Browsing by Subject "Genomische Vorhersage"

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Evaluation of association mapping and genomic prediction in diverse barley and cauliflower breeding material
(2018) Thorwarth, Patrick; Schmid, Karl J.
Due to the advent of new sequencing technologies and high-throughput phenotyping an almost unlimited amount of data is available. In combination with statistical methods such as Genome-wide association mapping (GWAM) and Genomic prediction (GP), these information can provide valuable insight into the genetic potential of individuals and support selection and crossing decisions in a breeding program. In this thesis we focused on the evaluation of the aforementioned methods in diverse barley (Hordeum vulgare L.) and cauliflower (Brassica oleracea var. botrytis) populations consisting of elite material and genetic resources. We concentrated on the dissection of the influence of specific parameters such as marker type, statistical models, influence of population structure and kinship, on the performance of GWAM and GP. For parts of this thesis, we additionally used simulated data to support findings based on empirical data. First, we compared four different GWAM methods that either use single-marker or haplotypes for the detection of quantitative trait loci in a barley population. To find out the required population size and marker density to detect QTLs of varying effect size, we performed a simulation study based on parameter estimates of the empirical population. We could demonstrate that already in small populations of about 100 individuals, QTLs with a large effect can be detected and that at least 500 individuals are necessary to detect QTLs with an effect < 10%. Furthermore, we demonstrated an increased power of haplotpye based methods in the detection of very small QTLs. In a second study we used a barley population consisting of 750 individuals as training set to compare different GP models, that are currently used by scientists and plant breeders. From the training set 33 offspring families were derived with a total of 750 individuals. This enabled us to assess the prediction ability not only based on cross-validation but also in a large offspring population with varying degree of relatedness to the training population. We investigated the effects of linkage disequilibrium and linkage phase, population structure and relatedness of individuals, on the prediction ability. We could demonstrate a strong effect of the population structure on the prediction ability and show that about 11,203 evenly spaced SNP markers are necessary to predict even genetically distant populations. This implies that at the current marker density prediction ability is based on the relatedness of the individuals. In a third study we focused on the evaluation of GWAM and GP in cauliflower. We focused on the evaluation of genotyping-by-sequencing and compared the influence of imputation methods on the prediction ability and the number of significant associations. We obtained a total 120,693 SNPs in a random collection of 174 cauliflower genebank accessions. We demonstrated that imputation did not increase prediction ability and that the number of detected QTLs only slightly differed between the imputed and the unimputed data set. GP performed well even in such a diverse gene bank sample, but population structure again influenced the prediction ability. We could demonstrate the usefulness and limitations of Genome-wide association mapping and genomic prediction in two species. Even though a lot of research in the field of statistical genetics has provided valuable insight, the usage of Genomic prediction should still be applied with care and only as a supporting tool for classical breeding methods.
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.
QTL mapping and genomic prediction of complex traits based on high-density genotyping in multiple crosses of maize (Zea mays L.)
(2013) Stange, Michael; Melchinger, Albrecht E.
Most important agronomic traits like disease resistance or grain yield (GY) in maize show a quantitative trait variation and, therefore, are controlled by dozens to thousands of quantitative trait loci (QTL). Mapping of these QTL is well established in plant genetics to elucidate the genetic architecture of quantitative traits and to detect QTL for knowledge-based breeding. Nowadays, high-density genotyping is routinely applied in maize breeding and offers a huge number of SNP markers used in association mapping and genomic selection (GS). This enables also the construction of high-density linkage maps with marker densities of 1 cM or even higher. Nevertheless, QTL mapping studies were until recently mostly based on low-density maps. This raises the question if high-density maps are an overkill for QTL mapping, or in contrast, if important QTL mapping parameters would profit from them. High-density maps could also be beneficial for dissection of the complex trait GY into its components 100-kernel weight (HKW) and kernel number (KN). Analysis of these less complex traits may help to unravel the genetic architecture and improve the predictive ability for complex traits. However, an open question is whether consideration of component traits and epistatic interactions in QTL mapping models are beneficial for predicting the performance of untested genotypes for the complex trait GY. In this thesis, high-density linkage maps were constructed for biparental maize populations of doubled haploid (DH) lines and applied in different QTL linkage mapping approaches. In detail, the objectives of this study were to (1) investigate the effect of high-density versus low-density linkage maps in QTL mapping of important QTL mapping parameters and to analyze the resolution of closely linked QTL with experimental data and computer simulations, (2) map QTL for HKW, KN, and GY with high-density maps and to analyze epistatic interactions, (3) compare the prediction accuracy for GY with different QTL mapping models, and (4) answer the question how the composition of the test set (TS) influences the accuracy in genomic prediction of progenies from individual crosses. This thesis was based on five interconnected biparental populations with a total of 699 DH lines evaluated in field experiments for GER resistance related traits as well as for HKW, KN, and GY. All DH lines were genotyped with the Illumina MaizeSNP50 Bead Chip and high-density linkage maps were constructed separately for each population. For evaluation of high-density versus low-density maps on QTL mapping parameters, three linkage maps with marker densities of 1, 2, and 5 cM were constructed, starting from the full linkage map with 7,169 markers mapped in the largest population (N=204). QTL mapping was performed with all three marker densities in the experimental population for GER resistance related traits and for yield related traits, as well as in a simulation study with different population sizes. In the simulation study, independent QTL with additive effects explaining 0.14 to 7.70% of the expected phenotypic variance, as well as linked QTL with map distances of 5 and 10 cM, were simulated. Results showed that high-density maps had only minor effects on the QTL detection power and the proportion of genotypic variance explained. In contrast, support interval length decreased with increasing marker density, indicating an increasing precision of QTL localization. The precision of QTL effect estimates was measured as deviation between the reference additive effects and the estimated QTL effects. It gained from an increase in marker density, especially for small and medium effect QTL. Increasing the marker density from 5 to 1 cM was advantageous for separately detecting linked QTL in coupling phase with both linkage distances. In conclusion, this study showed that QTL mapping parameters relevant for knowledge-based breeding profited from an increase in marker density. For QTL mapping of the complex trait GY and the components HKW and KN, three QTL mapping models were applied to the four largest populations, of which two models were based on the component traits HKW and KN. All models included tests for epistatic interactions. The results showed that heritability was slightly higher for the component traits compared to the complex trait. The average length of support intervals of detected QTL was short with 12 cM, indicating high precision of QTL localization. Co-located QTL with same parental origin of favorable alleles were detected within populations for different traits and between populations for same traits, reflecting common QTL across populations. However, to finally confirm these common QTL, multi-population QTL mapping should be conducted. Based on the detected QTL, predictions for GY showed that epistatic models did not outperform the respective additive models. Nevertheless, component trait based models can be advantageous for identification of favorable allele combinations for multiplicative traits. For all five populations, the comparison of genetic similarities reflected the crossing scheme with full-sib families, half-sib families and unrelated families. The evaluation of prediction accuracies for different scenarios depended on the composition of the TS. Highest prediction accuracies were observed for DH lines within full-sib families, medium values if full-sib DH lines were replaced by half-sib DH lines, and lowest values if the TS comprised of DH lines from unrelated crosses. In conclusion, I found high-density linkage maps to be advantageous for linkage mapping in biparental DH populations by improving important QTL mapping parameters. Higher costs for high-density genotyping are by far compensated by these advantages. Dissecting the complex trait GY into its component traits HKW and KN by component trait based QTL mapping models revealed a complex genetic network of GY. Future research should focus on high-density consensus maps applied in multi-population QTL mapping to take advantage of the improved QTL detection power and to confirm common QTL across populations.