High-Throughput Field Imaging and Basic Image Analysis in a Wheat Breeding Programme

Visual assessment of colour-based traits plays a key role within field-crop breeding programmes, though the process is subjective and time-consuming. Digital image analysis has previously been investigated as an objective alternative to visual assessment for a limited number of traits, showing suitability and slight improvement to throughput over visual assessment. However, easily adoptable, field-based high-throughput methods are still lacking. The aim of the current study was to produce a high-throughput digital imaging and analysis pipeline for the assessment of colour-based traits within a wheat breeding programme. This was achieved through the steps of (i) a proof-of-concept study demonstrating basic image analysis methods in a greenhouse, (ii) application of these methods to field trials using hand-held imaging, and (iii) developing a field-based high-throughput imaging infrastructure for data collection. The proof of concept study showed a strong correlation (r = 0.95) between visual and digital assessments of wheat physiological yellowing (PY) in a greenhouse environment, with both scores having similar heritability (H2 = 0.85 and 0.76, respectively). Digital assessment of hand-held field images showed strong correlations to visual scores for PY (r = 0.61 and 0.78), senescence (r = 0.74 and 0.75) and Septoria tritici blotch (STB; r = 0.76), with greater heritability of digital scores, excluding STB. Development of the high-throughput imaging infrastructure allowed for images of field plots to be collected at a rate of 7,400 plots per hour. Images of an advanced breeding trial collected with this system were analysed for canopy cover at two time-points, with digital scores correlating strongly to visual scores (r = 0.88 and 0.86) and having similar or greater heritability. This study details how high-throughput digital phenotyping can be applied to colour-based traits within field trials of a wheat breeding programme. It discusses the logistics of implementing such systems with minimal disruption to the programme, provides a detailed methodology for the basic image analysis methods utilized, and has potential for application to other field-crop breeding or research programmes

The genetic analysis and manipulation of economically important traits in bread wheat.

The aims of this thesis were to firstly gain an improved understanding of the genetic basis to economically important complex traits in bread wheat, and secondly, to investigate marker assisted selection (MAS) methodologies that may lead to improved rates of genetic gain. An elite Australian breeder's line, 'Stylet', and its parents 'Trident' and 'Molineux' were used as the basis of this study. A doubled-haploid (DH) population previously produced from a cross between 'Trident' and 'Molineux' (TIM DH) was used to dissect the genetic basis to end-use quality and agronomic performance. The study of end-use quality confirmed the widely published relationship between the glutenin loci and dough rheology. However this study also identified a quantitative trait locus (QTL) on chromosome 2A that was shown to be associated with dough resistance and baking quality, and another QTL on 3A that was associated with baking quality. QTL were identified in the T/M DH population that were involved in the control of time to ear-emergence through their effects on vernalisation sensitivity, photoperiod sensitivity and earliness per se. In addition to the well characterised Vrn-A1 and Ppd-B1 genes, six other QTL were identified. Three of these, QPpd.agt-1A, QPpd.agt-7A and QPpd.agt-7B are putative new loci involved in the control of photoperiod sensitivity in wheat. QPpd.agt-1A appears homoeologous to the photoperiod response gene Ppd-H2 in barley. QPpd.agt-7A and QPpd.agt-7B are located in homoeologous regions, and may represent a new phenology gene series in wheat. The T/M DH population was also used to dissect the genetic basis to grain yield and grain yield components, and to examine the influence of QTL-by environmental covariable interaction on genotype-by-environment interaction. The association of plant height genes, rust resistance genes and phenology genes with grain yield were determined. Overall, semi-dwarf rust resistant DH lines, carrying alleles conferring a short time to ear-emergence, showed the highest and most stable grain yield. Nine genetic associations with grain yield, without effects on plant height, time to ear-emergence and rust resistance, were identified. Two QTL, QGyld.agt-1B and QGyld.agt-4D were shown to have large and frequent associations with grain yield. QGyld.agt-1B showed only low levels of interaction with environmental covariables and therefore constitutes a prime candidate for MAS for grain yield. The second part of this study investigated the potential role of MAS through a practical breeding strategy and by computer simulation. An 'Anneullo/2*Stylet' cross aimed at producing a rust resistant 'Stylet' derivative with improved end-use quality was used as the model for this analysis. MAS was shown to be highly effective at improving the rate of genetic gain for rust resistance and end-use quality. This was most evident when undertaken on the BC₁F₁ population, although MAS also improved the efficiency of the breeding programme when performed on fixed lines. Practical implementation of the MAS breeding strategy validated the results from the simulation study and produced elite lines approaching the grain yield level of 'Stylet', with resistance to leaf, stem and stripe rust, and with improved end-use quality. While the results from this study highlight the complex nature of the major economically important traits being manipulated by wheat breeders, this study also concluded that improvements in rate of genetic gain are possible through the application of MAS.Thesis (Ph.D.) -- University of Adelaide, School of Agriculture, Food and Wine, 200

Plant Variety Selection Using Interaction Classes Derived From Factor Analytic Linear Mixed Models: Models With Independent Variety Effects

A major challenge in the analysis of plant breeding multi-environment datasets is the provision of meaningful and concise information for variety selection in the presence of variety by environment interaction (VEI). This is addressed in the current paper by fitting a factor analytic linear mixed model (FALMM) then using the fundamental factor analytic parameters to define groups of environments in the dataset within which there is minimal crossover VEI, but between which there may be substantial crossover VEI. These groups are consequently called interaction classes (iClasses). Given that the environments within an iClass exhibit minimal crossover VEI, it is then valid to obtain predictions of overall variety performance (across environments) for each iClass. These predictions can then be used not only to select the best varieties within each iClass but also to match varieties in terms of their patterns of VEI across iClasses. The latter is aided with the use of a new graphical tool called an iClass Interaction Plot. The ideas are introduced in this paper within the framework of FALMMs in which the genetic effects for different varieties are assumed independent. The application to FALMMs which include information on genetic relatedness is the subject of a subsequent paper

Genetic and economic analysis of a targeted marker-assisted wheat breeding strategy

The advent of molecular markers as a tool to aid selection has provided plant breeders with the opportunity to rapidly deliver superior genetic solutions to problems in agricultural production systems. However, a major constraint to the implementation of marker-assisted selection (MAS) in pragmatic breeding programs in the past has been the perceived high relative cost of MAS compared to conventional phenotypic selection. In this paper, computer simulation was used to design a genetically effective and economically efficient marker-assisted breeding strategy aimed at a specific outcome. Under investigation was a strategy involving the integration of both restricted backcrossing and doubled haploid (DH) technology. The point at which molecular markers are applied in a selection strategy can be critical to the effectiveness and cost efficiency of that strategy. The application of molecular markers was considered at three phases in the strategy: allele enrichment in the BC1F1 population, gene selection at the haploid stage and the selection for recurrent parent background of DHs prior to field testing. Overall, incorporating MAS at all three stages was the most effective, in terms of delivering a high frequency of desired outcomes and at combining the selected favourable rust resistance, end use quality and grain yield alleles. However, when costs were included in the model the combination of MAS at the BC1F1 and haploid stage was identified as the optimal strategy. A detailed economic analysis showed that incorporation of marker selection at these two stages not only increased genetic gain over the phenotypic alternative but actually reduced the over all cost by 40%

Increased genomic prediction accuracy in wheat breeding using a large Australian panel

Key message: Genomic prediction accuracy within a large panel was found to be substantially higher than that previously observed in smaller populations, and also higher than QTL-based prediction.Abstract: In recent years, genomic selection for wheat breeding has been widely studied, but this has typically been restricted to population sizes under 1000 individuals. To assess its efficacy in germplasm representative of commercial breeding programmes, we used a panel of 10,375 Australian wheat breeding lines to investigate the accuracy of genomic prediction for grain yield, physical grain quality and other physiological traits. To achieve this, the complete panel was phenotyped in a dedicated field trial and genotyped using a custom Axiom TM Affymetrix SNP array. A high-quality consensus map was also constructed, allowing the linkage disequilibrium present in the germplasm to be investigated. Using the complete SNP array, genomic prediction accuracies were found to be substantially higher than those previously observed in smaller populations and also more accurate compared to prediction approaches using a finite number of selected quantitative trait loci. Multi-trait genetic correlations were also assessed at an additive and residual genetic level, identifying a negative genetic correlation between grain yield and protein as well as a positive genetic correlation between grain size and test weight

Increased genomic prediction accuracy in wheat breeding using a large Australian panel

Key message: Genomic prediction accuracy within a large panel was found to be substantially higher than that previously observed in smaller populations, and also higher than QTL-based prediction.Abstract: In recent years, genomic selection for wheat breeding has been widely studied, but this has typically been restricted to population sizes under 1000 individuals. To assess its efficacy in germplasm representative of commercial breeding programmes, we used a panel of 10,375 Australian wheat breeding lines to investigate the accuracy of genomic prediction for grain yield, physical grain quality and other physiological traits. To achieve this, the complete panel was phenotyped in a dedicated field trial and genotyped using a custom Axiom TM Affymetrix SNP array. A high-quality consensus map was also constructed, allowing the linkage disequilibrium present in the germplasm to be investigated. Using the complete SNP array, genomic prediction accuracies were found to be substantially higher than those previously observed in smaller populations and also more accurate compared to prediction approaches using a finite number of selected quantitative trait loci. Multi-trait genetic correlations were also assessed at an additive and residual genetic level, identifying a negative genetic correlation between grain yield and protein as well as a positive genetic correlation between grain size and test weight

The Genetic Control of Grain Protein Content under Variable Nitrogen Supply in an Australian Wheat Mapping Population

<div><p>Genetic variation has been observed in both protein concentration in wheat grain and total protein content (protein yield). Here we describe the genetic analysis of variation for grain protein in response to nitrogen (N) supply and locate significant genomic regions controlling grain protein components in a spring wheat population. In total, six N use efficiency (NUE) field trials were carried out for the target traits in a sub-population of doubled haploid lines derived from a cross between two Australian varieties, RAC875 and Kukri, in Southern and Western Australia from 2011 to 2013. Twenty-four putative Quantitative Trait Loci (QTL) for protein-related traits were identified at high and low N supply and ten QTL were identified for the response to N for the traits studied. These loci accounted for a significant proportion of the overall effect of N supply. Several of the regions were co-localised with grain yield QTL and are promising targets for further investigation and selection in breeding programs.</p></div

The location, climate and basic soil characteristics, growing conditions and average grain yield (GY, kg ha^-1) of five southern Australian trial sites used for nitrogen use efficiency field trials in this study.

<p>The location, climate and basic soil characteristics, growing conditions and average grain yield (GY, kg ha^-1) of five southern Australian trial sites used for nitrogen use efficiency field trials in this study.</p