Modelling of genotype by environment interaction and prediction of complex traits across multiple environments as a synthesis of crop growth modelling, genetics and statistics

Abstract

Bustos -Korts, D. (2017). Modelling of Genotype by Environment Interaction and Prediction of Complex Traits across Multiple Environments as a Synthesis of Crop Growth Modelling, Genetics and Statistics. PhD thesis, Wageningen University, the Netherlands.The main objective of plant breeders is to create and identify genotypes that are well-adapted to the target population of environments (TPE). The TPE corresponds to the future growing conditions in which the varieties produced by a breeding program will be grown. All possible genotypes that could be considered as selection candidates for a specific TPE are said to belong to the target population of genotypes, TPG.Genotypes commonly show different sensitivities to environmental gradients and then genotype by environment interaction (GxE) is observed. GxE can lead to changes in genotypic ranking, complicating the breeding process. The main aim of this thesis was to investigate statistical models and the combination of statistical and crop growth models to improve phenotype prediction across multiple environments. One aspect that determines the quality of phenotype prediction is the set of genotypes used to train the prediction model, especially when the TPG is structured. We proposed a method that uniformly covers the genetic space of the TPG, leading to a larger prediction accuracy than random sampling. We produced positive results for wheat, maize and rice. A second aspect that influences the accuracy of phenotype predictions is the choice of environments used to train the prediction model, which should capture the heterogeneity in the TPE. When accounting for heterogeneity in environmental quality, it is important to distinguish between repeatable and well predictable elements in the environmental conditions from those that are badly predictable. We proposed statistical methods based on the AMMI model and on mixed models to identify groups of environments that show repeatable GxE, illustrating our ideas with multienvironment wheat data in North-Western Europe. The importance of training set construction strategies and multi-environment genomic prediction models was also demonstrated for barley data. If breeders are interested in identifying the genetic basis of the target traits, it is advantageous to have a higher SNP density. In this thesis, we used exome sequence data of the EU-Whealbi-barley germplasm, which corresponds to a unique set of genotypes with a diverse origin, growth habit and breeding history.vi For this diverse data, we assessed the effects of QTLs and haplotypes across multiple environments for awn length, grain weight, heading date and plant height. Our results show that the EU-Whealbi-barley collection possesses a large diversity of promising alleles regulating the four traits we analysed. The last major topic addressed in this thesis is the use of a combination of statistical-genetic models and crop growth models (APSIM) as a strategy to assess the traits and phenotyping schemes to improve the prediction accuracy of a target trait like yield...

Candidate gene based association mapping in Fusarium culmorum for field quantitative pathogenicity and mycotoxin production in wheat

“…Conducting experiments as multi-environment trials, using plots rather than single plants as experimental units and applying mixed models in the statistical analysis are standard practices in the study of quantitative traits of plants [75]. These approaches have only recently been applied to study quantitative plant-pathogen relationships [37, 38].…”

Optimization of multi-environment trials for genomic selection based on crop models

“…The interest of using ecophysiological modelling to better model GEI is now well recognized in the plant genetics community (Chapman et al 2002; Hammer et al 2002; Reymond et al 2003; Heslot et al 2014; Technow et al 2015; Bustos-Kort et al 2016). It has been shown in various studies that CGM could be used to structure environments in groups according to the type and frequency of stress experienced by the crop (Löffler et al 2005; Hammer and Jordan 2007; Chenu et al 2011).…”

“…However, photoperiod can vary from one environment to another which generates GEI, because other varieties can have different photoperiod sensitivities. CGM can be used to predict GEI, since they integrate explicitly both variety characteristics (genetic parameters) and environmental covariates (Chapman et al 2002; Hammer et al 2002; Bertin et al 2010; Bustos-Korts et al 2016). Once the genetic parameters have been estimated, their genetic architecture can be determined and GS models can be calibrated.…”

Dealing with multi‐source and multi‐scale information in plant phenomics: the ontology‐driven Phenotyping Hybrid Information System

“…Furthermore, each plant or sub‐plot senses different environmental conditions within a given field, glasshouse or growth chamber (Granier et al ., ; Cabrera‐Bosquet et al ., ), so keeping track of the position of each plant or plot is essential. This is widely accepted for field experiments in view of the large variability of traits and yield within and between fields, which can be accounted for by using mixed models (van Eeuwijk et al ., ; Bustos‐Korts et al ., ). Paradoxically, this is less accepted in controlled conditions in which the spatial distribution of environmental conditions and the x – y positions of plants are seldom stored in databases.…”