Browsing by Subject "Genomik"

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Assessment of phenotypic, genomic and novel approaches for soybean breeding in Central Europe
(2022) Zhu, Xintian; Würschum, Tobias
Soybean is the economically most important leguminous crop worldwide and serves as a main source of plant protein for human nutrition and animal feed. Europe is dependent on plant protein imports and the EU protein self-sufficiency, which is an issue that has been on the political agenda for several decades, has recently received renewed interest. The protein imports are mainly in the form of soybean meal, and soybean therefore appears well-suited to mitigate the protein deficit in Europe. This, however, requires an improvement of soybean production as well as an expansion of soybean cultivation and thus breeding of new cultivars that combine agronomic performance with adaptation to the climatic conditions in Central Europe. The objective of this thesis was to characterize, evaluate and devise approaches that can improve the efficiency of soybean breeding. Breeding is essentially the generation of new genetic variation and the subsequent selection of superior genotypes as candidates for new cultivars. The process of selection can be supported by marker-assisted or genomic selection, which are both based on molecular markers. A first step towards the utilization of these approaches in breeding is the characterization of the genetic architecture underlying the target traits. In this study, we therefore performed QTL mapping for six target traits in a large population of 944 recombinant inbred lines from eight biparental families. The results showed that some major-effect QTL are present that could be utilized in marker-assisted selection, but in general the target traits are quantitatively inherited. For such traits controlled by numerous small-effect QTL, genomic selection has proven as a powerful tool to assist selection in breeding programs. We therefore also evaluated the genomic prediction accuracy and found this to be high and promising for the six traits of interest. In conclusion, these results illustrated the potential of genomic selection for soybean breeding programs, but a potential limitation of this approach are the costs required for genotyping with molecular markers. Phenomic selection is an alternative approach that uses near-infrared or other spectral data for prediction instead of the marker data used for its genomic counterpart. Here, we evaluated the phenomic predictive ability in soybean as well as in triticale and maize. Phenomic prediction based on near-infrared spectroscopy (NIRS) of seeds showed a comparable or even slightly higher predictive ability than genomic prediction. Collectively, our results illustrate the potential of phenomic selection for breeding of complex traits in soybean and other crops. The advantage of this approach is that NIRS data are often available anyhow and can be generated with much lower costs than the molecular marker data, also in high-throughput required to screen the large numbers of selection candidates in breeding programs. Soybean is a short-day plant originating from temperate China, and thus adaptation to the climatic conditions of Central Europe is a major breeding goal. In this study, we established a large diversity panel of 1,503 early-maturing soybeans, comprising of European breeding material and accessions from genebanks. This panel was evaluated in six environments, which revealed valuable genetic variation that can be introgressed into our breeding programs. In addition, we deciphered the genetic architecture of the adaptation traits flowering time and maturity. Taken together, the findings of this study show the potential of several phenotypic, genomic and novel approaches that can be integrated to improve the efficiency of soybean breeding and thus hold great promise to assist the expansion of soybean cultivation in Central Europe through breeding of adapted and agronomically improved cultivars.
Breeding for resistance to Fusarium ear diseases in maize and small-grain cereals using genomic tools
(2021) Gaikpa, David Sewordor; Miedaner, Thomas
The world’s human and livestock population is increasing and there is the need to increase quality food production to achieve the global sustainable development goal 3, zero hunger by year 2030 (United Nations, 2015). However, biotic stresses such as Fusarium ear infections pose serious threat to cereal crop production. Breeding for host plant resistance against toxigenic Fusarium spp. is a sustainable way to produce more and safer cereal crops such as maize and small-grain winter cereals. Many efforts have been made to improve maize and small-grain cereals for ear rot (ER) and Fusarium head blight (FHB) resistances, using conventional and genomic techniques. Among small-grain cereals, rye had the shortest maturity period followed by the descendant, hexaploid triticale while both wheat species had the longest maturity period. In addition, rye and triticale were more robust to Fusarium infection and deoxynivalenol accumulation, making them safer grain sources for human and animal consumption. However, a few resistant cultivars have been produced by prolonged conventional breeding efforts in durum wheat and bread wheat. High genetic variation was present within each crop species and can be exploited for resistance breeding. In this thesis, the genetic architecture of FHB resistance in rye was investigated for the first time, by means of genome-wide association study (GWAS) and genomic prediction (GP). GWAS detected 15 QTLs for Fusarium culmorum head blight severity, of which two had major effects. Both weighted and unweighted GP approaches yielded higher prediction abilities than marker-assisted selection (MAS) for FHB severity, heading stage and plant height. Genomics-assisted breeding can shorten the duration of breeding rye for FHB resistance. In the past decade, genetic mapping and omics were used to identify a multitude of QTLs and candidate genes for ear rot resistances and mycotoxin accumulation in maize. The polygenic nature of resistance traits, high genotype x environment interaction, and large-scale phenotyping remain major bottlenecks to increasing genetic gains for ear rots resistance in maize. Phenotypic and molecular analyses of DH lines originating from two European flint landraces (“Kemater Landmais Gelb”, KE, and “Petkuser Ferdinand Rot”, PE) revealed high variation for Gibberella ear rot (GER) severity and three agronomic traits viz. number of days to female flowering, plant height and proportion of kernels per cob. By employing multi-SNP GWAS method, we found four medium-effect QTLs and many small-effect (10) QTLs for GER severity in combined DH libraries (when PCs used as fixed effects), none co-localized with the QTLs detected for the three agronomic traits analyzed. However, one major QTL was detected within KE DH library for GER severity. Two prioritized SNPs detected for GER resistance were associated with 25 protein-coding genes placed in various functional categories, which further enhances scientific knowledge on the molecular mechanisms of GER resistance. Genome-based approaches seems promising for tapping GER resistance alleles from European maize landraces for applied breeding. After several cycles of backcrossing and sufficient selection for agronomic adaptation traits, the resistant lines identified in this thesis can be incorporated into existing maize breeding programs to improve immunity against F. graminearum ear infection. Breeding progress can be faster using KE landrace than PE. A successful validation of QTLs identified in this thesis can pave way for MAS in rye and marker-assisted backcrossing in maize. Effective implementation of genomic selection requires proper design of the training and validation sets, which should include part of the current breeding population.
Genome-wide association mapping of molecular and physiological component traits in maize
(2013) Riedelsheimer, Christian; Melchinger, Albrecht E.
Genome-wide association (GWA) mapping emerged as a powerful tool to dissect complex traits in maize. Yet, most agronomic traits were found to be highly polygenic and the detected associations explained together only a small portion of the total genetic variance. Hence, the majority of genetic factors underlying many agronomically important traits are still unknown. New approaches are needed for unravelling the chain from the genes to the phenotype which is still largely unresolved for most quantitative traits in maize. Instead of further enlarging the mapping population to increase the power to detect even smaller QTL, this thesis research aims to present an alternative route by mapping not the polygenic trait of primary interest itself, but genetically correlated molecular and physiological component traits. As such components represent biological sub-processes underlying the trait of interest, they are supposed to be genetically less complex and thus, more suitable for genetic mapping. Using large diversity panels of maize inbred lines, this approach is demonstrated with (i) biomass yield by using metabolites and lipids as molecular component traits and with (ii) chilling sensitivity by using physiological component traits such as photosynthesis parameters derived from chlorophyll fluorescence measurements. In a first step, we developed a sampling and randomization scheme which allowed us to obtain metabolic and lipid profiles from large-scale field trials. Both profiles were found to be inten- sively structured reflecting their functional grouping. They also showed repeatabilities higher than in comparable profiles obtained in previous studies with the model plant Arabidopsis under controlled conditions. By applying GWAS with 56,110 SNPs to metabolites and lipids, large-scale genetic associations explaining more than 30 % of the genetic variance were detected. Confounding with structure was found to be a problem of less extent for molecular components than for agronomic traits like flowering time. The lipidome was also found to show a multilevel control architecture similar as employed in controlling complex mechanical systems. In several instances, direct links between candidate genes underlying the detected associations and agronomic traits could be established. An example is cinnamoyl-CoA reductase, a key enzyme in the lingin biosynthesis pathway. It was found to be a candidate gene underlying a major QTL found for several intermediates in the lignin biosynthesis pathways. These intemediates were in turn found to be correlated with plant height, lignin content, and dry matter yield at the end of the vegetation period. The different signs of these correlations indicated that the relationships between pathway intermediates and the final product is not simple. Directly modeling complex traits with individual component traits may therefore require consideration of feedback loops and other interdependencies. Such connections were however found difficult to be established with physiological components underlying chilling sensitivity. The main reasons for this were the weak correlations between physiological components under controlled conditions and chilling sensitivity in the field as well as high levels of genotype × environment interactions caused by the complex and environment- dependent responses of maize after perception of chilling temperatures. The approach explored in this thesis research uses component traits to gain biological insights about the genetic control of biomass yield and chilling sensitivity evaluated in diverse populations of still manageable sizes. We showed that GWAS with 56k SNPs can identify large additive effects for component traits correlated with these traits. For mapping epistatic interactions and rare variants, classical linkage mapping with biparental populations will be a reasonable complementary approach. However, controlling and modeling genotype × environment interactions remains an important issue for understanding the genetic basis of especially chilling sensitivity. If the goal is merely to predict the phenotypic value in a given set of en- vironments, black-box genomic selection methods with either SNPs, molecular profiles, or a combination of both, are very promising strategies to achieve this goal.
Genome-wide prediction of testcross performance and phenotypic stability for important agronomic and quality traits in elite hybrid rye (Secale cereale L.)
(2016) Wang, Yu; Miedaner, Thomas
Genomic selection offers a greater potential for improving complex, quantitative traits in winter rye than marker-assisted selection. Prediction accuracies for grain yield for unrelated test populations have, however, to be improved. Nevertheless, they are already favorable for selecting phenotypic stability of quality traits.
Genomic and phenotypic improvement of triticale (×Triticosecale Wittmack) line and hybrid breeding programs
(2021) Trini, Johannes Philipp; Würschum, Tobias
Triticale (×Triticosecale Wittmack) breeding is a success story as it evolved to a serious alternative in farmer’s crop rotations since the 1970s and is grown globally on around 4 million hectares today. New developments, however, pointed out additional possibilities to improve triticale line and hybrid breeding programs increasing its future competitiveness and were evaluated in this study. In more detail, these were to (i) examine the genetic control and evaluate long term genetic trends of plant height in Central European winter triticale, (ii) evaluate the potential of triticale hybrid breeding and hybrid prediction approaches in triticale with a focus on biomass yield, (iii) introduce and examine a concept bypassing the time and resource consuming evaluation of female candidate lines in cytoplasmatic male sterility (CMS) based hybrid breeding, and (iv) to draw conclusions for the future improvement of triticale line and hybrid breeding programs. The genome wide association study detected markers significantly associated with plant height and developmental stage, respectively. These explained 42,16% and 29,31% of the total genotypic variance of plant height and development stage and are probably related to four and three quantitative trait loci (QTL), respectively. The two major QTL detected for plant height were located on chromosomes 5A and 5R which most likely could be assigned to the known height reducing genes Rht12 from wheat and Ddw1 from rye. The third major QTL detected located on chromosome 4B could not be assigned to a known height reducing gene and it cannot be precluded, that these significantly associated markers are identifying one and the same QTL as the markers located on chromosome 5R, as these showed a high linkage disequilibrium amongst each other. Evaluating the 129 registered cultivars showed that plant height decreased since the 1980’s. Evaluating their genetic constitution revealed that most cultivars carried at least one height reducing QTL and that plant height could be reduced even further in cultivars combining more than one height reducing QTL. It was further observed that the frequency of cultivars carrying one or a combination of height reducing QTL increased since the 1980’s. A considerable amount of heterosis has been observed for biomass related traits in triticale hybrids before. However, the use of hybrid prediction approaches for these traits has not been evaluated. Hybrid prediction based on mid parent values already showed very good results illustrating their potential to preselect the most promising parents as prediction accuracies based on parental general combining ability (GCA) effects were only slightly better. When incorporating molecular markers into GCA based prediction accuracies, prediction accuracies decreased slightly compared to prediction accuracies solely based on phenotypic GCA effects. Predicting hybrids incorporating one or two untested parental lines, imitating a scenario where novel female and/or male candidate lines are introduced into a hybrid breeding program, reduced genomic prediction accuracies even further due to the decreasing amount of information which could be exploited from the parents. Additionally including specific combining ability (SCA) effects in the genomic prediction models did not yield additional use. A high proportion of SCA variance compared to the total genetic variance decreased prediction accuracies for the traits fresh and dry biomass yield. In this study simulation studies were used to demonstrate what a prediction accuracy of a specific value actually means for a hybrid breeding programs. Further, an approach was introduced and evaluated showing great potential to evaluate novel female candidate lines for their use in a CMS based hybrid breeding program by bypassing their time and resource demanding introgression into a male sterile cytoplasm using three way hybrids. Prediction accuracies obtained by this novel approach showed highly promising results for most evaluated traits compared to prediction accuracies based on GCA effects or mid parent performance. Additionally incorporating SCA effects into the prediction models showed only a little increase of the prediction accuracies. Further, the results were supported by simulation studies adjusting different parameters, such as the number of parents or the proportion of SCA variance compared to the total genetic variance.
Genomics-assisted breeding strategies for quantitative resistances to Northern corn leaf blight in maize (Zea mays L.) and Fusarium diseases in maize and in triticale (× Triticosecale Wittm.)
(2021) Galiano Carneiro, Ana Luísa; Miedaner, Thomas
Fusarium head blight (FHB) in triticale (× Triticosecale Wittm.), Gibberella ear rot (GER) and Northern corn leaf blight (NCLB) in maize (Zea mays L.) are devastating crop diseases causing yield losses and/or reducing grain quality worldwide. Resistance breeding is the most efficient and sustainable approach to reduce the damages caused by these diseases. For all three pathosystems, a quantitative inheritance based on many genes with small effects has been described in previous studies. Hence, this thesis aimed to assess the potential of genomics-assisted breeding strategies to reduce FHB, GER and NCLB in applied breeding programs. In particular, the objectives were to: (i) Dissect the genetic architecture underlying quantitative variation for FHB, GER and NCLB through different quantitative trait loci (QTL) and association mapping approaches; (ii) assess the potential of genomics-assisted selection to select superior triticale genotypes harboring FHB resistance; (iii) phenotype and characterize Brazilian resistance donors conferring resistance to GER and NCLB in multi-environment trials in Brazil and in Europe; and (iv) evaluate approaches for the introgression and integration of NCLB and GER resistances from tropical to adapted germplasm. The genome-wide association study (GWAS) conducted for FHB resistance in triticale revealed six QTL that reduced damages by 5 to 8%. The most prominent QTL identified in our study was mapped on chromosome 5B and explained 30% of the genotypic variance. To evaluate the potential of genomic selection (GS), we performed a five-fold cross-validation study. Here, weighted genomic selection increased the prediction accuracy from 0.55 to 0.78 compared to the non-weighted GS model, indicating the high potential of the weighted genomic selection approach. The successful application of GS requires large training sets to develop robust models. However, large training sets based on the target trait deoxynivalenol (DON) are usually not available. Due to the rather moderate correlation between FHB and DON, we recommend a negative selection based on genomic estimated breeding values (GEBVs) for FHB severity in early breeding stages. In the long-run, however, we encourage breeders to build and test GS calibrations for DON content in triticale. The genetic architecture of GER caused by Fusarium graminearum in maize was investigated in Brazilian tropical germplasm in multi-environment trials. We observed high genotype-by-environment interactions which requires trials in many environments for the identification of stable QTL. We identified four QTL that explained between 5 to 22% of the genotypic variance. Most of the resistance alleles identified in our study originated from the Brazilian tropical parents indicating the potential of this exotic germplasm as resistance source. The QTL located on chromosome bin 1.02 was identified both in Brazilian and in European trials, and across all six biparental populations. This QTL is likely stable, an important feature for its successful employment across different genetic backgrounds and environments. This stable QTL is a great candidate for validation and fine mapping, and subsequent introgression in European germplasm but possible negative linkage drag should be tackled. NCLB is another economically important disease in maize and the most devastating leaf disease in maize grown in Europe. Virulent races have already overcome the majority of known qualitative resistances. Therefore, a constant monitoring of S. turcica races is necessary to assist breeders on the choice of effective resistances in each target environment. We investigated the genetic architecture of NCLB in Brazilian tropical germplasm and identified 17 QTL distributed along the ten chromosomes of maize explaining 4 to 31% of the trait genotypic variance each. Most of the alleles reducing the infections originated from Brazilian germplasm and reduced NCLB between 0.3 to 2.5 scores in the 1-9 severity scale, showing the potential of Brazilian germplasm to reduce not only GER but also NCLB severity in maize. These QTL were identified across a wide range of environments comprising different S. turcica race compositions indicating race non-specific resistance and most likely stability. Indeed, QTL 7.03 and 9.03/9.04 were identified both in Brazil and in Europe being promising candidates for trait introgression. These major and stable QTL identified for GER and NCLB can be introgressed into elite germplasm by marker-assisted selection. Subsequently, an integration step is necessary to account for possible negative linkage drag. A rapid genomics-assisted breeding approach for the introgression and integration of exotic into adapted germplasm has been proposed in this thesis. Jointly, our results demonstrate the high potential of genomics-assisted breeding strategies to efficiently increase the quantitative resistance levels of NCLB in maize and Fusarium diseases in maize and in triticale. We identified favorable QTL to increase resistance levels in both crops. In addition, we successfully characterized Brazilian germplasm for GER and NCLB resistances. After validation and fine mapping, the introgression and integration of the QTL identified in this study might contribute to the release of resistant cultivars, an important pillar to cope with global food security.
Integration of hyperspectral, genomic, and agronomic data for early prediction of biomass yield in hybrid rye (Secale cereale L.)
(2021) Galán, Rodrigo José; Miedaner, Thomas
Currently, the combination of a growing bioenergy demand and the need to diversify the dominant cultivation of energy maize opens a highly attractive scenario for alternative biomass crops. Rye (Secale cereale L.) stands out for its vigorous growth and increased tolerance to abiotic and biotic stressors. In Germany, less than a quarter of the total harvest is used for food production. Consequently, rye arises as a source of renewables with a reduced bioenergy-food tradeoff, emerging biomass as a new breeding objective. However, rye breeding is mainly driven by grain yield while biomass is destructively evaluated in later selection stages by expensive and time-consuming methods. The overall motivation of this research was to investigate the prospects of combining hyperspectral, genomic, and agronomic data for unlocking the potential of hybrid rye as a dual-purpose crop to meet the increasing demand for renewable sources of energy affordably. A specific aim was to predict the biomass yield as precisely as possible at an early selection stage. For this, a panel of 404 elite rye lines was genotyped and evaluated as testcrosses for grain yield and a subset of 274 genotypes additionally for biomass. Field trials were conducted at four locations in Germany in two years (eight environments). Hyperspectral fingerprints consisted of 400 discrete narrow bands (from 410 to 993 nm) and were collected in two points of time after heading for all hybrids in each site by an uncrewed aerial vehicle. In a first study, population parameters were estimated for different agronomic traits and a total of 23 vegetation indices. Dry matter yield showed significant genetic variation and was stronger correlated with plant height (r_g=0.86) than with grain yield (r_g=0.64) and individual vegetation indices (r_g: =<|0.35|). A multiple linear regression model based on plant height, grain yield, and a subset of vegetation indices surpassed the prediction ability for dry matter yield of models based only on agronomic traits by about 6 %. In a second study, whole-spectrum data was used to indirectly estimate dry matter yield. For this, single-kernel models based on hyperspectral reflectance-derived (HBLUP) and genomic (GBLUP) relationship matrices, a multi-kernel model combining both matrices, and a bivariate model fitted also with plant height as a secondary trait, were considered. HBLUP yielded superior predictive power than the models based on vegetation indices previously tested. The phenotypic correlations between individual wavelengths and dry matter yield were generally significant (p < 0.05) but low (r_p: =< |0.29|). Across environments and training set sizes, the bivariate model yielded the highest prediction abilities (0.56 – 0.75). All models profited from larger training populations. However, if larger training sets cannot be afforded, HBLUP emerged as a promising approach given its higher prediction power on reduced calibration populations compared to the well-established GBLUP. Before its incorporation into prediction models, filtering the hyperspectral data available by the least absolute shrinkage and selection operator (Lasso) was worthwhile to deal with data dimensionally. In a third study, the effects of trait heritability, as well as genetic and environmental relatedness on the prediction ability of GBLUP and HBLUP for biomass-related traits were compared. While the prediction ability of GBLUP (0.14 - 0.28) was largely affected by genetic relatedness and trait heritability, HBLUP was significantly more accurate (0.41 - 0.61) across weakly connected datasets. In this context, dry matter yield could be better predicted (up to 20 %) by a bivariate model. Nevertheless, due to environmental variances, genomic and reflectance-enabled predictions were strongly dependant on a sufficient environmental relationship between data used for model training and validation. In summary, to affordably breed rye as a double-purpose crop to meet the increasing bioenergy demands, the early prediction of biomass across selection cycles is crucial. Hyperspectral imaging has proven to be a suitable tool to select high-yielding biomass genotypes across weakly linked populations. Due to the synergetic effect of combining hyperspectral, genomic, and agronomic traits, higher prediction abilities can be obtained by integrating these data sources into bivariate models.
Phenotypic and genomics-assisted breeding of soybean for Central Europe : from environmental adaptation to tofu traits
(2022) Kurasch, Alena; Würschum, Tobias
Soybean (Glycine max Merr.) is one of the major crops in the world providing an important source of protein and oil for food and feed; however it is still a minor crop in Central Europe. Soybean cultivation can play an important role in a more sustainable agricultural system by increasing local and regional protein production in Europe. The demand for locally produced soybean products is still growing in Europe. The key for a successful establishment of soybean cultivation in Europe is adaptation of soybean varieties to the Central European growing conditions. For the latitudinal adaptation to long-day conditions in Central to Northern Europe, an adapted early flowering and maturity time is of crucial importance for a profitable cultivation. The key traits flowering and maturity are quantitatively inherited and mainly affected by photoperiod responsiveness and temperature sensitivity. The most important loci for an early flowering and maturity are E1-E4 and the various allelic combinations condition soybean flowering and maturity time and therefore strongly contribute to the wide adaptability (Jiang et al., 2014; Tsubokura et al., 2014; M. Xu et al., 2013). Besides the main usage as protein source for animal feeding, soybean is also a very valuable source for human consumption. Tofu is enjoying ever greater popularity in Europe, as it is one of the best sources of plant protein with additional health benefits, rich in essential amino acids, beneficial lipids, vitamins, and minerals, as well as other bioactive compounds, such as isoflavones, soyasaponin, and others, (Lima et al., 2017; Zhang et al., 2018). Thus, plant breeding has to provide not only well-adapted varieties with good agronomic and quality properties, but also provide varieties well-suited to the further processing into soymilk and tofu. Therefore, a good knowledge about the breeding target, how to assess it and how it is inherited is crucial. The conducted studies covered a broad range of aspects relevant to improve a soybean breeding program. By combining environmental analysis, E-gene analysis, genomic approaches (QTL mapping and genomic prediction), and tofu phenotyping, breeder decisions become more accurate and targeted in the way of selection thereby increasing the genetic gain. In addition, combining the results of the different aspects helps to optimize the resources of a breeding program. Increasing the knowledge about the different aspects from environment to tofu QTL enables a breeder to be more precise and focused. But the more targeted and specific, the more complex a breeding program gets, which requires adequate tools to handle all the different information in a meaningful and efficient way to enable a quick and precise breeding decision.
Speciation and domestication genomics of Amaranthus spp.
(2017) Stetter, Markus; Schmid, Karl J.
The genus Amaranthus consists of 50 to 70 species, including several cultivated and weedy species. The seeds of the three grain amaranth species, A. caudatus, A. cruentus and A. hypochondriacus have a high nutritional value and are gluten free. In this work, three main aspects of amaranth genetics are studied, because previous work was limited to few species and few genetic markers: First, the evolutionary relationship between species in the genus; second, the domestication syndrome of South American grain amaranth; and third, crossing methods and controlled growth conditions for amaranth breeding. The genus has been taxonomically split into three subgenera, A. Amaranthus, A. Albersia and A. Acnida. Together with their two relatives A. hybridus and A. quitensis, the three grain amaranths form the Hybridus complex within the A. Amaranthus subgenus. We used genotyping by sequencing (GBS) of 94 genebank accessions, representing 35 species to infer the phylogeny of Amaranthus. SNPs were called using de novo and reference genome based methods and genome sizes of the species were measured using flow cytometry. The analysis of genome size evolution within the genus revealed that with the exception of two lineages polyploidization played a minor role in the history of the genus. A distancebased neighbor joining tree of individual accessions and a species tree based on the multispecies coalescent were constructed. Both phylogenies supported the previous taxonomic classification into three subgenera, but split the A. Acnida subgenus into two distant groups. Analyzing the Hybridus complex gave insights into the domestication history of grain amaranth. The complex was well separated from the other species in the A. Amaranthus subgenus and included the three grain amaranth species and their wild relatives. Individuals within the Hybridus complex did not cluster by species, but rather by their geographic origin from South and Central America. Geographically separated lineages of A. hybridus appeared to be the common ancestor of the three cultivated grain amaranths, while A. quitensis was involved in the domestication of A. caudatus. The domestication of grain amaranth remains unclear and seems to be complex, because the domestication syndrome that differentiates crops from their wild ancestors is only weakly pronounced. Therefore, the domestication syndrome in South American grain amaranth (A. caudatus) was studied by characterizing genetic and phenotypic diversity of A. caudatus and the two potential wild relatives, A. hybridus and A. quitensis. To investigate the evolutionary relationship of A. caudatus and its potential wild ancestors, 119 amaranth accession from the Andean region were characterized using 9,485 GBS derived SNPs. Additionally, two domestication related phenotypes, seed color and seed size, were analyzed. None of the accessions of wild amaranths had white seeds, while this was the predominant seed color in A. caudatus. The seed size did not significantly differ between species, but a genetically distinct group of A. caudatus from Bolivia had significantly larger seeds than the other groups. The genetic analysis revealed a strong differentiation of A. caudatus from its wild relatives. The two relatives did not cluster according to their species assignment, but rather by their geographic origins from Peru and Ecuador. Surprisingly, A. caudatus had a higher genetic diversity than its two close relatives and shared a high proportion of polymorphisms with them, consistent with the absence of strong bottlenecks or high levels of gene flow between them. Efficient crosses are an essential tool for plant research and breeding. Three different crossing methods (open pollination, hot water emasculation and hand emasculation) were evaluated for their efficiency and validated with low cost genetic markers. We identified controlled growth conditions for amaranth that allow short generation times of only six weeks instead of six months. All three crossing methods successfully produced offspring, but with different success rates. Open pollination had the lowest (10%) and hand emasculation the highest success rate (74%). Hot water emasculation showed an intermediate success rate (26%), but high maximum of 94%. Additionally, this method is easy to perform and suitable for large-scale hybrid production. Eleven PCR-based SNP markers were found to be sufficient for intra- and interspecific hybrid identification. Despite the very small flowers, amaranth crosses can be carried out efficiently and verified with inexpensive SNP markers with short generation times under suitable conditions. The phylogeny and population genetic analysis suggest that amaranth domestication was incomplete. Gene flow from A. quitensis into A. caudatus might have prevented the fixation of domestication related alleles. The genus phylogeny and the over 200 genotyped accessions in this work provide the largest genomic resource for amaranth so far.
Strategies for selecting high-yielding and broadly adapted maize hybrids for the target environment in Eastern and Southern Africa
(2012) Windhausen, Sandra Vanessa; Melchinger, Albrecht E.
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.