Modeling additive and non-additive effects in a hybrid population using genome-wide genotyping: Prediction accuracy implications
Hybrids are broadly used in plant breeding and accurate estimation of variance components is crucial for optimizing genetic gain. Genome-wide information may be used to explore models designed to assess the extent of additive and non-additive variance and test their prediction accuracy for the genomic selection. Ten linear mixed models, involving pedigree- and marker- based relationship matrices among parents, were developed to estimate additive (A), dominance (D) and epistatic (AA, AD and DD) effects. Five complementary models, involving the gametic phase to estimate marker-based relationships among hybrid progenies, were developed to assess the same effects. The models were compared using tree height and 3303 single-nucleotide polymorphism markers from 1130 cloned individuals obtained via controlled crosses of 13 Eucalyptus urophylla females with 9 Eucalyptus grandis males. Akaike information criterion (AIC), variance ratios, asymptotic correlation matrices of estimates, goodness-of- fi t, prediction accuracy and mean square error (MSE) were used for the comparisons. The variance components and variance ratios differed according to the model. Models with a parent marker-based relationship matrix performed better than those that were pedigree-based, that is, an absence of singularities, lower AIC, higher goodness-of- fi t and accuracy and smaller MSE. However, AD and DD variances were estimated with high s.es. Using the same criteria, progeny gametic phase-based models performed better in fi tting the observations and predicting genetic values. However, DD variance could not be separated from the dominance variance and null estimates were obtained for AA and AD effects. This study highlighted the advantages of progeny models using genome-wide information.
id |
dig-cirad-fr-577277 |
---|---|
record_format |
koha |
spelling |
dig-cirad-fr-5772772024-01-28T22:51:21Z http://agritrop.cirad.fr/577277/ http://agritrop.cirad.fr/577277/ Modeling additive and non-additive effects in a hybrid population using genome-wide genotyping: Prediction accuracy implications. Bouvet Jean-Marc, Makouanzi Garel, Cros David, Vigneron Philippe. 2016. Heredity, 116 : 146-157.https://doi.org/10.1038/hdy.2015.78 <https://doi.org/10.1038/hdy.2015.78> Modeling additive and non-additive effects in a hybrid population using genome-wide genotyping: Prediction accuracy implications Bouvet, Jean-Marc Makouanzi, Garel Cros, David Vigneron, Philippe eng 2016 Heredity F30 - Génétique et amélioration des plantes U10 - Informatique, mathématiques et statistiques K10 - Production forestière Eucalyptus grandis Eucalyptus urophylla génotype sélection hybride génomique marqueur génétique modèle linéaire polymorphisme génétique modèle mathématique méthode statistique essai de provenances héritabilité génotypique http://aims.fao.org/aos/agrovoc/c_2693 http://aims.fao.org/aos/agrovoc/c_26492 http://aims.fao.org/aos/agrovoc/c_3225 http://aims.fao.org/aos/agrovoc/c_6951 http://aims.fao.org/aos/agrovoc/c_3707 http://aims.fao.org/aos/agrovoc/c_92382 http://aims.fao.org/aos/agrovoc/c_24030 http://aims.fao.org/aos/agrovoc/c_34040 http://aims.fao.org/aos/agrovoc/c_24031 http://aims.fao.org/aos/agrovoc/c_24199 http://aims.fao.org/aos/agrovoc/c_7377 http://aims.fao.org/aos/agrovoc/c_36850 http://aims.fao.org/aos/agrovoc/c_1373982832607 République démocratique du Congo http://aims.fao.org/aos/agrovoc/c_8500 Hybrids are broadly used in plant breeding and accurate estimation of variance components is crucial for optimizing genetic gain. Genome-wide information may be used to explore models designed to assess the extent of additive and non-additive variance and test their prediction accuracy for the genomic selection. Ten linear mixed models, involving pedigree- and marker- based relationship matrices among parents, were developed to estimate additive (A), dominance (D) and epistatic (AA, AD and DD) effects. Five complementary models, involving the gametic phase to estimate marker-based relationships among hybrid progenies, were developed to assess the same effects. The models were compared using tree height and 3303 single-nucleotide polymorphism markers from 1130 cloned individuals obtained via controlled crosses of 13 Eucalyptus urophylla females with 9 Eucalyptus grandis males. Akaike information criterion (AIC), variance ratios, asymptotic correlation matrices of estimates, goodness-of- fi t, prediction accuracy and mean square error (MSE) were used for the comparisons. The variance components and variance ratios differed according to the model. Models with a parent marker-based relationship matrix performed better than those that were pedigree-based, that is, an absence of singularities, lower AIC, higher goodness-of- fi t and accuracy and smaller MSE. However, AD and DD variances were estimated with high s.es. Using the same criteria, progeny gametic phase-based models performed better in fi tting the observations and predicting genetic values. However, DD variance could not be separated from the dominance variance and null estimates were obtained for AA and AD effects. This study highlighted the advantages of progeny models using genome-wide information. article info:eu-repo/semantics/article Journal Article info:eu-repo/semantics/publishedVersion http://agritrop.cirad.fr/577277/7/577277.pdf text Cirad license info:eu-repo/semantics/restrictedAccess https://agritrop.cirad.fr/mention_legale.html https://doi.org/10.1038/hdy.2015.78 10.1038/hdy.2015.78 info:eu-repo/semantics/altIdentifier/doi/10.1038/hdy.2015.78 info:eu-repo/semantics/altIdentifier/purl/https://doi.org/10.1038/hdy.2015.78 info:eu-repo/semantics/dataset/purl/https://doi.org/10.5061/dryad.g73t2 |
institution |
CIRAD FR |
collection |
DSpace |
country |
Francia |
countrycode |
FR |
component |
Bibliográfico |
access |
En linea |
databasecode |
dig-cirad-fr |
tag |
biblioteca |
region |
Europa del Oeste |
libraryname |
Biblioteca del CIRAD Francia |
language |
eng |
topic |
F30 - Génétique et amélioration des plantes U10 - Informatique, mathématiques et statistiques K10 - Production forestière Eucalyptus grandis Eucalyptus urophylla génotype sélection hybride génomique marqueur génétique modèle linéaire polymorphisme génétique modèle mathématique méthode statistique essai de provenances héritabilité génotypique http://aims.fao.org/aos/agrovoc/c_2693 http://aims.fao.org/aos/agrovoc/c_26492 http://aims.fao.org/aos/agrovoc/c_3225 http://aims.fao.org/aos/agrovoc/c_6951 http://aims.fao.org/aos/agrovoc/c_3707 http://aims.fao.org/aos/agrovoc/c_92382 http://aims.fao.org/aos/agrovoc/c_24030 http://aims.fao.org/aos/agrovoc/c_34040 http://aims.fao.org/aos/agrovoc/c_24031 http://aims.fao.org/aos/agrovoc/c_24199 http://aims.fao.org/aos/agrovoc/c_7377 http://aims.fao.org/aos/agrovoc/c_36850 http://aims.fao.org/aos/agrovoc/c_1373982832607 http://aims.fao.org/aos/agrovoc/c_8500 F30 - Génétique et amélioration des plantes U10 - Informatique, mathématiques et statistiques K10 - Production forestière Eucalyptus grandis Eucalyptus urophylla génotype sélection hybride génomique marqueur génétique modèle linéaire polymorphisme génétique modèle mathématique méthode statistique essai de provenances héritabilité génotypique http://aims.fao.org/aos/agrovoc/c_2693 http://aims.fao.org/aos/agrovoc/c_26492 http://aims.fao.org/aos/agrovoc/c_3225 http://aims.fao.org/aos/agrovoc/c_6951 http://aims.fao.org/aos/agrovoc/c_3707 http://aims.fao.org/aos/agrovoc/c_92382 http://aims.fao.org/aos/agrovoc/c_24030 http://aims.fao.org/aos/agrovoc/c_34040 http://aims.fao.org/aos/agrovoc/c_24031 http://aims.fao.org/aos/agrovoc/c_24199 http://aims.fao.org/aos/agrovoc/c_7377 http://aims.fao.org/aos/agrovoc/c_36850 http://aims.fao.org/aos/agrovoc/c_1373982832607 http://aims.fao.org/aos/agrovoc/c_8500 |
spellingShingle |
F30 - Génétique et amélioration des plantes U10 - Informatique, mathématiques et statistiques K10 - Production forestière Eucalyptus grandis Eucalyptus urophylla génotype sélection hybride génomique marqueur génétique modèle linéaire polymorphisme génétique modèle mathématique méthode statistique essai de provenances héritabilité génotypique http://aims.fao.org/aos/agrovoc/c_2693 http://aims.fao.org/aos/agrovoc/c_26492 http://aims.fao.org/aos/agrovoc/c_3225 http://aims.fao.org/aos/agrovoc/c_6951 http://aims.fao.org/aos/agrovoc/c_3707 http://aims.fao.org/aos/agrovoc/c_92382 http://aims.fao.org/aos/agrovoc/c_24030 http://aims.fao.org/aos/agrovoc/c_34040 http://aims.fao.org/aos/agrovoc/c_24031 http://aims.fao.org/aos/agrovoc/c_24199 http://aims.fao.org/aos/agrovoc/c_7377 http://aims.fao.org/aos/agrovoc/c_36850 http://aims.fao.org/aos/agrovoc/c_1373982832607 http://aims.fao.org/aos/agrovoc/c_8500 F30 - Génétique et amélioration des plantes U10 - Informatique, mathématiques et statistiques K10 - Production forestière Eucalyptus grandis Eucalyptus urophylla génotype sélection hybride génomique marqueur génétique modèle linéaire polymorphisme génétique modèle mathématique méthode statistique essai de provenances héritabilité génotypique http://aims.fao.org/aos/agrovoc/c_2693 http://aims.fao.org/aos/agrovoc/c_26492 http://aims.fao.org/aos/agrovoc/c_3225 http://aims.fao.org/aos/agrovoc/c_6951 http://aims.fao.org/aos/agrovoc/c_3707 http://aims.fao.org/aos/agrovoc/c_92382 http://aims.fao.org/aos/agrovoc/c_24030 http://aims.fao.org/aos/agrovoc/c_34040 http://aims.fao.org/aos/agrovoc/c_24031 http://aims.fao.org/aos/agrovoc/c_24199 http://aims.fao.org/aos/agrovoc/c_7377 http://aims.fao.org/aos/agrovoc/c_36850 http://aims.fao.org/aos/agrovoc/c_1373982832607 http://aims.fao.org/aos/agrovoc/c_8500 Bouvet, Jean-Marc Makouanzi, Garel Cros, David Vigneron, Philippe Modeling additive and non-additive effects in a hybrid population using genome-wide genotyping: Prediction accuracy implications |
description |
Hybrids are broadly used in plant breeding and accurate estimation of variance components is crucial for optimizing genetic gain. Genome-wide information may be used to explore models designed to assess the extent of additive and non-additive variance and test their prediction accuracy for the genomic selection. Ten linear mixed models, involving pedigree- and marker- based relationship matrices among parents, were developed to estimate additive (A), dominance (D) and epistatic (AA, AD and DD) effects. Five complementary models, involving the gametic phase to estimate marker-based relationships among hybrid progenies, were developed to assess the same effects. The models were compared using tree height and 3303 single-nucleotide polymorphism markers from 1130 cloned individuals obtained via controlled crosses of 13 Eucalyptus urophylla females with 9 Eucalyptus grandis males. Akaike information criterion (AIC), variance ratios, asymptotic correlation matrices of estimates, goodness-of- fi t, prediction accuracy and mean square error (MSE) were used for the comparisons. The variance components and variance ratios differed according to the model. Models with a parent marker-based relationship matrix performed better than those that were pedigree-based, that is, an absence of singularities, lower AIC, higher goodness-of- fi t and accuracy and smaller MSE. However, AD and DD variances were estimated with high s.es. Using the same criteria, progeny gametic phase-based models performed better in fi tting the observations and predicting genetic values. However, DD variance could not be separated from the dominance variance and null estimates were obtained for AA and AD effects. This study highlighted the advantages of progeny models using genome-wide information. |
format |
article |
topic_facet |
F30 - Génétique et amélioration des plantes U10 - Informatique, mathématiques et statistiques K10 - Production forestière Eucalyptus grandis Eucalyptus urophylla génotype sélection hybride génomique marqueur génétique modèle linéaire polymorphisme génétique modèle mathématique méthode statistique essai de provenances héritabilité génotypique http://aims.fao.org/aos/agrovoc/c_2693 http://aims.fao.org/aos/agrovoc/c_26492 http://aims.fao.org/aos/agrovoc/c_3225 http://aims.fao.org/aos/agrovoc/c_6951 http://aims.fao.org/aos/agrovoc/c_3707 http://aims.fao.org/aos/agrovoc/c_92382 http://aims.fao.org/aos/agrovoc/c_24030 http://aims.fao.org/aos/agrovoc/c_34040 http://aims.fao.org/aos/agrovoc/c_24031 http://aims.fao.org/aos/agrovoc/c_24199 http://aims.fao.org/aos/agrovoc/c_7377 http://aims.fao.org/aos/agrovoc/c_36850 http://aims.fao.org/aos/agrovoc/c_1373982832607 http://aims.fao.org/aos/agrovoc/c_8500 |
author |
Bouvet, Jean-Marc Makouanzi, Garel Cros, David Vigneron, Philippe |
author_facet |
Bouvet, Jean-Marc Makouanzi, Garel Cros, David Vigneron, Philippe |
author_sort |
Bouvet, Jean-Marc |
title |
Modeling additive and non-additive effects in a hybrid population using genome-wide genotyping: Prediction accuracy implications |
title_short |
Modeling additive and non-additive effects in a hybrid population using genome-wide genotyping: Prediction accuracy implications |
title_full |
Modeling additive and non-additive effects in a hybrid population using genome-wide genotyping: Prediction accuracy implications |
title_fullStr |
Modeling additive and non-additive effects in a hybrid population using genome-wide genotyping: Prediction accuracy implications |
title_full_unstemmed |
Modeling additive and non-additive effects in a hybrid population using genome-wide genotyping: Prediction accuracy implications |
title_sort |
modeling additive and non-additive effects in a hybrid population using genome-wide genotyping: prediction accuracy implications |
url |
http://agritrop.cirad.fr/577277/ http://agritrop.cirad.fr/577277/7/577277.pdf |
work_keys_str_mv |
AT bouvetjeanmarc modelingadditiveandnonadditiveeffectsinahybridpopulationusinggenomewidegenotypingpredictionaccuracyimplications AT makouanzigarel modelingadditiveandnonadditiveeffectsinahybridpopulationusinggenomewidegenotypingpredictionaccuracyimplications AT crosdavid modelingadditiveandnonadditiveeffectsinahybridpopulationusinggenomewidegenotypingpredictionaccuracyimplications AT vigneronphilippe modelingadditiveandnonadditiveeffectsinahybridpopulationusinggenomewidegenotypingpredictionaccuracyimplications |
_version_ |
1792498875139883008 |