Penalized regression techniques for modeling relationships between metabolites and tomato taste attributes

Menendez, Patricia, Eilers, Paul, Tikunov, Yury, Bovy, Arnaud and van Eeuwijk, Fred (2012) Penalized regression techniques for modeling relationships between metabolites and tomato taste attributes. Euphytica, 183 3: 379-387. doi:10.1007/s10681-011-0374-5


Author Menendez, Patricia
Eilers, Paul
Tikunov, Yury
Bovy, Arnaud
van Eeuwijk, Fred
Title Penalized regression techniques for modeling relationships between metabolites and tomato taste attributes
Journal name Euphytica   Check publisher's open access policy
ISSN 0014-2336
1573-5060
Publication date 2012-02-01
Year available 2011
Sub-type Article (original research)
DOI 10.1007/s10681-011-0374-5
Volume 183
Issue 3
Start page 379
End page 387
Total pages 9
Place of publication Dordrecht, Netherlands
Publisher Springer
Collection year 2013
Language eng
Abstract The search for models which link tomato taste attributes to their metabolic profiling, is a main challenge within the breeding programs that aim to enhance tomato flavor. In this paper, we compared such models calculated by the traditional statistical approach, stepwise regression, with models obtained by the new generation of regression techniques, known as penalized regression or regularization methods. In addition, for penalized regression, different scenarios and various model selection criteria were discussed to conclude that classical crossvalidation, selects models with many superfluous variables whereas model selection criteria such as Bayesian information criterion, seem to be more suitable, when the goal is to find parsimonious models, to explain tomato taste attributes based on metabolic information. An exhaustive comparison of the discussed methodology was done for six sensory traits, showing that the most important covariates were identified by the stepwise regression as well as by some of the penalized regression methods, despite the general disagreement on the size of the regression coefficients between them. In particular, for stepwise regression the coefficients are inflated due to their high variance which is not the case with penalized regression, showing that this new methodology, can be an alternative to obtain more accurate models.
Keyword Penalized regression
Tomato taste attributes
Metabolites
Phenotype prediction
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status Non-UQ
Additional Notes Published online: 6 February 2011. Special issue: Selected Papers from the XIV-th Meeting of Eucarpia Section Biometrics in Plant Breeding.

Document type: Journal Article
Sub-type: Article (original research)
Collections: School of Mathematics and Physics
Non HERDC
 
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Created: Fri, 18 Jan 2013, 09:14:25 EST by Patricia Menendez Galvan on behalf of Mathematics