Prediction of Complex Traits: Robust Alternatives to Best Linear Unbiased Prediction
A widely used method for prediction of complex traits in animal and plant breeding is “genomic best linear unbiased prediction” (GBLUP). In a quantitative genetics setting, BLUP is a linear regression of phenotypes on a pedigree or on a genomic relationship matrix, depending on the type of input inf...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2018-06-01
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| Series: | Frontiers in Genetics |
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| Online Access: | https://www.frontiersin.org/article/10.3389/fgene.2018.00195/full |
| _version_ | 1818202166592012288 |
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| author | Daniel Gianola Daniel Gianola Daniel Gianola Daniel Gianola Daniel Gianola Alessio Cecchinato Hugo Naya Chris-Carolin Schön |
| author_facet | Daniel Gianola Daniel Gianola Daniel Gianola Daniel Gianola Daniel Gianola Alessio Cecchinato Hugo Naya Chris-Carolin Schön |
| author_sort | Daniel Gianola |
| collection | DOAJ |
| description | A widely used method for prediction of complex traits in animal and plant breeding is “genomic best linear unbiased prediction” (GBLUP). In a quantitative genetics setting, BLUP is a linear regression of phenotypes on a pedigree or on a genomic relationship matrix, depending on the type of input information available. Normality of the distributions of random effects and of model residuals is not required for BLUP but a Gaussian assumption is made implicitly. A potential downside is that Gaussian linear regressions are sensitive to outliers, genetic or environmental in origin. We present simple (relative to a fully Bayesian analysis) to implement robust alternatives to BLUP using a linear model with residual t or Laplace distributions instead of a Gaussian one, and evaluate the methods with milk yield records on Italian Brown Swiss cattle, grain yield data in inbred wheat lines, and using three traits measured on accessions of Arabidopsis thaliana. The methods do not use Markov chain Monte Carlo sampling and model hyper-parameters, viewed here as regularization “knobs,” are tuned via some cross-validation. Uncertainty of predictions are evaluated by employing bootstrapping or by random reconstruction of training and testing sets. It was found (e.g., test-day milk yield in cows, flowering time and FRIGIDA expression in Arabidopsis) that the best predictions were often those obtained with the robust methods. The results obtained are encouraging and stimulate further investigation and generalization. |
| first_indexed | 2024-12-12T03:05:08Z |
| format | Article |
| id | doaj.art-a1a7cb782cda4f4ea802c0f413e3d5d7 |
| institution | Directory Open Access Journal |
| issn | 1664-8021 |
| language | English |
| last_indexed | 2024-12-12T03:05:08Z |
| publishDate | 2018-06-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Genetics |
| spelling | doaj.art-a1a7cb782cda4f4ea802c0f413e3d5d72022-12-22T00:40:32ZengFrontiers Media S.A.Frontiers in Genetics1664-80212018-06-01910.3389/fgene.2018.00195360177Prediction of Complex Traits: Robust Alternatives to Best Linear Unbiased PredictionDaniel Gianola0Daniel Gianola1Daniel Gianola2Daniel Gianola3Daniel Gianola4Alessio Cecchinato5Hugo Naya6Chris-Carolin Schön7Department of Animal Sciences, University of Wisconsin-Madison, Madison, WI, United StatesDepartment of Dairy Science, University of Wisconsin-Madison, Madison, WI, United StatesDepartment of Plant Sciences, TUM School of Life Sciences, Technical University of Munich, Munich, GermanyDepartment of Agronomy, Food Natural Resources, Animals and Environment, Università degli Studi di Padova, Padova, ItalyInstitut Pasteur de Montevideo, Montevideo, UruguayDepartment of Agronomy, Food Natural Resources, Animals and Environment, Università degli Studi di Padova, Padova, ItalyInstitut Pasteur de Montevideo, Montevideo, UruguayDepartment of Plant Sciences, TUM School of Life Sciences, Technical University of Munich, Munich, GermanyA widely used method for prediction of complex traits in animal and plant breeding is “genomic best linear unbiased prediction” (GBLUP). In a quantitative genetics setting, BLUP is a linear regression of phenotypes on a pedigree or on a genomic relationship matrix, depending on the type of input information available. Normality of the distributions of random effects and of model residuals is not required for BLUP but a Gaussian assumption is made implicitly. A potential downside is that Gaussian linear regressions are sensitive to outliers, genetic or environmental in origin. We present simple (relative to a fully Bayesian analysis) to implement robust alternatives to BLUP using a linear model with residual t or Laplace distributions instead of a Gaussian one, and evaluate the methods with milk yield records on Italian Brown Swiss cattle, grain yield data in inbred wheat lines, and using three traits measured on accessions of Arabidopsis thaliana. The methods do not use Markov chain Monte Carlo sampling and model hyper-parameters, viewed here as regularization “knobs,” are tuned via some cross-validation. Uncertainty of predictions are evaluated by employing bootstrapping or by random reconstruction of training and testing sets. It was found (e.g., test-day milk yield in cows, flowering time and FRIGIDA expression in Arabidopsis) that the best predictions were often those obtained with the robust methods. The results obtained are encouraging and stimulate further investigation and generalization.https://www.frontiersin.org/article/10.3389/fgene.2018.00195/fullcomplex traitspredictiongenomic selectionquantitative geneticsgenome-enabled prediction |
| spellingShingle | Daniel Gianola Daniel Gianola Daniel Gianola Daniel Gianola Daniel Gianola Alessio Cecchinato Hugo Naya Chris-Carolin Schön Prediction of Complex Traits: Robust Alternatives to Best Linear Unbiased Prediction Frontiers in Genetics complex traits prediction genomic selection quantitative genetics genome-enabled prediction |
| title | Prediction of Complex Traits: Robust Alternatives to Best Linear Unbiased Prediction |
| title_full | Prediction of Complex Traits: Robust Alternatives to Best Linear Unbiased Prediction |
| title_fullStr | Prediction of Complex Traits: Robust Alternatives to Best Linear Unbiased Prediction |
| title_full_unstemmed | Prediction of Complex Traits: Robust Alternatives to Best Linear Unbiased Prediction |
| title_short | Prediction of Complex Traits: Robust Alternatives to Best Linear Unbiased Prediction |
| title_sort | prediction of complex traits robust alternatives to best linear unbiased prediction |
| topic | complex traits prediction genomic selection quantitative genetics genome-enabled prediction |
| url | https://www.frontiersin.org/article/10.3389/fgene.2018.00195/full |
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