Literature DB >> 35451784

Phenomic Selection: A New and Efficient Alternative to Genomic Selection.

Pauline Robert1,2, Charlotte Brault3,4,5, Renaud Rincent1,2, Vincent Segura6,7.   

Abstract

Recently, it has been proposed to switch molecular markers to near-infrared (NIR) spectra for inferring relationships between individuals and further performing phenomic selection (PS), analogous to genomic selection (GS). The PS concept is similar to genomic-like omics-based (GLOB) selection, in which molecular markers are replaced by endophenotypes, such as metabolites or transcript levels, except that the phenomic information obtained for instance by near-infrared spectroscopy (NIRS ) has usually a much lower cost than other omics. Though NIRS has been routinely used in breeding for several decades, especially to deal with end-product quality traits, its use to predict other traits of interest and further make selections is new. Since the seminal paper on PS , several publications have advocated the use of spectral acquisition (including NIRS and hyperspectral imaging) in plant breeding towards PS , potentially providing a scope of what is possible. In the present chapter, we first come back to the concept of PS as originally proposed and provide a classification of selected papers related to the use of phenomics in breeding. We further provide a review of the selected literature concerning the type of technology used, the preprocessing of the spectra, and the statistical modeling to make predictions. We discuss the factors that likely affect the efficiency of PS and compare it to GS in terms of predictive ability. Finally, we propose several prospects for future work and application of PS in the context of plant breeding.
© 2022. The Author(s).

Entities:  

Keywords:  Genomic selection (GS); Genomic-like omics-based (GLOB) selection; Hyperspectral imaging; Near-infrared spectroscopy (NIRS ); Phenomic selection (PS); Plant breeding

Mesh:

Year:  2022        PMID: 35451784     DOI: 10.1007/978-1-0716-2205-6_14

Source DB:  PubMed          Journal:  Methods Mol Biol        ISSN: 1064-3745


  19 in total

1.  Genomic and metabolic prediction of complex heterotic traits in hybrid maize.

Authors:  Christian Riedelsheimer; Angelika Czedik-Eysenberg; Christoph Grieder; Jan Lisec; Frank Technow; Ronan Sulpice; Thomas Altmann; Mark Stitt; Lothar Willmitzer; Albrecht E Melchinger
Journal:  Nat Genet       Date:  2012-01-15       Impact factor: 38.330

2.  Beyond Genomic Prediction: Combining Different Types of omics Data Can Improve Prediction of Hybrid Performance in Maize.

Authors:  Tobias A Schrag; Matthias Westhues; Wolfgang Schipprack; Felix Seifert; Alexander Thiemann; Stefan Scholten; Albrecht E Melchinger
Journal:  Genetics       Date:  2018-01-23       Impact factor: 4.562

3.  Metabolomic prediction of yield in hybrid rice.

Authors:  Shizhong Xu; Yang Xu; Liang Gong; Qifa Zhang
Journal:  Plant J       Date:  2016-08-29       Impact factor: 6.417

4.  Accelerating wheat breeding for end-use quality with multi-trait genomic predictions incorporating near infrared and nuclear magnetic resonance-derived phenotypes.

Authors:  B J Hayes; J Panozzo; C K Walker; A L Choy; S Kant; D Wong; J Tibbits; H D Daetwyler; S Rochfort; M J Hayden; G C Spangenberg
Journal:  Theor Appl Genet       Date:  2017-08-24       Impact factor: 5.699

5.  Differentially penalized regression to predict agronomic traits from metabolites and markers in wheat.

Authors:  Jane Ward; Mariann Rakszegi; Zoltán Bedő; Peter R Shewry; Ian Mackay
Journal:  BMC Genet       Date:  2015-02-26       Impact factor: 2.797

6.  Deducing hybrid performance from parental metabolic profiles of young primary roots of maize by using a multivariate diallel approach.

Authors:  Kristen Feher; Jan Lisec; Lilla Römisch-Margl; Joachim Selbig; Alfons Gierl; Hans-Peter Piepho; Zoran Nikoloski; Lothar Willmitzer
Journal:  PLoS One       Date:  2014-01-07       Impact factor: 3.240

Review 7.  Fortune telling: metabolic markers of plant performance.

Authors:  Olivier Fernandez; Maria Urrutia; Stéphane Bernillon; Catherine Giauffret; François Tardieu; Jacques Le Gouis; Nicolas Langlade; Alain Charcosset; Annick Moing; Yves Gibon
Journal:  Metabolomics       Date:  2016-09-15       Impact factor: 4.290

8.  Predicting grain yield using canopy hyperspectral reflectance in wheat breeding data.

Authors:  Osval A Montesinos-López; Abelardo Montesinos-López; José Crossa; Gustavo de Los Campos; Gregorio Alvarado; Mondal Suchismita; Jessica Rutkoski; Lorena González-Pérez; Juan Burgueño
Journal:  Plant Methods       Date:  2017-01-03       Impact factor: 4.993

9.  Integration of genotypic, hyperspectral, and phenotypic data to improve biomass yield prediction in hybrid rye.

Authors:  Rodrigo José Galán; Angela-Maria Bernal-Vasquez; Christian Jebsen; Hans-Peter Piepho; Patrick Thorwarth; Philipp Steffan; Andres Gordillo; Thomas Miedaner
Journal:  Theor Appl Genet       Date:  2020-07-17       Impact factor: 5.699

10.  Evaluation of the utility of gene expression and metabolic information for genomic prediction in maize.

Authors:  Zhigang Guo; Michael M Magwire; Christopher J Basten; Zhanyou Xu; Daolong Wang
Journal:  Theor Appl Genet       Date:  2016-09-01       Impact factor: 5.699

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