Strategies for selecting high-yielding and broadly adapted maize hybrids for the target environment in Eastern and Southern Africa

Maize is a major food crop in Africa and primarily grown by small-holder farmers under rain-fed conditions with low fertilizer input. Projections of decreasing precipitation and increasing fertilizer prices accentuate the need to provide farmers with maize varieties tolerant to random abiotic stress, especially drought and N deficiency. Genetic improvement for the target environment in Eastern and Southern Africa can be achieved by: (i) direct selection of grain yield in random abiotic stress environments, (ii) indirect selection for a secondary trait or grain yield in optimal, low-N and/or managed stress environments, or (iii) index selection using information from all test environments. At present, the maize hybrid testing programs of the International Maize and Wheat Improvement Center (CIMMYT) select primarily for grain yield under managed stress and optimal environments and subdivide the target environment according to geographic and climatic differences. It is not known to what extend the current strategy contributes to selection gains. The same holds true for genomic prediction, a strategy that is not yet implemented into the CIMMYT maize breeding program but that may accelerate breeding progress and reduce cycle length by predicting genotype performance based on molecular markers. Regarding the different strategies mentioned for selecting high-yielding and broadly adapted maize hybrids, the breeder needs to decide which of them are most promising to increase genetic gains. Consequently, the objectives of my thesis were to (1) evaluate the potential of leaf and canopy spectral reflectance as novel secondary traits to predict grain yield across different environments, (2) estimate to what extent indirect selection in managed drought and low-N stress environments is predictive of grain yield in random abiotic stress environments, (3) investigate whether subdividing the target environment into climate, altitude, geographic, yield level or country subregions is likely to increase rates of genetic gain, and (4) evaluate the prospects of genomic prediction in the presence of population structure. The measurement of spectral reflectance (495 ? 1853 nm) of both leaves and canopy at anthesis and milk grain stage explained less than 40% of the genetic variation in grain yield after validation. Consequently, selection based on predicted grain yield is only suitable for pre-screening, while final yield evaluation will still be necessary. Nevertheless, the prospect of developing inexpensive and easy to handle devices that can provide, at anthesis, precise estimates of final grain yield warrants further research. Based on a retrospective analysis across 9 years, more than 600 trials and 448 maize hybrids, it was shown that maize hybrids were broadly adapted to climate, altitude, geographic and country subregions in Eastern and Southern Africa. Consequently, I recommend that the maize breeding programs of CIMMYT in the region should be consolidated. Within the consolidated breeding programs, genotypes should be selected for performance in low- and high yielding environments as the genotype-by-yield level interaction variance was high relative to the genetic variance and genetic correlations between low- and high-yielding environments were moderate. Genetic gains were maximized by index selection, considering the yield-level effect as fixed and appropriately weighting information from all trials. To allow better allocation of resources, locations with high occurrence of random abiotic stress need to be identified. Heritability in trials conducted at these locations may be increased by the use of row- and column designs and/or spatial adjustment. Furthermore, resources invested into managed drought trials should be maintained during early breeding stages but shifted to the conduct of low-N trials at later breeding stages. Investments in a larger number of low-N trials may increase selection gain, because performance under low-N and random abiotic stress was highly correlated and genotypes can be easily selected under different levels of soil N. Prospects are promising to accelerate breeding cycles by the use of genomic prediction. Based on two large data sets on the performance of eight breeding populations, it was shown that prediction accuracy resulted primarily from differences in mean performance of these populations. Genomic prediction may be implemented into the CIMMYT maize breeding program to predict the performance of lines from a diversity panel, segregating lines from the same or related crosses, and progenies from closed populations within a recurrent selection program. The breeding scenarios in which genomic prediction is most promising still need to be defined. Generally, the construction of larger training sets with strong relationship to the validation set and a detailed analysis of the population structure within the training and validation sets are required. In conclusion, combining index and genomic selection is the most promising strategy for providing high-yielding and broadly adapted maize genotypes for the target environments in Eastern and Southern Africa.Mais ist eine der wichtigen Nahrungspflanzen in Afrika und wird vor allem von Kleinbauern ohne Bewässerung und mit limitierter Stickstoffdünung angebaut. Die Prognosen von abnehmenden Niederschlägen und steigenden Düngemittelpreisen erfordern die Züchtung von Maissorten, die eine hohe Stresstoleranz bei trockenen und stickstoffarmen Umwelten besitzen. Eine züchterische Verbesserung der Stresstoleranz kann für die Zielregionen in Ost- und Süd-Afrika durch folgende Strategien erreicht werden: (i) direkte Selektion von Kornertrag in Umwelten mit abiotischem Stress, (ii) indirekte Selektion für sekundäre Merkmale oder Kornertrag in optimalen oder kontrollierten Stressumwelten oder (iii) Index-Selektion unter Verwendung der Informationen aller Testumwelten. Derzeit selektiert das Maiszüchtungsprogramm des Internationalen Mais- und Weizenforschungszentrums (CIMMYT) in erster Linie auf Kornertrag in kontrollierten Stress- sowie optimalen Umwelten und unterteilt die Zielregion nach geographischen und klimatischen Unterschieden. Es ist nicht bekannt, inwieweit die aktuelle Strategie erfolgreich ist. Das gleiche gilt für die genomische Vorhersage anhand von genetischen Markern, einer Strategie, die im CIMMYT Maiszüchtungsprogramm künftig angewendet werden soll und den Züchtungsfortschritt erheblich beschleunigen könnte. Hinsichtlich der erwähnten Strategien für die Selektion von hoch-ertragreichen und universal angepassten Maishybriden muss ein Züchter entscheiden, welches die vielversprechendsten sind, um den Selektionsgewinn zu erhöhen. Folglich waren die Ziele meiner Arbeit zu bewerten, inwieweit (1) sich die Messung der Lichtreflektion von Blättern und des Blätterdachs als neues sekundäres Merkmal für die Vorhersage des Kornertrags in verschiedenen Umwelten eignet, (2) indirekte Selektion in kontrollierten Stressumwelten prädiktiv ist für den Kornertrag in abiotischen Stressumwelten, (3) die Unterteilung der Zielregion anhand von Unterschieden in Klima, Höhenlage, geografischer Lage, Ertragsniveau oder Landesgrenzen den Selektionserfolg erhöht, und (4) genomische Vorhersage bei Vorliegen von Populationsstruktur in das Züchtungsprogram integriert werden kann. Die Messung der Lichtreflektion (495 - 1853 nm) von Blättern und Blätterdach während und nach der Blüte erklärte weniger als 40% der genetischen Variation des Kornertrags nach der Validierung. Folglich ist die Selektion anhand des vorhergesagten Kornertrags nur angemessen für eine Vorbewertung und eine Erfassung des tatsächlichen Kornertrags nachwievor notwendig. Die Konstruktion von billigen und leicht zu handhabenden Geräten, die zur Blüte eine genaue Schätzung des Kornertrags ermöglichen, rechtfertigt jedoch weitere Forschungsarbeiten. Basierend auf einer retrospektiven Analyse über 9 Jahre, mehr als 600 Versuchen und 448 Maishybriden wurde gezeigt, dass Maishybriden adaptiert sind an verschiedene Klimata, Höhenlagen und geografische Regionen. Daher empfehle ich, dass die Zuchtprogramme von CIMMYT in Ost-und Südafrika zusammengelegt werden. Innerhalb der zusammengelegten Zuchtprogramme sollten die Genotypen für niedrig- und hoch-ertragreiche Umwelten selektiert werden, da die Interaktionsvarianz Genotyp-Ertragsniveau hoch war im Vergleich zu der genetischen Varianz und die genetischen Korrelationen zwischen niedrig- und hoch-ertragreichen Umwelten moderat waren. Der Selektionserfolg wurde durch Indexselektion maximiert, in dem das Ertragsniveau als fixer Effekt betrachtet und die Information aus allen Versuchen optimal gewichtet wurde. Um eine bessere Ressourcenallokation zu ermöglichen, sollten Standorte mit häufigem Auftreten von abiotischem Stress identifiziert werden. Die Wiederholbarkeit von Versuchen an diesen Standorten könnte durch die Verwendung von Zeilen- und Spalten-Designs und/oder räumlicher Anpassung erhöht werden. Darüber hinaus sollten die Ressourcen, die in frühen Zuchtstadien für Versuche in kontrollierten Stressumwelten investiert wurden, beibehalten werden, wohingegen sie in späteren Zuchtphasen für die Durchführung von Versuchen mit reduzierter Stickstoffdüngung verwendet werden sollten. Die Investitionen in eine größere Anzahl dieser Versuche verspricht den Zuchtfortschritt zu erhöhen, weil der Kornertrag in stickstoffarmen und abiotischen Stressumwelten hoch korreliert war und Genotypen zuverlässig unter verschiedenen Stickstoffniveaus selektiert werden können. Die Aussichten sind vielversprechend, den Züchtungsfortschritt mit genomischer Vorhersage zu beschleunigen. Basierend auf zwei großen Datensätzen über die Leistung von acht Populationen wurde gezeigt, dass die hohe Vorhersagegenauigkeit in erster Linie auf Unterschieden in der mittleren Leistung dieser Populationen basiert. Genomische Vorhersage kann in das CIMMYT Maiszüchtungsprogramm integriert werden, um die Leistung von Linien aus einem diversem Panel, spaltenden Linien aus denselben oder verwandten Kreuzungen und Populationsnachkommen in einem rekurrentem Selektionsprogram vorherzusagen. Die Szenarien, in denen genomische Vorhersage am vielversprechendsten ist, müssen noch genauer erforscht werden. Generell sind größere Trainingssets mit naher Verwandtschaft zum Validationsset und eine detaillierte Analyse der Populationsstruktur in den Trainings- und Validierungssets erforderlich. Die Kombination von Index- und genomischer Selektion ist die vielversprechendste Strategie, um hoch-ertragreiche und universal angepasste Maishybriden für die Zielregionen in Ost-und Südafrika bereitzustellen

Advanced therapy for pulmonary arterial hypertension due to congenital heart disease: a clinical perspective in a new therapeutic era

Cardiolog

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

The value of expanding the training population to improve genomic selection models in tetraploid potato

<p>Genomic selection (GS) is becoming increasingly applicable to crops as the genotyping costs continue to decrease, which makes it an attractive alternative to traditional selective breeding based on observed phenotypes. With genome-wide molecular markers, selection based on predictions from genotypes can be made in the absence of direct phenotyping. The reliability of predictions depends strongly on the number of individuals used for training the predictive algorithms, particularly in a highly genetically diverse organism such as potatoes; however, the relationship between the individuals also has an enormous impact on prediction accuracy. Here we have studied genomic prediction in three different panels of potato cultivars, varying in size, design, and phenotypic profile. We have developed genomic prediction models for two important agronomic traits of potato, dry matter content and chipping quality. We used genotyping-by-sequencing to genotype 1,146 individuals and generated genomic prediction models from 167,637 markers to calculate genomic estimated breeding values with genomic best linear unbiased prediction. Cross-validated prediction correlations of 0.75–0.83 and 0.39–0.79 were obtained for dry matter content and chipping quality, respectively, when combining the three populations. These prediction accuracies were similar to those obtained when predicting performance within each panel. In contrast, but not unexpectedly, predictions across populations were generally lower, 0.37–0.71 and 0.28–0.48 for dry matter content and chipping quality, respectively. These predictions are not limited by the number of markers included, since similar prediction accuracies could be obtained when using merely 7,800 markers (<5%). Our results suggest that predictions across breeding populations in tetraploid potato are presently unreliable, but that individual prediction models within populations can be combined in an additive fashion to obtain high quality prediction models relevant for several breeding populations.</p

The family Carditidae (Bivalvia) in the early Danian of Patagonia (Argentina)

The first systematic analysis of the Danian carditids of Patagonia is presented, which includes four genera—one new genus and the first records of three other genera in South America. They consist of Claibornicardia paleopatagonica (Ihering, 1903), a widely distributed species occuring in the Jagüel, Roca and Salamanca formations (Neuquén, Río Negro and Chubut Provinces); Rotundicardia Heaslip, 1968, represented by the new species R. mariobrosorum n. sp., which is restricted to the Roca Formation (Río Negro Province); Cardites feruglioi (Petersen, 1846) (Roca and Lefipán formations, Río Negro and Chubut Provinces); and by Kalelia new genus, which includes K. burmeisteri (Böhm, 1903) from the Salamanca and Roca formations (Río Negro and Chubut Provinces), which is related to the Paris Basin species K. multicostata (Lamarck, 1806) n. comb. and K. pectuncularis (Lamarck, 1806) n. comb. ‘Venericardia’ iheringi (Böhm, 1903), a species known only from internal molds, is described and regarded as a carditid with uncertain affinities. The presence of Claibornicardia, Rotundicardia, and Cardites in Patagonia constitutes the most ancient record of these genera and confirms biogeographical connections previously established between the Danian Argentinian and North American/European fossil faunas.Fil: Perez, Damián. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Museo Argentino de Ciencias Naturales ; ArgentinaFil: del Río, Claudia Julia. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Museo Argentino de Ciencias Naturales ; Argentin

Genomic Prediction in Maize Breeding Populations with Genotyping-by-Sequencing

Genotyping-by-sequencing (GBS) technologies have proven capacity for delivering large numbers of marker genotypes with potentially less ascertainment bias than standard single nucleotide polymorphism (SNP) arrays. Therefore, GBS has become an attractive alternative technology for genomic selection. However, the use of GBS data poses important challenges, and the accuracy of genomic prediction using GBS is currently undergoing investigation in several crops, including maize, wheat, and cassava. The main objective of this study was to evaluate various methods for incorporating GBS information and compare them with pedigree models for predicting genetic values of lines from two maize populations evaluated for different traits measured in different environments (experiments 1 and 2). Given that GBS data come with a large percentage of uncalled genotypes, we evaluated methods using nonimputed, imputed, and GBS-inferred haplotypes of different lengths (short or long). GBS and pedigree data were incorporated into statistical models using either the genomic best linear unbiased predictors (GBLUP) or the reproducing kernel Hilbert spaces (RKHS) regressions, and prediction accuracy was quantified using cross-validation methods. The following results were found: relative to pedigree or marker-only models, there were consistent gains in prediction accuracy by combining pedigree and GBS data; there was increased predictive ability when using imputed or nonimputed GBS data over inferred haplotype in experiment 1, or nonimputed GBS and information-based imputed short and long haplotypes, as compared to the other methods in experiment 2; the level of prediction accuracy achieved using GBS data in experiment 2 is comparable to those reported by previous authors who analyzed this data set using SNP arrays; and GBLUP and RKHS models with pedigree with nonimputed and imputed GBS data provided the best prediction correlations for the three traits in experiment 1, whereas for experiment 2 RKHS provided slightly better prediction than GBLUP for drought-stressed environments, and both models provided similar predictions in well-watered environments

Genomic prediction in CIMMYT maize and wheat breeding programs

Genomic selection (GS) has been implemented in animal and plant species, and is regarded as a useful tool for accelerating genetic gains. Varying levels of genomic prediction accuracy have been obtained in plants, depending on the prediction problem assessed and on several other factors, such as trait heritability, the relationship between the individuals to be predicted and those used to train the models for prediction, number of markers, sample size and genotype × environment interaction (GE). The main objective of this article is to describe the results of genomic prediction in International Maize and Wheat Improvement Center's (CIMMYT's) maize and wheat breeding programs, from the initial assessment of the predictive ability of different models using pedigree and marker information to the present, when methods for implementing GS in practical global maize and wheat breeding programs are being studied and investigated. Results show that pedigree (population structure) accounts for a sizeable proportion of the prediction accuracy when a global population is the prediction problem to be assessed. However, when the prediction uses unrelated populations to train the prediction equations, prediction accuracy becomes negligible. When genomic prediction includes modeling GE, an increase in prediction accuracy can be achieved by borrowing information from correlated environments. Several questions on how to incorporate GS into CIMMYT's maize and wheat programs remain unanswered and subject to further investigation, for example, prediction within and between related bi-parental crosses. Further research on the quantification of breeding value components for GS in plant breeding populations is required.J Crossa, P Pérez, J Hickey, J Burgueño, L Ornella, J Cerón-Rojas, X Zhang, S Dreisigacker, R Babu, Y Li, D Bonnett and K Mathew