ArticleGenetics, selection, evolution : GSE2015
A crossbred reference population can improve the response to genomic selection for crossbred performance.
Article in Genetics, selection, evolution : GSE, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 papers.
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42 citing papers in PubMed, 91 citations in OpenAlex.
- Leveraging data from commercial cattle for genetic improvement- an international perspective.Journal of animal science · 2025Review
- Genome-wide selection signatures address trait specific candidate genes in cattle indigenous to arid regions of India.Animal biotechnology · 2024Article
- Genomic Selection for Weaning Weight in Alpine Merino Sheep Based on GWAS Prior Marker Information.Animals : an open access journal from MDPI · 2024Article
- MAGE: metafounders-assisted genomic estimation of breeding value, a novel additive-dominance single-step model in crossbreeding systems.Bioinformatics (Oxford, England) · 2024Article
- Optimizing purebred selection to improve crossbred performance.Frontiers in genetics · 2024Article
- Impacts of reference population size and methods on the accuracy of genomic prediction for fleece traits in Inner Mongolia Cashmere Goats.Frontiers in veterinary science · 2024Article
- Genomic Selection for Live Weight in the 14th Month in Alpine Merino Sheep Combining GWAS Information.Animals : an open access journal from MDPI · 2023Article
- Genomic prediction in hybrid breeding: I. Optimizing the training set design.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2023Article
- A breed-of-origin of alleles model that includes crossbred data improves predictive ability for crossbred animals in a multi-breed population.Genetics, selection, evolution : GSE · 2023Article
- Genomic Prediction Accuracies for Growth and Carcass Traits in a Brangus Heifer Population.Animals : an open access journal from MDPI · 2023Article
- Genomic prediction in pigs using data from a commercial crossbred population: insights from the Duroc x (Landrace x Yorkshire) three-way crossbreeding system.Genetics, selection, evolution : GSE · 2023Article
- Utilizing Variants Identified with Multiple Genome-Wide Association Study Methods Optimizes Genomic Selection for Growth Traits in Pigs.Animals : an open access journal from MDPI · 2023Article
- Genotyping and phenotyping strategies for genetic improvement of meat quality and carcass composition in swine.Genetics, selection, evolution : GSE · 2022Article
- Genetic variation and marker-trait association affect the genomic selection prediction accuracy of soybean protein and oil content.Frontiers in plant science · 2022Article
- Accuracy of Genomic Selection for Important Economic Traits of Cashmere and Meat Goats Assessed by Simulation Study.Frontiers in veterinary science · 2022Article
- Bias in variance component estimation in swine crossbreeding schemes using selective genotyping and phenotyping strategies.Journal of animal science · 2021Article
- Purebred-crossbred genetic parameters for reproductive traits in swine.Journal of animal science · 2021Article
- Review: optimizing genomic selection for crossbred performance by model improvement and data collection.Journal of animal science · 2021Review
- Genomic prediction using a reference population of multiple pure breeds and admixed individuals.Genetics, selection, evolution : GSE · 2021Article
- Selective genotyping and phenotypic data inclusion strategies of crossbred progeny for combined crossbred and purebred selection in swine breeding.Journal of animal science · 2021Article
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Authors and funding
3 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundBreeding goals in a crossbreeding system should be defined at the commercial crossbred level. However, selection is often performed to improve purebred performance. A genomic selection (GS) model that includes dominance effects can be used to select purebreds for crossbred performance. Optimization of the GS model raises the question of whether marker effects should be estimated from data on the pure lines or crossbreds. Therefore, the first objective of this study was to compare response to selection of crossbreds by simulating a two-way crossbreeding program with either a purebred or a crossbred training population. We assumed a trait of interest that was controlled by loci with additive and dominance effects. Animals were selected on estimated breeding values for crossbred performance. There was no genotype by environment interaction. Linkage phase and strength of linkage disequilibrium between quantitative trait loci (QTL) and single nucleotide polymorphisms (SNPs) can differ between breeds, which causes apparent effects of SNPs to be line-dependent. Thus, our second objective was to compare response to GS based on crossbred phenotypes when the line origin of alleles was taken into account or not in the estimation of breeding values.
resultsTraining on crossbred animals yielded a larger response to selection in crossbred offspring compared to training on both pure lines separately or on both pure lines combined into a single reference population. Response to selection in crossbreds was larger if both phenotypes and genotypes were collected on crossbreds than if phenotypes were only recorded on crossbreds and genotypes on their parents. If both parental lines were distantly related, tracing the line origin of alleles improved genomic prediction, whereas if both parental lines were closely related and the reference population was small, it was better to ignore the line origin of alleles.
conclusionsResponse to selection in crossbreeding programs can be increased by training on crossbred genotypes and phenotypes. Moreover, if the reference population is sufficiently large and both pure lines are not very closely related, tracing the line origin of alleles in crossbreds improves genomic prediction.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.