Modelling strategies for assessing and increasing the effectiveness of new phenotyping techniques in plant breeding. (May 2019)

• Record Type: Journal Article
• Authors: van Eeuwijk, Fred A.; Bustos-Korts, Daniela; Millet, Emilie J.; Boer, Martin P.; Kruijer, Willem; Thompson, Addie; Malosetti, Marcos; Iwata, Hiroyoshi; Quiroz, Roberto; Kuppe, Christian; Muller, Onno; Blazakis, Konstantinos N.; Yu, Kang; Tardieu, Francois; Chapman, Scott C.
• Is Part Of: Plant science. Volume 282(2019)
• Page Start: 23
• Page End: 39
• Publication Date: 2019-05
• Subjects: APSIM Agricultural Production Systems sIMulator -- BLUP best linear unbiased predictor -- BLUE best linear unbiased estimator -- CGM crop growth model -- G×E genotype by environment (interaction) -- G2P genotype to phenotype -- HTP high throughput phenotyping -- NDVI normalized difference vegetation index -- MET multi-environment trial -- QTL quantitative trait locus -- SEM structural equations model -- SNP single nucleotide polymorphism -- SpATS spatial analysis of field trials with splines -- TPE target population of environments -- VCOV variance-covariance; Crop growth model -- Genomic prediction -- Genotype-by-environment-interaction -- Genotype-to-phenotype model -- Mixed model -- Multi-environment model -- Multi-trait model -- Phenotyping -- Phenotyping platform -- Physiology -- Plant breeding -- Prediction -- Reaction norm -- Response surface -- Statistical genetics; Botany -- Periodicals; Botanique -- Périodiques; 580
• DOI: 10.1016/j.plantsci.2018.06.018
• Languages: English
• ISSNs: 0168-9452

Abstract

Highlights: We present various genotype to phenotype models to predict complex phenotypes with G×E as function of genotypic and environmental inputs. We show how genotype-to-phenotype models can be generalized to incorporate additional phenotypic information to improve yield predictions. We discuss how to evaluate the utility of information from new phenotyping techniques in the context of predictive genotype-to-phenotype models. Abstract: New types of phenotyping tools generate large amounts of data on many aspects of plant physiology and morphology with high spatial and temporal resolution. These new phenotyping data are potentially useful to improve understanding and prediction of complex traits, like yield, that are characterized by strong environmental context dependencies, i.e., genotype by environment interactions. For an evaluation of the utility of new phenotyping information, we will look at how this information can be incorporated in different classes of genotype-to-phenotype (G2P) models. G2P models predict phenotypic traits as functions of genotypic and environmental inputs. In the last decade, access to high-density single nucleotide polymorphism markers (SNPs) and sequence information has boosted the development of a class of G2P models called genomic prediction models that predict phenotypes from genome wide marker profiles. The challenge now is to build G2P models that incorporate simultaneously extensive genomic information alongside with new phenotypic information. Beyond the modification of existing G2P models, new G2P paradigms are required. We present candidate G2P models for the integration of genomic and new phenotyping information and illustrate their use in examples. Special attention will be given to the modelling of genotype by environment interactions. The G2P models provide a framework for model based phenotyping and the evaluation of the utility of phenotyping information in the context of breeding programs.