Genome and Environment Based Prediction Models and Methods of Complex Traits Incorporating Genotype × Environment Interaction

Genomic-enabled prediction models are of paramount importance for the successful implementation of genomic selection (GS) based on breeding values. As opposed to animal breeding, plant breeding includes extensive multienvironment and multiyear field trial data. Hence, genomic-enabled prediction models should include genotype × environment (G × E) interaction, which most of the time increases the prediction performance when the response of lines are different from environment to environment. In this chapter, we describe a historical timeline since 2012 related to advances of the GS models that take into account G × E interaction. We describe theoretical and practical aspects of those GS models, including the gains in prediction performance when including G × E structures for both complex continuous and categorical scale traits. Then, we detailed and explained the main G × E genomic prediction models for complex traits measured in continuous and noncontinuous (categorical) scale. Related to G × E interaction models this review also examine the analyses of the information generated with high-throughput phenotype data (phenomic) and the joint analyses of multitrait and multienvironment field trial data that is also employed in the general assessment of multitrait G × E interaction. The inclusion of nongenomic data in increasing the accuracy and biological reliability of the G × E approach is also outlined. We show the recent advances in large-scale envirotyping (enviromics), and how the use of mechanistic computational modeling can derive the crop growth and development aspects useful for predicting phenotypes and explaining G × E

Plant Breeding Integrated with Genomic-Enabled Prediction

Peer reviewe

High performance computing for large-scale genomic prediction

In the past decades genetics was studied intensively leading to the knowledge that DNA is the molecule behind genetic inheritance and starting from the new millennium next-generation sequencing methods made it possible to sample this DNA with an ever decreasing cost. Animal and plant breeders have always made use of genetic information to predict agronomic performance of new breeds. While this genetic information previously was gathered from the pedigree of the population under study, genomic information of the DNA makes it possible to also deduce correlations between individuals that do not share any known ancestors leading to so-called genomic prediction of agronomic performance. Nowadays, the number of informative samples that can be taken from a genome ranges from one thousand to one million. Using all this information in a breeding context where agronomic performance is predicted and optimized for different environmental conditions is not a straightforward task. Moreover, the number of individuals for which this information is available keeps on growing and thus sophisticated computational methods are required for analyzing these large scale genomic data sets. This thesis introduces some concepts of high performance computing in a genomic prediction context and shows that analyzing phenotypic records of large numbers of genotyped individuals leads to a better prediction accuracy of the agronomic performance in different environments. Finally, it is even shown that the parts of the DNA that influence the agronomic performance under certain environmental conditions can be pinpointed, and this knowledge can thus be used by breeders to select individuals that thrive better in the targeted environment

A Canadian oat genomic selection study incorporating genetic and environmental information

Oat (Avena sativa L.) is an important crop in Canada that has been seeded on an average of 3.3 million acres over the past five years. It is considered a healthy cereal due to the presence of beta-glucan in the grain, which been shown to reduce the risk of heart disease, as well as being a good source of protein that is rich in globulins. Identifying new breeding strategies that can improve breeding efficiency in oat is important for future progress in this crop. To this end, genomic and environmental factors, along with their interactions, were examined to determine what contributed to variation in important oat traits. This information was then used to develop genomic selection (GS) models that can be used in oat breeding programs. In the first study, 305 elite oat breeding lines grown in the Western Cooperative Oat Registration Trial (WCORT) from 2002 to 2014 were used to investigate important factors for genomic selection model building. The influence of phenotypic data, genotyping platforms, statistical model, marker density, population structure, training population size and trait heritability were assessed. It was determined that the machine learning model Support Vector Machine and the additive linear model rr-BLUP offered the best overall prediction accuracies. Prediction accuracy increased when using the iSelect Oat 6K SNP chip, as the marker number increased, with larger training population size and with traits that were more heritable. In the second study, environmental and correlated agronomic variables, along with their inter-relationships, that contributed to variation in yield and grain β-glucan content in oat lines was investigated. A hypothesized structural equation model (SEM) that included variables related to environmental and phenotypic traits was created and tested against observed yield data. Significant paths were identified to explain yield variation (59%-76%) among the three oat varieties. A similar approach was taken for β-glucan in which significant paths were found which explained 16%-41% of the variation in β-glucan. Results from this study suggest that a longer period to heading and maturity, and a taller stature were the three phenotypic traits that most positively influence yield. Limited precipitation before maturity, high temperatures during heading and grain filling were the three environmental variables that contributed to decreased yield. Precipitation and July temperature were the two most important environmental variables that influenced β-glucan, while maturity was the most important trait affecting β-glucan, although the direction of effect for maturity varied by oat variety. In the third study, additional information was added into the previous GS models to determine if prediction could be improved. Genotype, environment and their interaction were used to conduct genomic selection for yield. Four mega-environments were identified from Ward’s hierarchical clustering using the significant environmental variables identified in the second study. It was found that using individual locations to represent environment provided more accuracy compared to using mega-environments. The reaction norm model was also tested which allowed significant environmental variables to be incorporated as a covariance matrix in the model. Including an environmental covariance matrix and interaction terms increased prediction accuracy compared to models with only genotype main effects. Multiple trait GS did not provide better prediction accuracy for most the traits. In the final study, GS was used to predict the GEBVs of two populations, a biparental derived population and a population consisting of elite breeding lines from several different breeding programs. Higher predication accuracy was found in the elite breeding line population which was likely due to the closer genetic relationship between it and the training population. Finally, random selection and genomic selection were compared in the two populations. Genomic selection out-performed random selection in the elite breeding population, but not in the bi-parental population. Again, the poor performance of GS in the bi-parental population was best explained by the unrelatedness between it and the training population. Taken together, these studies provided deeper insight into how GS could be applied in oat breeding programs

Genomic Selection, Quantitative Trait Loci and Genome-Wide Association Mapping for Spring Bread Wheat (Triticum aestivum L.) Improvement

Molecular breeding involves the use of molecular markers to identify and characterize genes that control quantitative traits. Two of the most commonly used methods to dissect complex traits in plants are linkage analysis and association mapping. These methods are used to identify markers associated with quantitative trait loci (QTL) that underlie trait variation, which are used for marker assisted selection (MAS). Marker assisted selection has been successful to improve traits controlled by moderate to large effect QTL; however, it has limited application for traits controlled by many QTL with small effects. Genomic selection (GS) is suggested to overcome the limitation of MAS and improve genetic gain of quantitative traits. GS is a type of MAS that estimates the effects of genome-wide markers to calculate genomic estimated breeding values (GEBVs) for individuals without phenotypic records. In recent years, GS is gaining momentum in crop breeding programs but there is limited empirical evidence for practical application. The objectives of this study were to: i) evaluate the performance of various statistical approaches and models to predict agronomic and end-use quality traits using empirical data in spring bread wheat, ii) determine the effects of training population (TP) size, marker density, and population structure on genomic prediction accuracy, iii) examine GS prediction accuracy when modelling genotype-by-environment interaction (G × E) using different approaches, iv) detect marker-trait associations for agronomic and end-use quality traits in spring bread wheat, v) evaluate the effects of TP composition, cross-validation technique, and genetic relationship between the TP and SC on GS accuracy, and vi) compare genomic and phenotypic prediction accuracy. Six studies were conducted to meet these objectives using two populations of 231 and 304 spring bread wheat lines that were genotyped with the wheat 90K SNP array and phenotyped for nine agronomic and end-use quality traits. The main finding across these studies is that GS can accurately predict GEBVs for wheat traits and can be used to make predictions in different environments; thus, GS should be applied in wheat breeding programs. Each study provides specific insights into some of the advantages and limitations of different GS approaches, and gives recommendations for the application of GS in future breeding programs. Specific recommendations include using the GS model BayesB (especially for large effect QTL) for genomic prediction in a single environment, across-year genomic prediction using the reaction norm model, using a large TP size for making accurate genomic predictions, and not making across-population genomic predictions except for highly related population

Optimizing Genomic-Enabled Prediction in Small-Scale Maize Hybrid Breeding Programs: A Roadmap Review

The usefulness of genomic prediction (GP) for many animal and plant breeding programs has been highlighted for many studies in the last 20 years. In maize breeding programs, mostly dedicated to delivering more highly adapted and productive hybrids, this approach has been proved successful for both large- and small-scale breeding programs worldwide. Here, we present some of the strategies developed to improve the accuracy of GP in tropical maize, focusing on its use under low budget and small-scale conditions achieved for most of the hybrid breeding programs in developing countries. We highlight the most important outcomes obtained by the University of São Paulo (USP, Brazil) and how they can improve the accuracy of prediction in tropical maize hybrids. Our roadmap starts with the efforts for germplasm characterization, moving on to the practices for mating design, and the selection of the genotypes that are used to compose the training population in field phenotyping trials. Factors including population structure and the importance of non-additive effects (dominance and epistasis) controlling the desired trait are also outlined. Finally, we explain how the source of the molecular markers, environmental, and the modeling of genotype–environment interaction can affect the accuracy of GP. Results of 7 years of research in a public maize hybrid breeding program under tropical conditions are discussed, and with the great advances that have been made, we find that what is yet to come is exciting. The use of open-source software for the quality control of molecular markers, implementing GP, and envirotyping pipelines may reduce costs in an efficient computational manner. We conclude that exploring new models/tools using high-throughput phenotyping data along with large-scale envirotyping may bring more resolution and realism when predicting genotype performances. Despite the initial costs, mostly for genotyping, the GP platforms in combination with these other data sources can be a cost-effective approach for predicting the performance of maize hybrids for a large set of growing conditions

Factors influencing the accuracy of genomic prediction in plant breeding

Genomic prediction (GP) is a novel statistical tool to estimate breeding values of selection candidates without the necessity to evaluate them phenotypically. The method calibrates a prediction model based on data of phenotyped individuals that were also genotyped with genome-wide molecular markers. The renunciation of an explicit identification of causal polymorphisms in the DNA sequence allows GP to explain significantly larger amounts of the genetic variance of complex traits than previous mapping-based approaches employed for marker-assisted selection. For these reasons, GP rapidly revolutionized dairy cattle breeding, where the method was originally developed and first implemented. By comparison, plant breeding is characterized by often intensively structured populations and more restricted resources routinely available for model calibration. This thesis addresses important issues related to these peculiarities to further promote an efficient integration of GP into plant breeding.Die genomische Leistungsvorhersage (GLV) ist ein neues statistisches Werkzeug zur Zuchtwertschätzung von Selektionskandidaten ohne die Notwendigkeit diese zuvor zu phänotypisieren. Die Methode kalibriert ein Vorhersagemodell auf Basis bereits phänotypisierter Individuen, welche zudem mit genomweiten molekularen Markern genotypisiert wurden. Der Verzicht auf die explizite Identifikation von kausalen Polymorphismen in der DNA-Sequenz ermöglicht der GLV signifikant größere Anteile der genetischen Varianz komplexer Merkmale zu erklären als frühere, kartierungsbasierte Ansätze zur markergestützten Selektion. Aus diesen Gründen revolutionierte die GLV bereits die Milchrinderzüchtung, in welcher die Methode ursprünglich entwickelt und auch erstmalig praktisch angewendet wurde. Im Vergleich hierzu zeichnet sich die Pflanzenzüchtung durch starke Populationsstruktur und stärker limitierte Ressourcen für den Zweck der Modellkalibration aus, welche in regelmäßigen Abständen zur Verfügung stehen. Die vorliegende Arbeit widmet sich wichtigen Fragen, die sich aus diesen Eigenschaften ergeben, um eine effizientere Integration der GLV in der Pflanzenzüchtung zu fördern

Cage row arrangement affects the performance of laying hens in the hot humid tropics

Although the traditional cage system of housing laying hens is gradually being faced out due to welfare reasons, cages are still common in most developing tropical countries in different arrangements. In a 12-week experiment, the effects of a three cage row arrangement on hen-day production and egg qualities of Shaver Brown hens was studied. Data were collected from 2 layer sheds housing 9,000 hens in a 3-cage row arrangement (southern row, northern row and middle row) with 3,000 hens per row. Data were analysed for a randomized complete block design where cage rows were the treatments and weeks the blocks. Results showed no significant effects of cage row arrangement on feed intake, hen-day production, per cent yolk and Haugh unit (P>0.05). Egg weight, egg mass and per cent shell were significantly reduced and feed conversion ratio increased on the middle row (P<0.05). Egg weight, egg mass, per cent shell and feed conversion ratio did not differ between the side rows (P>0.05). These results suggest that battery cage row arrangement may not affect the rate of lay but egg weight, egg mass and efficiency of feed utilisation may be adversely affected in hens housed in the middle row. These findings have both economic and welfare implications

Combining historical agricultural and climate datasets sheds new light on early 20th century barley performance

Barley (Hordeum vulgare ssp. vulgare) is cultivated globally across a wide range of environments, both in highly productive agricultural systems and in subsistence agriculture and provides valuable feedstock for the animal feed and malting industries. However, as the climate changes there is an urgent need to identify adapted barley varieties that will consistently yield highly under increased environmental stresses. Our ability to predict future local climates is only as good as the skill of the climate model, however we can look back over 100 years with much greater certainty. Historical weather datasets are an excellent resource for identifying causes of historical yield variability. In this research we combined recently digitised historical weather data from the early 20th century with published Irish spring barley trials data for two heritage varieties: Archer and Goldthorpe, following an analysis first published by Student in 1923. Using linear mixed models, we show that interannual variation in observed spring barley yields can be partially explained by recorded weather variability, in particular July maximum temperature and rainfall, and August maximum temperature. We find that while Archer largely yields more highly, Goldthorpe is more stable under wetter growing conditions, highlighting the importance of considering growing climate in variety selection. Furthermore, this study demonstrates the benefits of access to historical trials and climatic data and the importance of incorporating climate data in modern day breeding programmes to improve climate resilience of future varieties