Genomic selection in commercial pig breeding

Knol, E.F.; Nielsen, Bjarne; Knap, P. W.

doi:10.2527/af.2016-0003

Cited by 122 publications

(115 citation statements)

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“…Using genomic information within the parental lines [39] allows management of genomic diversity in the crossbred progeny even in the absence of genotypic data on the crossbred animals. In addition, in the purebred lines, regions with a high frequency of ROH are most susceptible to a loss of diversity due to selection and therefore are the most critical regions in which genetic diversity needs to be maintained in the parental populations.…”

Section: Introductionmentioning

confidence: 99%

Characterization and management of long runs of homozygosity in parental nucleus lines and their associated crossbred progeny

Howard

Tiezzi

Huang³

et al. 2016

Genet Sel Evol

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BackgroundIn nucleus populations, regions of the genome that have a high frequency of runs of homozygosity (ROH) occur and are associated with a reduction in genetic diversity, as well as adverse effects on fitness. It is currently unclear whether, and to what extent, ROH stretches persist in the crossbred genome and how genomic management in the nucleus population might impact low diversity regions and its implications on the crossbred genome.MethodsWe calculated a ROH statistic based on lengths of 5 (ROH5) or 10 (ROH10) Mb across the genome for genotyped Landrace (LA), Large White (LW) and Duroc (DU) dams. We simulated crossbred dam (LA × LW) and market [DU × (LA × LW)] animal genotypes based on observed parental genotypes and the ROH frequency was tabulated. We conducted a simulation using observed genotypes to determine the impact of minimizing parental relationships on multiple diversity metrics within nucleus herds, i.e. pedigree-(A), SNP-by-SNP relationship matrix or ROH relationship matrix. Genome-wide metrics included, pedigree inbreeding, heterozygosity and proportion of the genome in ROH of at least 5 Mb. Lastly, the genome was split into bins of increasing ROH5 frequency and, within each bin, heterozygosity, ROH5 and length (Mb) of ROH were evaluated.ResultsWe detected regions showing high frequencies of either ROH5 and/or ROH10 across both LW and LA on SSC1, SSC4, and SSC14, and across all breeds on SSC9. Long haplotypes were shared across parental breeds and thus, regions of ROH persisted in crossbred animals. Averaged across replicates and breeds, progeny had higher levels of heterozygosity (0.0056 ± 0.002%) and lower proportion of the genome in a ROH of at least 5 Mb (−0.015 ± 0.003%) than their parental genomes when genomic relationships were constrained, while pedigree relationships resulted in negligible differences at the genomic level. Across all breeds, only genomic data was able to target low diversity regions.ConclusionsWe show that long stretches of ROH present in the parents persist in crossbred animals. Furthermore, compared to using pedigree relationships, using genomic information to constrain parental relationships resulted in maintaining more genetic diversity and more effectively targeted low diversity regions.Electronic supplementary materialThe online version of this article (doi:10.1186/s12711-016-0269-y) contains supplementary material, which is available to authorized users.

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Section: Introductionmentioning

confidence: 99%

Characterization and management of long runs of homozygosity in parental nucleus lines and their associated crossbred progeny

Howard

Tiezzi

Huang³

et al. 2016

Genet Sel Evol

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“…The top layer is the nucleus, which is improved using recurrent selection. Nowadays most selection decisions are made using genomic based testing rather than traditional phenotype based testing [2][3][4][5][6]. The middle layer is the multiplication, where the nucleus genetics is multiplied and sometimes crosses between purebred lines are produced.…”

Section: Introductionmentioning

confidence: 99%

A strategy to exploit surrogate sire technology in livestock breeding programs

Gottardo

Battagin

Gaynor

et al. 2017

Preprint

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“…The observed trend for PEV held true for both EBV and DEBV response variables. Table 2 shows the means and their corresponding standard deviations for R 2 and PEV for the combinations of PEn and PEv when the response variable was EBV. In general, R 2 values were only slightly lower than those observed when DEBVs were used as response variables.…”

Section: Palabras Clave: Evaluación Genómica Valor Genético Predichomentioning

confidence: 99%

“…An increase in EBV accuracy for selection of candidate animals will spur genetic progress. Among other advantages, the use of genomic selection allows an increase in the accuracy of genetic values (1) , especially at a young age (2) . Three technological breakthroughs have boosted wide-spread DNA information use in animal breeding (3) : the development of genomic selection technology, the discovery of massive numbers of genetic markers (SNPs), and high throughput costeffective genotyping technology.…”

Section: Palabras Clave: Evaluación Genómica Valor Genético Predichomentioning

confidence: 99%

“…Accordingly, another research group (23) concluded that accuracy of GVs deteriorated as the relationship between animals in the PEn and those under selection decreased. One implication of this is that PEn has to be regularly updated to keep the marker effect estimates in sync with new generations of the breeding population (2) .…”

Section: Palabras Clave: Evaluación Genómica Valor Genético Predichomentioning

confidence: 99%

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Accuracy of genomic values predicted using deregressed predicted breeding values as response variables

Ruíz-Flores

García-Muñiz

Domínguez-Viveros

et al. 2017

Rev. Mex. Cienc. Pecu.

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Highly accurate predicted genetic values must be obtained at an early age to promote rapid genetic progress. The objectives of this study were to compare accuracies (R 2 ) of genomic values (GVs) and to estimate genetic correlation between true genetic values and genomic values obtained using predicted breeding values (EBV) and deregressed EBV (DEBV) as response variables. A first population, effective population size 800 and 100 generations, was simulated using the QMSim program to generate linkage disequilibrium. Thereafter, 20 males and 200 females were used to generate a second 14-generation population, with 6,400 individuals per generation and its corresponding phenotype and genotype in SNP terms. Generations 7 to 14 of the second population were used in several combinations as training (PEn) and evaluation (PEv) subpopulations. GVs, their accuracies, and genetic correlations were obtained using the GenSel and ASREML programs. When PEn was the largest, the mean R 2 of GV was the highest, 0.77 ± 0.01. The closer PEn was to PEv, the higher the R 2 , and correspondingly, the lower the predicted error variance. The trends for R 2 and PEV held true for both EBV and DEBV used as response variables. Genetic correlation estimates between true genetic values and GVs varied from 0.41 to 0.53 in the two scenarios studied. They decreased when PEn and PEv were farther apart. There were only slight advantages of using DEBVs as response variables over using EBVs.KEY WORDS: Genomic evaluation, Deregressed predicted genetic value, Genomic predicted value, Accuracy, Genetic correlation. RESUMENLos valores genéticos de individuos en una población deben obtenerse de forma precisa y a edad temprana para promover un progreso genético rápido. Los objetivos de este estudio fueron comparar las exactitudes (R 2 ) de valores genómicos predichos (GBV) y estimar la correlación genética entre los valores genéticos verdaderos (TGV) y los GBV, utilizando los valores genéticos estimados (EBV) y EBV ajustados (DEBV) como variables respuesta. Una primera población de 100 generaciones con tamaño efectivo 800 se simuló con el programa QMSim para generar desequilibrio de ligamiento. Posteriormente, se utilizaron 20 machos y 200 hembras por generación en una segunda población de 14 generaciones, con 6,400 individuos por generación y sus correspondientes fenotipos y genotipos en términos de SNP. Las generaciones 7 a 14 de la segunda población se usaron como subpoblaciones de entrenamiento (PT) y evaluación (PE). Los GBV, sus exactitudes y correlaciones genéticas se obtuvieron utilizando los programas GenSel y ASREML. Cuando la PT fue la más grande, R 2 media fue la más alta, 0.77 ± 0.01. Cuanto más cercana es PT a PE, mayor R 2 , y menor la varianza del error de predicción (PEV). Las tendencias para R 2 y PEV se mantuvieron tanto para EBV como para DEBV utilizadas como variables de respuesta. Los estimadores de correlación genética entre TGV y GBV variaron de 0.41 a 0.53 en los dos escenarios estudiados. La R 2 disminuyó cuando PT y PE es...

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Genomic selection in commercial pig breeding

Cited by 122 publications

References 40 publications

Characterization and management of long runs of homozygosity in parental nucleus lines and their associated crossbred progeny

Characterization and management of long runs of homozygosity in parental nucleus lines and their associated crossbred progeny

A strategy to exploit surrogate sire technology in livestock breeding programs

Accuracy of genomic values predicted using deregressed predicted breeding values as response variables

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