletraitgenomicpredictionusinggenotypedandnongenotypedhanwoobeefcattle