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 551194
Title Incorporating genome-wide association into eco-physiological simulation to identify markers for improving rice yields
Author(s) Kadam, Niteen N.; Jagadish, Krishna S.V.; Struik, Paul C.; Linden, Gerard C. van der; Yin, Xinyou
Source Journal of Experimental Botany 70 (2019)9. - ISSN 0022-0957 - p. 2575 - 2586.
Department(s) Centre for Crop Systems Analysis
Crop Physiology
Plant Breeding
Publication type Refereed Article in a scientific journal
Publication year 2019
Keyword(s) Oryza sativa - Crop modelling - genomic prediction - genotype–phenotype relationships - GWAS - marker design

We explored the use of the eco-physiological crop model GECROS to identify markers for improved rice yield under well-watered (control) and water deficit conditions. Eight model parameters were measured from the control in one season for 267 indica genotypes. The model accounted for 58% of yield variation among genotypes under control and 40% under water deficit conditions. Using 213 randomly selected genotypes as the training set, 90 single nucleotide polymorphism (SNP) loci were identified using a genome-wide association study (GWAS), explaining 42-77% of crop model parameter variation. SNP-based parameter values estimated from the additive loci effects were fed into the model. For the training set, the SNP-based model accounted for 37% (control) and 29% (water deficit) of yield variation, less than the 78% explained by a statistical genomic prediction (GP) model for the control treatment. Both models failed in predicting yields of the 54 testing genotypes. However, compared with the GP model, the SNP-based crop model was advantageous when simulating yields under either control or water stress conditions in an independent season. Crop model sensitivity analysis ranked the SNP loci for their relative importance in accounting for yield variation, and the rank differed greatly between control and water deficit environments. Crop models have the potential to use single-environment information for predicting phenotypes under different environments.

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