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  A comparative analysis of genomic and phenomic predictions of growth-related traits in 3-way coffee hybrids

Mbebi, A. J., Breitler, J.-C., Bordeaux, M., Sulpice, R., McHale, M., Tong, H., et al. (2022). A comparative analysis of genomic and phenomic predictions of growth-related traits in 3-way coffee hybrids. G3: Genes, Genomes, Genetics, 12(9): jkac170. doi:10.1093/g3journal/jkac170.

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 Creators:
Mbebi, A. J.1, Author           
Breitler, Jean-Christophe2, Author
Bordeaux, M’elanie2, Author
Sulpice, Ronan2, Author
McHale, Marcus2, Author
Tong, H.1, Author           
Toniutti, Lucile2, Author
Castillo, Jonny Alonso2, Author
Bertrand, Benoit2, Author
Nikoloski, Z.1, Author           
Affiliations:
1Mathematical Modelling and Systems Biology - Nikoloski, Cooperative Research Groups, Max Planck Institute of Molecular Plant Physiology, Max Planck Society, ou_1753310              
2external, ou_persistent22              

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 Abstract: Genomic prediction (GP) has revolutionized crop breeding despite remaining issues of transferability of models to unseen environmental conditions and environments. Usage of endophenotypes rather than genomic markers leads to the possibility of building phenomic prediction (PP) models that can account, in part, for this challenge. Here, we compare and contrast GP and PP models for three growth-related traits, namely, leaf count, tree height, and trunk diameter, from two coffee three-way hybrid (H3W) populations exposed to a series of treatment-inducing environmental conditions. The models are based on seven different statistical methods built with genomic markers and chlorophyll a fluorescence (ChlF) data used as predictors. This comparative analysis demonstrates that the best performing PP models show higher predictability than the best GP models for the considered traits and environments in the vast majority of comparisons within H3W populations. In addition, we show that PP models are transferrable between conditions, but to a lower extent between populations and we conclude that ChlF data can serve as alternative predictors in statistical models of coffee hybrid performance. Future directions will explore their combination with other endophenotypes to further improve the prediction of growth-related traits for crops.

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Language(s): eng - English
 Dates: 2022-07-062022-09
 Publication Status: Published in print
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: DOI: 10.1093/g3journal/jkac170
 Degree: -

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Title: G3: Genes, Genomes, Genetics
Source Genre: Journal
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Publ. Info: Bethesda : Genetics Society of America
Pages: - Volume / Issue: 12 (9) Sequence Number: jkac170 Start / End Page: - Identifier: ISSN: 2160-1836
CoNE: https://pure.mpg.de/cone/journals/resource/2160-1836