Nonparametric Methods for Incorporating Genomic Information Into Genetic Evaluations: An Application to Mortality in Broilers

González-Recio, Óscar; Gianola, Daniel; Long, Norton E.; Weigel, K.A.; Rosa, Guilherme J. M.; Avendaño, S.

doi:10.1534/genetics.107.084293

Cited by 104 publications

(102 citation statements)

References 36 publications

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“…The availability of thousands of genome-wide molecular markers has made possible the use of genomic selection (GS) for prediction of genetic values (Meuwissen et al 2001) in plants (e.g., Bernardo and Yu 2007;Piepho 2009;Jannink et al 2010) and animals (Gonzalez-Recio et al 2008;VanRaden et al 2008;Hayes et al 2009;de los Campos et al 2009a). Implementing GS poses several statistical and computational challenges, such as how models can cope with the curse of dimensionality, colinearity between markers, or the complexity of quantitative traits.…”

Section: P Edigree-based Prediction Of Genetic Valuesmentioning

confidence: 99%

Prediction of Genetic Values of Quantitative Traits in Plant Breeding Using Pedigree and Molecular Markers

et al. 2010

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The availability of dense molecular markers has made possible the use of genomic selection (GS) for plant breeding. However, the evaluation of models for GS in real plant populations is very limited. This article evaluates the performance of parametric and semiparametric models for GS using wheat (Triticum aestivum L.) and maize (Zea mays) data in which different traits were measured in several environmental conditions. The findings, based on extensive cross-validations, indicate that models including marker information had higher predictive ability than pedigree-based models. In the wheat data set, and relative to a pedigree model, gains in predictive ability due to inclusion of markers ranged from 7.7 to 35.7%. Correlation between observed and predictive values in the maize data set achieved values up to 0.79. Estimates of marker effects were different across environmental conditions, indicating that genotype 3 environment interaction is an important component of genetic variability. These results indicate that GS in plant breeding can be an effective strategy for selecting among lines whose phenotypes have yet to be observed.

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Section: P Edigree-based Prediction Of Genetic Valuesmentioning

confidence: 99%

Prediction of Genetic Values of Quantitative Traits in Plant Breeding Using Pedigree and Molecular Markers

et al. 2010

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“…This reference population typically comprises at least 1000 individuals that have reliable phenotypic as well as genotypic information. This phenotypic information could be own phenotypic performance, but also breeding values obtained from (national) evaluations based on phenotypic information (De Roos et al, 2007;De Roos et al, 2009;Su, 2009), deregressed proofs (Berry et al, 2009;Schenkel et al, 2009;VanRaden et al, 2009), daughter-yield deviations or average offspring performance (Gonzá lez-Recio et al, 2008). By linking the genotypic and phenotypic information together, estimates for each of the SNPs are obtained.…”

Section: Genomic Prediction -The Processmentioning

confidence: 99%

Genomic breeding value prediction: methods and procedures

Calus

2010

Animal

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Animal breeding faces one of the most significant changes of the past decades -the implementation of genomic selection. Genomic selection uses dense marker maps to predict the breeding value of animals with reported accuracies that are up to 0.31 higher than those of pedigree indexes, without the need to phenotype the animals themselves, or close relatives thereof. The basic principle is that because of the high marker density, each quantitative trait loci (QTL) is in linkage disequilibrium (LD) with at least one nearby marker. The process involves putting a reference population together of animals with known phenotypes and genotypes to estimate the marker effects. Marker effects have been estimated with several different methods that generally aim at reducing the dimensions of the marker data. Nearly all reported models only included additive effects. Once the marker effects are estimated, breeding values of young selection candidates can be predicted with reported accuracies up to 0.85. Although results from simulation studies suggest that different models may yield more accurate genomic estimated breeding values (GEBVs) for different traits, depending on the underlying QTL distribution of the trait, there is so far only little evidence from studies based on real data to support this. The accuracy of genomic predictions strongly depends on characteristics of the reference populations, such as number of animals, number of markers, and the heritability of the recorded phenotype. Another important factor is the relationship between animals in the reference population and the evaluated animals. The breakup of LD between markers and QTL across generations advocates frequent re-estimation of marker effects to maintain the accuracy of GEBVs at an acceptable level. Therefore, at low frequencies of re-estimating marker effects, it becomes more important that the model that estimates the marker effects capitalizes on LD information that is persistent across generations.

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“…Genomic selection permits estimation of genomic breeding values, or GEBV, for economically relevant traits (González‐Recio et al . 2008; De los Campos et al . 2009; Hayes et al .…”

Section: Introductionmentioning

confidence: 99%

Meta‐analysis of genome‐wide association from genomic prediction models

Rubio

Duarte²,

Bates³

et al. 2015

Animal Genetics

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SummaryGenome‐wide association (GWA) studies based on GBLUP models are a common practice in animal breeding. However, effect sizes of GWA tests are small, requiring larger sample sizes to enhance power of detection of rare variants. Because of difficulties in increasing sample size in animal populations, one alternative is to implement a meta‐analysis (MA), combining information and results from independent GWA studies. Although this methodology has been used widely in human genetics, implementation in animal breeding has been limited. Thus, we present methods to implement a MA of GWA, describing the proper approach to compute weights derived from multiple genomic evaluations based on animal‐centric GBLUP models. Application to real datasets shows that MA increases power of detection of associations in comparison with population‐level GWA, allowing for population structure and heterogeneity of variance components across populations to be accounted for. Another advantage of MA is that it does not require access to genotype data that is required for a joint analysis. Scripts related to the implementation of this approach, which consider the strength of association as well as the sign, are distributed and thus account for heterogeneity in association phase between QTL and SNPs. Thus, MA of GWA is an attractive alternative to summarizing results from multiple genomic studies, avoiding restrictions with genotype data sharing, definition of fixed effects and different scales of measurement of evaluated traits.

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Nonparametric Methods for Incorporating Genomic Information Into Genetic Evaluations: An Application to Mortality in Broilers

Cited by 104 publications

References 36 publications

Prediction of Genetic Values of Quantitative Traits in Plant Breeding Using Pedigree and Molecular Markers

Prediction of Genetic Values of Quantitative Traits in Plant Breeding Using Pedigree and Molecular Markers

Genomic breeding value prediction: methods and procedures

Meta‐analysis of genome‐wide association from genomic prediction models

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