hohPublica :: Browsing by Subject "Phänotyp"

Browsing by Subject "Phänotyp"

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Bioinformatische Analyse und funktionelle Charakterisierung von strukturellen Genvarianten in ADME-Genen in humaner Leber
(2016) Tremmel, Roman; Zanger, Ulrich M.
Pharmacogenetics is the study about inter-individual genetic variation that influences the response to drugs and other xenobiotics. A major part of this variation is due to hepatic drug metabolism with enzymes, transporters and receptors involved in the ab-sorption, distribution, metabolism and the excretion of drugs, xenobiotics and endoge-nous substances and collectively defined as ADME-genes. Genetic factors along with environmental and endogenous factors, including gender, age, inflammation processes and others are known to influence the expression and activity of ADME-genes. These influences can affect drug response, side effects or toxicity. According to newly published data, the human genome of any subject differs from a reference genome at 4.1 to 5.0 million positions. More than 99.9% of these differences are single nucleotide polymorphisms (SNP) or short insertions or deletions. Further-more, a person carries up to 2,500 structural variants, including copy number variations (CNV) affecting ~20 million bases (1000 Genomes Project Consortium et al., 2015). Thus structural variants affect more bases than SNPs. Per definition the CNVs are du-plicated or deleted DNA segments greater than 1kb and it was shown that they cover at least 12-30% of the human genome. Genome-wide studies investigating the function-ality of CNVs in the fruit fly, the mouse and in humans showed that there are genes whose expression is clearly affected by CNVs (dosage-sensitve), but also genes show-ing lower expression with increased copy number (dosage reversed) or genes without any expression alterations despite different copy number (dosage-insensitive). A prominent example of CNVs influencing drug metabolism is the phase I gene CYP2D6. Carriers of reduced or amplified gene copies show significantly altered ex-pression and enzyme activity levels and also a different drug metabolism of substrates like codeine (opioid) or tamoxifen (selective estrogen receptor antagonist) in compari-son to carriers with normal copy status of two. Genotyping of CYP2D6 gene copy num-ber may thus help to adjust drug dosage in a genotype dependent manner. In this work I investigated if further ADME-genes are affected by CNVs and if these variants have a functional impact on the expression phenotype and drug metabolism. The distribution of CNVs in the most important ADME-genes (n=340) was investigated in three independent cohorts using CNV data in a public accessible database of ge-nomic variants (DGV; dgv.tcag.ca), processed SNP microarray data of paired samples of healthy (n=269) and tumor (n=351) liver tissue of the TCGA project (http://cancergenome.nih.gov/) and ADME-panel based exon next generation sequenc-ing (NGS) applied on 150 well documented human liver samples of an in-house cohort (IKP148). For the NGS data analysis a method was developed and optimized to esti-mate the relative copy number of the ADME genes or every single exon via the read depth. The results were validated using qPCR with specific TaqMan assays. RNA-sequencing data of 50 healthy TCGA liver samples, and normalised expression data from microarray experiments applied to lymphoblastoid cell lines (LCL) from the HapMap samples and the 150 human liver samples (IKP148) were used to analyse the association between CNVs and the mRNA expression. Furthermore, in the IKP148 liver samples protein and enzymatic activity levels were available or measured using West-ernBlot and mass spectrometry for selected ADME-genes. All pharmacologically important CNVs of phase I and phase II genes, including CYP2A6, CYP2D6, GSTM1, GSTT1, SULT1A1 and UGT2B17 could be confirmed in all datasets. CNVs which were known, but so far not functionally assessed were found in the phase I and II genes CES1, CYP2E1, CYP21A2, UGT2B15 and UGT2B28. In this work rare CNVs (<1%) were mainly found for transporters like ABCA2, SLC2A4 and SLC47A1. The analysis of the read depth in the IKP148 samples data revealed hybrid genes for CYP2A6 and CYP2D6 with their pseudogenes and allowed a fine mapping of the different alleles. The functional analysis further confirmed the positive association between CNVs and the mRNA expression of CYP2A6, CYP2D6, GSTM1, GSTT1, SULT1A1 and UGT2B17 in all three cohorts. The combination of all data from the NGS project in the IKP148 liver subjects, including SNP and CNV genotypes showed that 11% and 53% of the variability of CYP2A6 and CYP2D6 enzyme activity were explained by the genetic factors. In contrast the mRNA expression of the genes CES1 and CYP2E1 was not dependent of the CNV pattern in healthy liver tissue (IKP148 and TCGA) and lymphoblastoid cell lines. A detailed analysis of the protein and enzyme activity levels (chlorzoxazone-6-hydroxylation) of CYP2E1 confirmed the dosage-insensitivity in the IKP148 liver sub-jects. The dosage compensation can be principally explained by different mechanisms and could be tissue or tumor specific. Furthermore, CNV-linked genetic variants, altered miRNA regulation, incomplete inclusion of regulatory elements or coding sequences, hybridgenes, monoallelic expression, feedback loops or epigenetics could be factors which mask the CNV effect. In this work a haplotype analysis of the CYP2E1 region identified SNPs which were linked to the duplication and a reduced expression phenotype in persons with European ancestry. Using in silico prediction tools we found a relation of one of the linked SNPs in the 3’UTR with additional predicted miRNA bind-ing sites potentially regulating additional CYP2E1 gene copies. The CNV influence on the mRNA expression of the genes CYP21A2, UGT2B25 and UGT2B28 was inconsistent. Although CYP21A2 deletions were associated with a de-creased expression, gene duplications showed normal expression levels compared to samples with two copies. A significant influence of UGT2B28 CNVs was found in LCLs but not in human liver samples (IKP148 and TCGA). In total 7 of 17, 2 of 12 and 3 of 14 ADME genes showed a significant association between expression and CNV type in the IKP148, TCGA and LCLs of the HapMap samples, respectively. In the TCGA cancer tissue nearly all ADME-genes carry CNVs and in 30% of the genes a significant correlation was observed. With cooperation partners further polymorphisms and phenotypes of SULT1A1 and CYP2E1 were analyzed. CYP2E1: In this part of the thesis factors influencing the risk of developing differentiat-ed thyroid carcinoma (DTC) were investigated. Known risk factors for the progression of DTC are genetic and environmental factors, including ionizing radiations, previous thyroid diseases, and hormone factors. It has been speculated that dietary acrylamide intake correlates with the DTC formation. The acrylamide molecule is metabolized by CYP2E1 to the reactive carcinogenic glycidamide. The enzymatic reaction is probably dependent on the CYP2E1 genotype. Together with a cooperation partner (Prof. Dr. Landi, University of Pisa, Pisa, Italy) we investigated, whether CYP2E1 variants influ-ence the DTC risk. Prof. Landi and colleagues used a case-control-cohort and a haplo-type approach and observed a significant association between a tag-SNP rs2480258 (A allele), which covers variants in intron eight and the 3’UTR, and an increased DTC risk. In the human liver samples (IKP148) the rs2480258 genotypes were assessed using an imputation analysis and it was shown that particularly the A allele of the SNP reduce significantly the mRNA and protein expression and the enzyme activity. An in silico prediction for the molecular mechanism suggested that miR570 specifically down regulates the transcripts in carriers of the A allele. These results indicated that the inter-individual CYP2E1 activity as well as acrylamide (similar to glycidamide) influences the risk for DTC. SULT1A1: Methyleugenol, a secondary metabolite present in herbs such as basil or laurel is metabolized in humans by sulfation to a reactive product which can covalently bind to DNA. The resulting DNA adducts are mutagenic and can promote carcinogene-sis. Our cooperation partner Prof. Dr. Hans-Rudolf Glatt (German Institute for Human Nutrition Potsdam-Rehbruecke (DIfE), Nuthetal, Germany) had shown, that the meth-yleugenol metabolism takes place in the liver of mice and is mainly catalyzed by the phase II enzyme SULT1A1. To investigate these facts in humans, the methyleugenol DNA-adduct levels were measured by the cooperation partner in liver tissues (n=121; IKP148) using mass spectrometry. In this work the SULT1A1 protein levels were de-termined using western blot analysis and the relation between the DNA adducts as well as the SULT1A1 expression and the SULT1A1 CNVs was assessed. The SULT1A1 mRNA and protein expression were significantly correlated to the DNA adducts, e.g. higher SULT1A1 expression resulted in higher adduct levels. This emphasized the role of SULT1A1 in the in vivo metabolism in human liver samples. As mentioned above, there were individuals (IKP148) carrying one, two, three, four and five copies of SULT1A1. Deletions were found less frequent (4%) than duplications (36%). The CNVs were significantly associated with the SULT1A1 mRNA and protein expression. This result was consistent to previous studies investigating the association between SULT1A1 CNVs and enzyme activity. The methyleugenol DNA adduct levels were also significantly associated to the SULT1A1 copy number. Carriers of at least three gene copies exhibited a 2.8-fold higher DNA adduct level compared to donors carrying only one SULT1A1 gene copy. As a consequence this could mean that individuals with mul-tiple SULT1A1 copies reach faster, more often and more easily critical and ultimate adduct levels which increase the risk for developing cancer. Future studies should clari-fy whether methyleugenol intake as well as the individual SULT1A1 CNV make-up in-fluences the risk of cancer.
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.
Mixed modelling for phenotypic data from plant breeding
(2011) Möhring, Jens; Piepho, Hans-Peter
Phenotypic selection and genetic studies require an efficient and valid analysis of phenotypic plant breeding data. Therefore, the analysis must take the mating design, the field design and the genetic structure of tested genotypes into account. In Chapter 2 unbalanced multi-environment trials (METs) in maize using a factorial design are analysed. The dataset from 30 years is subdivided in periods of up to three years. Variance component estimates for general and specific combining ability are calculated for each period. While mean grain yield increased with ongoing inter-pool selection, no changes for the mean of dry matter yield or for variance component estimate ratios were found. The continuous preponderance of general combining ability variance allows a hybrid selection based on general combining effects. The analysis of large datasets is often performed in stage-wise fashion by analysing each trial or location separately and estimating adjusted genotype means per trial or location. These means are then submitted to a mixed model to calculate genotype main effects across trials or locations. Chapter 3 studies the influence of stage-wise analysis on genotype main effect estimates for models which take account of the typical genetic structure of genotype effects within plant breeding data. For comparison, the genetic effects were assumed both fixed and random. The performance of several weighting methods for the stage-wise analysis are analysed by correlating the two-stage estimates with results of one-stage analysis and by calculating the mean square error (MSE) between both types of estimate. In case of random genetic effects, the genetic structure is modelled in one of three ways, either by using the numerator relationship matrix, a marker-based kinship matrix or by using crossed and nested genetic effects. It was found that stage-wise analysis results in comparable genotype main effect estimates for all weighting methods and for the assumption of random or fixed genetic effect if the model for analysis is valid. In case of choosing invalid models, e.g., if the missing data pattern is informative, both analyses are invalid and the results can differ. Informative missing data pattern can result from ignoring information either used for selecting the analysed genotypes or for selecting the test environments of genotypes, if not all genotypes are tested in all environments. While correlated information from relatives is rarely directly used for analysis of plant breeding data, it is often used implicitly by the breeder for selection decisions, e.g. by looking at the performance of a genotype and the average performance of the underlying cross. Chapter 4 proposed a model with a joint variance-covariance structure for related genotypes in analysis of diallels. This model is compared to other diallel models based on assumptions regarding the inheritance of several independent genes, i.e. on genetic models with more restrictive assumptions on the relationship between relatives. The proposed diallel model using a joint variance-covariance structure for parents and parental effects in crosses is shown to be a general model subsuming other more specialized diallel models, as these latter models can be obtained from the general model by adding restrictions on the variance-covariance structure. If no a priori information about the genetic model is available the proposed general model can outperform the more restrictive models. Using restrictive models can result in biased variance component estimates, if restrictions are not fulfilled by the data analysed. Chapter 5 evaluates, whether a subdivision of 21 triticale genotypes into heterotic pools is preferable. Subdividing genotypes into heterotic pools implies a factorial mating design between heterotic pools and a diallel mating design within each heterotic pool. For two (or more) heterotic pools the model is extended by assuming a joint variance-covariance structure for parental effects and general combing ability effects within the diallel and within the factorials. It is shown that a model with two heterotic pools has the best model fit. The variance component estimates for the general combing ability decrease within the heterotic pools and increase between heterotic pools. The results in Chapter 2 to 5 show, that an efficient and valid analysis of phenotypic plant breeding data is an essential part of the plant breeding process. The analysis can be performed in one or two stages. The used mixed models recognizing the field and mating design and the genetic structure can be used for answering questions about the genetic variance in cultivar populations under selection and of the number of heterotic pools. The proposed general diallel model using a joint variance-covariance structure between related effects can further be modified for factorials and other mating designs with related genotypes.
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.
The development of phenotypic protocols and adjustment of experimental designs in Pelargonium zonale breeding
(2018) Molenaar, Heike; Piepho, Hans-Peter
Ornamental plant variety improvement is limited by current phenotyping approaches and the lack of use of experimental designs. Robust phenotypic data obtained from experiments laid out to best control local variation by blocking allow adequate statistical analysis and are crucial for any breeding purpose, including MAS. Often experiments consist of multiple phases like in P. zonale breeding, where in the first phase stock plants are cultivated to obtain the stem cutting count and in the second phase the stem cuttings are further assess for root formation. The first analyses of rooting experiments raised questions regarding options for improving the two-phase experimental layout, for example whether there is a disadvantage to using exactly the same design in both phases. The other question was, whether a design can be optimized across both phases, such that the MVD can be decreased. Instead of generating a separate layout for each phase. Moreover, optimal selection methods that maximize selection gain in P. zonale breeding based on available data collected from unreplicated trials and containing pedigree information were sought. This thesis was conducted to evaluate the benefits of using two-phase experimental designs and corresponding analysis in P. zonale for production related traits, for which it was necessary to establish phenotyping protocols. To optimize the rooting experiments with their two-phase nature, alternative approaches were explored involving two-phase design generation either in phase wise order or across phases. Furthermore, selection methods considering pedigreeinformation (family-index selection) or not (individual selection), were evaluated to enhance selection efficiency in P. zonale breeding. The benefits of using experimental designs in P. zonale breeding was shown by the simulated response to selection. Alternative designs were evaluated by the MVD obtained by the intrablock analysis and the joint inter-block-intra-block analysis. The efficiency of individual and family-index selection was evaluated in terms of heritability obtained from linear mixed models implementing the selection methods. Simulated response to selection varied greatly, depending on the genotypic variances of the breeding population and traits. However, by using efficient designs allowing adequate analysis, a varietal improvement of over 20% of stock plant reduction is possible for stem cutting count, root formation, branch count and flower count. The smallest MVD for alternative designs was most frequently obtained for designs generated across phases rather than for each phase separately, in particular when both phases of the design were separated with a single pseudolevel. Family-index selection was superior to individual selection in P. zonale indicating that the pedigree-based BLUP procedure can further enhance selection efficiency in productionrelated traits in P. zonale. The quantification of genotypic variation by phenotypic protocols and the optimized two-phase designs for estimating genotypic values were necessary and successful steps in laying the foundation for effective MAS. Phenotypic protocols effectively characterized the genetic material on an observational unit level, while the two-phase experimental designs enabled effective characterization on a genotype level by adjusting entry means using linear mixed models. The resulting adjusted entry means are the basis for future genotype phenotype association for MAS.

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