Incorporating genome-wide association into eco-physiological simulation to identify markers for improving rice yields

Niteen N. Kadam, Krishna S.V. Jagadish, Paul C. Struik, Gerard C. van der Linden, Xinyou Yin*

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

1 Citation (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)2575-2586
Number of pages12
JournalJournal of Experimental Botany
Volume70
Issue number9
DOIs
Publication statusPublished - 29 Apr 2019

Fingerprint

Single Nucleotide Polymorphism
crop models
Genome
single nucleotide polymorphism
rice
Genotype
genome
Water
genotype
loci
Genome-Wide Association Study
water
Dehydration
genomics
prediction
Oryza
Phenotype
water stress
phenotype
testing

Keywords

  • Oryza sativa
  • Crop modelling
  • genomic prediction
  • genotype–phenotype relationships
  • GWAS
  • marker design

Cite this

@article{9813283f85dc4603acb46c897c283888,
title = "Incorporating genome-wide association into eco-physiological simulation to identify markers for improving rice yields",
abstract = "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.",
keywords = "Oryza sativa, Crop modelling, genomic prediction, genotype–phenotype relationships, GWAS, marker design",
author = "Kadam, {Niteen N.} and Jagadish, {Krishna S.V.} and Struik, {Paul C.} and {van der Linden}, {Gerard C.} and Xinyou Yin",
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Incorporating genome-wide association into eco-physiological simulation to identify markers for improving rice yields. / Kadam, Niteen N.; Jagadish, Krishna S.V.; Struik, Paul C.; van der Linden, Gerard C.; Yin, Xinyou.

In: Journal of Experimental Botany, Vol. 70, No. 9, 29.04.2019, p. 2575-2586.

Research output: Contribution to journalArticleAcademicpeer-review

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AU - Jagadish, Krishna S.V.

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AU - Yin, Xinyou

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N2 - 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.

AB - 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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