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Adding gene transcripts into genomic prediction improves accuracy and reveals sampling time dependence

Recent developments allowed generating multiple high-quality ‘omics’ data that could increase the predictive performance of genomic prediction for phenotypes and genetic merit in animals and plants. Here, we have assessed the performance of parametric and nonparametric models that leverage transcrip...

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Autores principales: Perez, Bruno C, Bink, Marco C A M, Svenson, Karen L, Churchill, Gary A, Calus, Mario P L
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9635642/
https://www.ncbi.nlm.nih.gov/pubmed/36161485
http://dx.doi.org/10.1093/g3journal/jkac258
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author Perez, Bruno C
Bink, Marco C A M
Svenson, Karen L
Churchill, Gary A
Calus, Mario P L
author_facet Perez, Bruno C
Bink, Marco C A M
Svenson, Karen L
Churchill, Gary A
Calus, Mario P L
author_sort Perez, Bruno C
collection PubMed
description Recent developments allowed generating multiple high-quality ‘omics’ data that could increase the predictive performance of genomic prediction for phenotypes and genetic merit in animals and plants. Here, we have assessed the performance of parametric and nonparametric models that leverage transcriptomics in genomic prediction for 13 complex traits recorded in 478 animals from an outbred mouse population. Parametric models were implemented using the best linear unbiased prediction, while nonparametric models were implemented using the gradient boosting machine algorithm. We also propose a new model named GTCBLUP that aims to remove between-omics-layer covariance from predictors, whereas its counterpart GTBLUP does not do that. While gradient boosting machine models captured more phenotypic variation, their predictive performance did not exceed the best linear unbiased prediction models for most traits. Models leveraging gene transcripts captured higher proportions of the phenotypic variance for almost all traits when these were measured closer to the moment of measuring gene transcripts in the liver. In most cases, the combination of layers was not able to outperform the best single-omics models to predict phenotypes. Using only gene transcripts, the gradient boosting machine model was able to outperform best linear unbiased prediction for most traits except body weight, but the same pattern was not observed when using both single nucleotide polymorphism genotypes and gene transcripts. Although the GTCBLUP model was not able to produce the most accurate phenotypic predictions, it showed the highest accuracies for breeding values for 9 out of 13 traits. We recommend using the GTBLUP model for prediction of phenotypes and using the GTCBLUP for prediction of breeding values.
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spelling pubmed-96356422022-11-07 Adding gene transcripts into genomic prediction improves accuracy and reveals sampling time dependence Perez, Bruno C Bink, Marco C A M Svenson, Karen L Churchill, Gary A Calus, Mario P L G3 (Bethesda) Investigation Recent developments allowed generating multiple high-quality ‘omics’ data that could increase the predictive performance of genomic prediction for phenotypes and genetic merit in animals and plants. Here, we have assessed the performance of parametric and nonparametric models that leverage transcriptomics in genomic prediction for 13 complex traits recorded in 478 animals from an outbred mouse population. Parametric models were implemented using the best linear unbiased prediction, while nonparametric models were implemented using the gradient boosting machine algorithm. We also propose a new model named GTCBLUP that aims to remove between-omics-layer covariance from predictors, whereas its counterpart GTBLUP does not do that. While gradient boosting machine models captured more phenotypic variation, their predictive performance did not exceed the best linear unbiased prediction models for most traits. Models leveraging gene transcripts captured higher proportions of the phenotypic variance for almost all traits when these were measured closer to the moment of measuring gene transcripts in the liver. In most cases, the combination of layers was not able to outperform the best single-omics models to predict phenotypes. Using only gene transcripts, the gradient boosting machine model was able to outperform best linear unbiased prediction for most traits except body weight, but the same pattern was not observed when using both single nucleotide polymorphism genotypes and gene transcripts. Although the GTCBLUP model was not able to produce the most accurate phenotypic predictions, it showed the highest accuracies for breeding values for 9 out of 13 traits. We recommend using the GTBLUP model for prediction of phenotypes and using the GTCBLUP for prediction of breeding values. Oxford University Press 2022-09-26 /pmc/articles/PMC9635642/ /pubmed/36161485 http://dx.doi.org/10.1093/g3journal/jkac258 Text en © The Author(s) 2022. Published by Oxford University Press on behalf of Genetics Society of America. https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Investigation
Perez, Bruno C
Bink, Marco C A M
Svenson, Karen L
Churchill, Gary A
Calus, Mario P L
Adding gene transcripts into genomic prediction improves accuracy and reveals sampling time dependence
title Adding gene transcripts into genomic prediction improves accuracy and reveals sampling time dependence
title_full Adding gene transcripts into genomic prediction improves accuracy and reveals sampling time dependence
title_fullStr Adding gene transcripts into genomic prediction improves accuracy and reveals sampling time dependence
title_full_unstemmed Adding gene transcripts into genomic prediction improves accuracy and reveals sampling time dependence
title_short Adding gene transcripts into genomic prediction improves accuracy and reveals sampling time dependence
title_sort adding gene transcripts into genomic prediction improves accuracy and reveals sampling time dependence
topic Investigation
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9635642/
https://www.ncbi.nlm.nih.gov/pubmed/36161485
http://dx.doi.org/10.1093/g3journal/jkac258
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