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  1. Article ; Online: Comparison of models for missing pedigree in single-step genomic prediction.

    Masuda, Yutaka / Tsuruta, Shogo / Bermann, Matias / Bradford, Heather L / Misztal, Ignacy

    Journal of animal science

    2021  Volume 99, Issue 2

    Abstract: ... of the unified relationship matrix (H-inverse) required for single-step genomic best linear unbiased prediction ... is well established for the pedigree model, it is unclear how UPGs are integrated into the inverse ... inverses and to compare genetic trends among models with UPG and MF H-inverses using a simulated purebred ...

    Abstract Pedigree information is often missing for some animals in a breeding program. Unknown-parent groups (UPGs) are assigned to the missing parents to avoid biased genetic evaluations. Although the use of UPGs is well established for the pedigree model, it is unclear how UPGs are integrated into the inverse of the unified relationship matrix (H-inverse) required for single-step genomic best linear unbiased prediction. A generalization of the UPG model is the metafounder (MF) model. The objectives of this study were to derive 3 H-inverses and to compare genetic trends among models with UPG and MF H-inverses using a simulated purebred population. All inverses were derived using the joint density function of the random breeding values and genetic groups. The breeding values of genotyped animals (u2) were assumed to be adjusted for UPG effects (g) using matrix Q2 as u2∗=u2+Q2g before incorporating genomic information. The Quaas-Pollak-transformed (QP) H-inverse was derived using a joint density function of u2∗ and g updated with genomic information and assuming nonzero cov(u2∗,g'). The modified QP (altered) H-inverse also assumes that the genomic information updates u2∗ and g, but cov(u2∗,g')=0. The UPG-encapsulated (EUPG) H-inverse assumed genomic information updates the distribution of u2∗. The EUPG H-inverse had the same structure as the MF H-inverse. Fifty percent of the genotyped females in the simulation had a missing dam, and missing parents were replaced with UPGs by generation. The simulation study indicated that u2∗ and g in models using the QP and altered H-inverses may be inseparable leading to potential biases in genetic trends. Models using the EUPG and MF H-inverses showed no genetic trend biases. These 2 H-inverses yielded the same genomic EBV (GEBV). The predictive ability and inflation of GEBVs from young genotyped animals were nearly identical among models using the QP, altered, EUPG, and MF H-inverses. Although the choice of H-inverse in real applications with enough data may not result in biased genetic trends, the EUPG and MF H-inverses are to be preferred because of theoretical justification and possibility to reduce biases.
    MeSH term(s) Animals ; Female ; Genome ; Genomics ; Genotype ; Models, Genetic ; Pedigree ; Phenotype
    Language English
    Publishing date 2021-01-25
    Publishing country United States
    Document type Journal Article
    ZDB-ID 390959-1
    ISSN 1525-3163 ; 0021-8812
    ISSN (online) 1525-3163
    ISSN 0021-8812
    DOI 10.1093/jas/skab019
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article ; Online: 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

    Genetics, selection, evolution : GSE

    2017  Volume 49, Issue 1, Page(s) 2

    Abstract: ... to be included in the analysis. The single-step genomic best linear unbiased prediction (SSGBLUP) method ... are imputed implicitly. Single-step Bayesian regression (SSBR) extends SSGBLUP to BayesB-like models ... pedigree-based BLUP when the same phenotypic data were modeled from either genotyped individuals only or ...

    Abstract 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.
    MeSH term(s) Animals ; Bayes Theorem ; Cattle ; Genetic Association Studies ; Genome ; Genome-Wide Association Study ; Genomics/methods ; Genotype ; Models, Genetic ; Models, Statistical ; Phenotype ; Quantitative Trait, Heritable ; Reproducibility of Results
    Language English
    Publishing date 2017--04
    Publishing country France
    Document type Journal Article
    ZDB-ID 1005838-2
    ISSN 1297-9686 ; 0754-0264 ; 0999-193X
    ISSN (online) 1297-9686
    ISSN 0754-0264 ; 0999-193X
    DOI 10.1186/s12711-016-0279-9
    Database MEDical Literature Analysis and Retrieval System OnLINE

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

    Lee, Joonho / Hao Cheng / Dorian Garrick / Bruce Golden / Jack Dekkers / Kyungdo Park / Deukhwan Lee / Rohan Fernando

    Genetics, selection, evolution. 2017 Dec., v. 49, no. 1

    2017  

    Abstract: ... to be included in the analysis. The single-step genomic best linear unbiased prediction (SSGBLUP) method ... are imputed implicitly. Single-step Bayesian regression (SSBR) extends SSGBLUP to BayesB-like models ... thickness (BFT), carcass weight (CWT), eye-muscle area, and marbling score (MAR). Single-trait pedigree ...

    Abstract 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.
    Keywords backfat ; beef cattle ; carcass weight ; genomics ; genotype ; genotyping ; marbling ; models ; pedigree ; phenotype ; prediction ; records ; single nucleotide polymorphism ; steers
    Language English
    Dates of publication 2017-12
    Size p. 2.
    Publishing place BioMed Central
    Document type Article
    ZDB-ID 1005838-2
    ISSN 1297-9686 ; 0754-0264 ; 0999-193X
    ISSN (online) 1297-9686
    ISSN 0754-0264 ; 0999-193X
    DOI 10.1186/s12711-016-0279-9
    Database NAL-Catalogue (AGRICOLA)

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