Staff Publications

Staff Publications

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    'Staff publications' is the digital repository of Wageningen University & Research

    'Staff publications' contains references to publications authored by Wageningen University staff from 1976 onward.

    Publications authored by the staff of the Research Institutes are available from 1995 onwards.

    Full text documents are added when available. The database is updated daily and currently holds about 240,000 items, of which 72,000 in open access.

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Record number 495491
Title Genotype-phenotype modeling considering intermediate level of biological variation : A case study involving sensory traits, metabolites and QTLs in ripe tomatoes
Author(s) Wang, Huange; Paulo, Joao; Kruijer, Willem; Boer, Martin; Jansen, Hans; Tikunov, Yury; Usadel, Björn; Heusden, Sjaak Van; Bovy, Arnaud; Eeuwijk, Fred Van
Source Molecular BioSystems 11 (2015)11. - ISSN 1742-206X - p. 3101 - 3110.
DOI https://doi.org/10.1039/c5mb00477b
Department(s) Biometris (WU MAT)
Biometris (PPO/PRI)
PE&RC
WUR Plant Breeding
WUR PB Breeding for Quality
EPS
Publication type Refereed Article in a scientific journal
Publication year 2015
Abstract

Modeling genotype-phenotype relationships is a central objective in plant genetics and breeding. Commonly, variations in phenotypic traits are modeled directly in relation to variations at the DNA level, regardless of intermediate levels of biological variation. Here we present an integrative method for the simultaneous modeling of a set of multilevel phenotypic responses to variations at the DNA level. More specifically, for ripe tomato fruits, we use Gaussian graphical models and causal inference techniques to learn the dependencies of 24 sensory traits on 29 metabolites and the dependencies of those sensory and metabolic traits on 21 QTLs. The inferred dependency network which, though not essentially representing biological pathways, suggests how the effects of allele substitutions propagate through multilevel phenotypes. Such simultaneous study of the underlying genetic architecture and multifactorial interactions is expected to enhance the prediction and manipulation of complex traits.

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