Extensions of genomic prediction methods and approaches for plant breeding

Marker assisted selection (MAS) was a first attempt to exploit molecular marker information for selection purposes in plant breeding. The MAS approach rested on the identification of quantitative trait loci (QTL). Because of inherent shortcomings of this approach, MAS failed as a tool for improving polygenic traits, in most instances. By shifting focus from QTL identification to prediction of genetic values, a novel approach called 'genomic selection', originally suggested for breeding of dairy cattle, presents a solution to the shortcomings of MAS. In genomic selection, a training population of phenotyped and genotyped individuals is used for building the prediction model. This model uses the whole marker information simultaneously, without a preceding QTL identification step. Genetic values of selection candidates, which are only genotyped, are then predicted based on that model. Finally, the candidates are selected according their predicted genetic values. Because of its success, genomic selection completely revolutionized dairy cattle breeding. It is now on the verge of revolutionizing plant breeding, too. However, several features set apart plant breeding programs from dairy cattle breeding. Thus, the methodology has to be extended to cover typical scenarios in plant breeding. Providing such extensions to important aspects of plant breeding are the main objectives of this thesis. Single-cross hybrids are the predominant type of cultivar in maize and many other crops. Prediction of hybrid performance is of tremendous importance for identification of superior hybrids. Using genomic prediction approaches for this purpose is therefore of great interest to breeders. The conventional genomic prediction models estimate a single additive effect per marker. This was not appropriate for prediction of hybrid performance because of two reasons. (1) The parental inbred lines of single-cross hybrids are usually taken from genetically very distant germplasm groups. For example, in hybrid maize breeding in Central Europe, these are the Dent and Flint heterotic groups, separated for more than 500 years. Because of the strong divergence between the heterotic groups, it seemed necessary to estimate heterotic group specific marker effects. (2) Dominance effects are an important component of hybrid performance. They had to be included into the prediction models to capture the genetic variance between hybrids maximally. The use of different heterotic groups in hybrid breeding requires parallel breeding programs for inbred line development in each heterotic group. Increasing the training population size with lines from the opposite heterotic group was not attempted previously. Thus, a further objective of this thesis was to investigate whether an increase in the accuracy of genomic prediction can be achieved by using combined training sets. Important traits in plant breeding are characterized by binomially distributed phenotypes. Examples are germination rate, fertility rates, haploid induction rate and spontaneous chromosome doubling rate. No genomic prediction methods for such traits were available. Therefore, another objective was to provide methodological extensions for such traits. We found that incorporation of dominance effects for genomic prediction of maize hybrid performance led to considerable gains in prediction accuracy when the variance attributable to dominance effects was substantial compared to additive genetic variance. Estimation of marker effects specific to the Dent and Flint heterotic group was of less importance, at least not under the high marker densities available today. The main reason for this was the surprisingly high linkage phase consistency between Dent and Flint heterotic groups. Furthermore, combining individuals from different heterotic groups (Flint and Dent) into a single training population can result in considerable increases in prediction accuracy. Our extensions of the prediction methods to binomially distributed data yielded considerably higher prediction accuracies than approximate Gaussian methods. In conclusion, the developed extensions of prediction methods (to hybrid prediction and binomially distributed data) and approaches (training populations combining heterotic groups) can lead to considerable, cost free gains in prediction accuracy. They are therefore valuable tools for exploiting the full potential of genomic selection in plant breeding.Die markergestütze Selektion (MGS) war ein Versuch molekulare Markern für Selektionszwecke in der Pflanzenzüchtung nutzbar zu machen. Der MGS Ansatz basierte auf der Identifikation von "quantitative trait loci'' (QTL, zu deutsch: Loci mit Effekt auf ein quantitatives Merkmal). Auf Grund inhärenter Defizite schlug der Versuch, MGS für die Verbesserung poligener Merkmale zu verwenden, fehl. Mit einem neuen Ansatz, genomische Selektion genannt und für die Milchrinderzüchtung entwickelt, gelang es, die Defizite der MGS zu überwinden, indem der Schwerpunkt weg von der Identifikation von QTL und hin zur Vorhersage von genetischen Werten gelegt wurde. Für die genomische Selektion wird mit Hilfe einer Kalibrierungspopulation, bestehend aus phenotypisierten und genotypisierten Individuen, ein Vorhersagemodell erstellt. Für dieses Modell wird die Information aller molekularer Marker simultan verwendet. Mit Hilfe des Vorhersagemodells werden anschließend die genetischen Werte der Selektionskandidaten, die nur genotypisiert wurden, vorhergesagt. Aufgrund ihres Erfolges revolutionierte die genomische Selektion bereits die Milchrinderzüchtung. Pflanzenzüchtung und Milchrinder-züchtung unterscheiden sich aber in grundlegenden Aspekten. Auf Grund dessen war es notwendig, die Methodik zu erweitern, um die genomische Selektion für die in der Pflanzenzüchtung typischen Szenarien einsetzen zu können. Einfachkreuzungen sind der dominierende Sortentyp in Mais und vielen anderen Kulturen. Um überlegene Hybriden zu identifizieren, ist die Vorhersage der Hybridleistung von zentraler Bedeutung. Der Einsatz von genomischen Vorhersageverfahren ist daher von großem Interesse für die Pflanzenzüchtung. Die herkömlichen genomischen Vorhersagemodelle schätzen nur einen einzigen, additive Effekt pro Marker. Aus zwei Gründen war dies nicht adäquat für die Vorhersage der Hybridleistung. (1) Die Elternlinien einer Hybride entstammen üblicherweise genetisch sehr verschiedenen Genpools, auch heterotische Gruppen genannt. In der Maishybridzüchtung in Mitteleuropa, sind dies zum Beispiel der Dent- und Flintpool, die seit mindestens 500 Jahren getrennt sind. Wegen dieser ausgeprägten Divergenz schien es notwendig, spezifische Markereffekte für jede heterotische Gruppe zu schätzen. (2) Dominanzeffekte sind eine wesentliche Komponente der Hybridleistung. Sie mussten daher in die Vorhersagemodelle aufgenommen werden, um die genetische Varianz zwischen den Hybriden so vollständig wie möglich zu erfassen. Die Verwendung verschiedener heterotischer Gruppen in der Hybridzüchtung erfordert es, für die Linienentwicklung innerhalb der heterotischer Gruppen, parallele Zuchtprogramme zu unterhalten. Es wurde allerdings noch nicht versucht, die Größe der Kalibrierungspopulation mit Linien der jeweils anderen heterotischen Gruppe zu erhöhen. Ein weiteres Ziel dieser Dissertation war es deshalb, zu untersuchen, ob die Vereinigung verschiedener heterotischer Gruppen in einer Kalibrierungspopulation zu einer Erhöhung der Vorhersagegenauigkeit führen kann. Einige für die Pflanzenzüchtung wichtige Merkmale sind dadurch gekennzeichnet, dass die phenotypischen Daten einer Binomialverteilung folgen. Beispiele dafür sind Keim-, Fruchtbarkeits- und Haploideninduktionsraten. Da für diese Art von Merkmal bisher keine Vorhersagemethodik zur Verfügung stand, sollte diese in der vorliegenden Arbeit entwickelt werden. Unsere Ergebnisse zeigten, dass die Schätzung von Dominanzeffekten die Genauigkeit der vorhergesagten Hybridleistung deutlich erhöhen konnte, wenn die Dominanzvarianz einen wesentlichen Anteil an der gesamten genetischen Varianz darstellt. Bei hohen Markerdichten machte es kaum einen Unterschied, ob für heterotische Gruppen spezifische Markereffekte geschätzt wurden. Der Hauptgrund dafür war die überraschend hohe Übereinstimmung in den Kopplungsphasen der heterotischen Gruppen Dent und Flint. Des weiteren zeigten wir, dass die Vereinigung von Linien aus Dent und Flint in einer einzigen Kalibrierungspopulation zu einer beträchtlichen Steigerung der Vorhersagegenauigkeit führen kann. Unsere Erweiterungen der Vorhersagemethodik auf binomialverteilte Daten erzielten im Vergleich zu approximativen Methoden eine deutlich höhere Vorhersagegenauigkeit. Insgesamt zeigen die erzielten Ergebnisse, dass die in dieser Dissertation entwickelten Erweiterungen der Vorhersagemethoden (für Vorhersage der Hybridleistung und für binomialverteilte Daten) und -ansätze (Vereinigung von heterotischen Gruppen in einer Kalibrierungspopulation), zu einer beträchtlichen, kostenfreien Erhöhung der Vorhersagegenauigkeit in der genomischen Selektion im pflanzenzüchterischen Kontext führen können. Sie stellen daher ein wertvolles Mittel dar, um das Potential der genomischen Selektion in der Pflanzenzüchtung voll auszuschöpfen

Development of Cognitive Vulnerability for Depression in Youth: Sex, Emotional Maltreatment, and Depression Predict Negative Cognitive Style

Hopelessness theory is a prominent cognitive theory of depression that has been shown to predict depression in youth. However, research has yet to elucidate normative mean-level development of the cognitive risk factor in hopelessness theory from childhood through adolescence. The current study utilized a multi-wave design and hierarchical linear modeling (HLM) analyses to examine mean-level negative cognitive style growth and stability in late childhood, early adolescence, and mid-late adolescence. Participant sex, emotional maltreatment, and major depression were also tested as predictors of negative cognitive style. For three years, youth (N = 681, ages 7-18 at baseline) were assessed every 1.5 years with measures of negative cognitive style and emotional maltreatment and every six months with semi-structured diagnostic interviews for major depressive episodes. Results showed decreasing trajectories of negative cognitive style in late childhood and mid-late adolescence and a marginally increasing trajectory in early adolescence. Sex differences emerged in the early adolescent cohort with girls increasing in negative cognitive style over time while boys decreased. Emotional maltreatment was associated with higher negative cognitive style in all cohorts. In the mid-late adolescent cohort, major depressive episodes over the course of the study were associated with higher negative cognitive style, and baseline history of major depression predicted an increasing trajectory of negative cognitive style over time. These findings give insight into the development of this important risk factor for depression and how sex differences in depression prevalence may emerge, as well as have implications for identifying youth who may be targets for depression prevention interventions to interrupt first onsets of depressive episodes and depressive recurrences

Effectiveness of Genomic Prediction of Maize Hybrid Performance in Different Breeding Populations and Environments

Genomic prediction is expected to considerably increase genetic gains by increasing selection intensity and accelerating the breeding cycle. In this study, marker effects estimated in 255 diverse maize (Zea mays L.) hybrids were used to predict grain yield, anthesis date, and anthesis-silking interval within the diversity panel and testcross progenies of 30 F(2)-derived lines from each of five populations. Although up to 25% of the genetic variance could be explained by cross validation within the diversity panel, the prediction of testcross performance of F(2)-derived lines using marker effects estimated in the diversity panel was on average zero. Hybrids in the diversity panel could be grouped into eight breeding populations differing in mean performance. When performance was predicted separately for each breeding population on the basis of marker effects estimated in the other populations, predictive ability was low (i.e., 0.12 for grain yield). These results suggest that prediction resulted mostly from differences in mean performance of the breeding populations and less from the relationship between the training and validation sets or linkage disequilibrium with causal variants underlying the predicted traits. Potential uses for genomic prediction in maize hybrid breeding are discussed emphasizing the need of (1) a clear definition of the breeding scenario in which genomic prediction should be applied (i.e., prediction among or within populations), (2) a detailed analysis of the population structure before performing cross validation, and (3) larger training sets with strong genetic relationship to the validation set

Genetic relationships between spring emergence, canopy phenology and biomass yield increase the accuracy of genomic prediction in Miscanthus

Miscanthus has potential as a bioenergy crop but the rapid development of high-yielding varieties is challenging. Previous studies have suggested that phenology and canopy height are important determinants of biomass yield. Furthermore, while genome-wide prediction was effective for a broad range of traits, the predictive ability for yield was very low. We therefore developed models clarifying the genetic associations between spring emergence, consequent canopy phenology and dry biomass yield. The timing of emergence was a moderately strong predictor of early-season elongation growth (genetic correlation >0.5), but less so for growth later in the season and for the final yield (genetic correlation <0.1). In contrast, early-season canopy height was consistently more informative than emergence for predicting biomass yield across datasets for two species in Miscanthus and two growing seasons. We used the associations uncovered through these models to develop selection indices that are expected to increase the response to selection for yield by as much as 21% and improve the performance of genome-wide prediction by an order of magnitude. This multivariate approach could have an immediate impact in operational breeding programmes, as well as enable the integration of crop growth models and genome-wide predictionpublishersversionPeer reviewe

Plant phenomics, from sensors to knowledge

Major improvements in crop yield are needed to keep pace with population growth and climate change. While plant breeding efforts have greatly benefited from advances in genomics, profiling the crop phenome (i.e., the structure and function of plants) associated with allelic variants and environments remains a major technical bottleneck. Here, we review the conceptual and technical challenges facing plant phenomics. We first discuss how, given plants’ high levels of morphological plasticity, crop phenomics presents distinct challenges compared with studies in animals. Next, we present strategies for multi-scale phenomics, and describe how major improvements in imaging, sensor technologies and data analysis are now making high-throughput root, shoot, whole-plant and canopy phenomic studies possible. We then suggest that research in this area is entering a new stage of development, in which phenomic pipelines can help researchers transform large numbers of images and sensor data into knowledge, necessitating novel methods of data handling and modelling. Collectively, these innovations are helping accelerate the selection of the next generation of crops more sustainable and resilient to climate change, and whose benefits promise to scale from physiology to breeding and to deliver real world impact for ongoing global food security efforts

Quantitative genetics theory for genomic selection and efficiency of genotypic value prediction in open-pollinated populations

ABSTRACT: Quantitative genetics theory for genomic selection has mainly focused on additive effects. This study presents quantitative genetics theory applied to genomic selection aiming to prove that prediction of genotypic value based on thousands of single nucleotide polymorphisms (SNPs) depends on linkage disequilibrium (LD) between markers and QTLs, assuming dominance and epistasis. Based on simulated data, we provided information on dominance and genotypic value prediction accuracy, assuming mass selection in an open-pollinated population, all quantitative trait loci (QTLs) of lower effect, and reduced sample size. We show that the predictor of dominance value is proportional to the square of the LD value and to the dominance deviation for each QTL that is in LD with each marker. The weighted (by the SNP frequencies) dominance value predictor has greater accuracy than the unweighted predictor. The linear × linear, linear × quadratic, quadratic × linear, and quadratic × quadratic SNP effects are proportional to the corresponding linear combinations of epistatic effects for QTLs and the LD values. LD between two markers with a common QTL causes a bias in the prediction of epistatic values. Compared to phenotypic selection, the efficiency of genomic selection for genotypic value prediction increases as trait heritability decreases. The degree of dominance did not affect the genotypic value prediction accuracy and the approach to maximum accuracy is asymptotic with increases in SNP density. The decrease in the sample size from 500 to 200 did not markedly reduce the genotypic value prediction accuracy