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Genomics is the field of modern biology that studies the genome as the sum of all genes of a given organism. Genomics includes the analysis of genomic variations in order to identify genetic susceptibility loci for various human diseases. Besides genomics, there are related fields summarized by the term "Omics" such as transcriptomics and proteomics, studying the sum of all transcripts and proteins in a defined biological system, respectively. Genetic variants, namely single nucleotide polymorphisms (SNPs) and copy number variations (CNVs) are used to identify genomic loci associated with human traits and diseases. Genome-wide association studies (GWASs) based on SNP data have been performed for a wide range of human traits and diseases. In the population-based Study of Health in Pomerania (SHIP) and the independent SHIP-TREND study, whole-genome genotyping data were available for 4081 and 986 individuals, respectively. In contrast to the widely used GWAS based on SNPs, association studies using CNV data are difficult to implement and thus less common. Therefore, one aim of this work was to detect CNVs using the whole-genome genotyping data available for 4081 individuals from SHIP. Another aim was to develop an efficient workflow for the analysis of these CNVs. As most common genetic variants exhibit only relatively small effects on phenotypic variability, large sample sizes are needed to maximize the statistical power to detect such effects. Therefore, the integration of data from multiple collaborating studies is indispensable. In this context, several CNV studies with the SHIP data have been performed and published, for example on body mass index (BMI) phenotypes where the SHIP cohort was used as a population-based control. Trait-associated genetic markers identified through GWASs are often intergenic or synonymous coding, and those loci identified through whole-genome CNV analyses often contain multiple genes, making it difficult to identify the causal variants. In this context, the functional analysis of identified loci aids in determining causal variant(s). One possibility to conduct functional analysis is the expression quantitative trait loci (eQTL) analysis, defined as the association of genome-wide genotyping data with genome-wide gene expression data based on measured transcriptomes. This allows the identification of genetic variants influencing the expression levels of defined genes. A further example are transcriptome-wide association analysis (TWAS), defined as the association of phenotype data with whole-genome expression data. Thus, another aim of this work was to establish an analysis pipeline for processing such expression data, which were available for about 1000 individuals from the SHIP-TREND study. Here, array-based gene expression data were generated using RNA prepared from whole-blood. Interpretation of TWAS results is often difficult, because of possible reverse causation on gene expression data. Furthermore, technical errors of measurement may bias the results. In a comprehensive work, biological and technical factors influencing measured gene expression data have been identified and were subsequently taken into account to improve the association analyses. To further elucidate the molecular mechanisms underlying the relationship of gene expression levels with human traits or diseases, pathway analyses using the Ingenuity Pathway Analysis (IPA) tool have been performed in connection with the TWAS. As for GWASs, the associations identified in TWAS usually exhibit only small effect sizes, highlighting the need for larger studies or meta-analysis to identify all susceptibility variants. In this context several eQTL- and TWAS meta-analyses using the SHIP-TREND data have been performed, for example on the phenotypes age, sex, BMI, smoking status and serum lipid traits. The results of these analyses are in preparation for publication and the most advanced example, the correlation of expression data with BMI, is presented here. The integration of whole-genome genotyping and expression data provides new functional information of the underlying biological mechanisms of complex human traits and diseases. Within the frame of this work, this could be demonstrated for the example of susceptibility to Helicobacter pylori infection.
Die juvenile idiopathische Arthritis (JIA) umfasst eine Gruppe sehr heterogener Krankheitsbilder, deren Ätiologie und Pathogenese noch nicht abschließend geklärt ist. Es wird eine multifaktorielle Krankheitsentstehung angenommen, bei der genetische Faktoren ein Suszibilitätsrisiko vermitteln. Unter anderem wird der Einfluss regulativer Zytokine auf die Krankheit diskutiert. In dieser Arbeit wurden Assoziationen zweier Promotorpolymorphismen regulativer Zytokine (IL-6 -174 G/C, MIF -173 G/C) untersucht. Mittels genspezifischer PCR und Restriktionsverdau erfolgte die Genotypisierung der beiden SNPs. Die Ergebnisse der Patientenproben wurden mit Hilfe des Chi-Quadrat-Tests und des Fisher’s Exact Tests mit den Kontrollgruppen verglichen. Für die Patientengruppe standen uns Proben aus der DNA-Datenbank der GKJR („Gesellschaft für Kinder- und Jugendrheumatologie“) zur Verfügung. In unserer Arbeit untersuchten wir zwei Kontrollgruppen hinsichtlich der beiden Promotorpolymorphismen. Die eine wurde durch 500 Proben aus einer populationsbasierten Studie (SHIP) gebildet, die andere durch 162 DNA-Proben aus einer Knochenmarkspenderdatei (ALL/AML; BMDG). In dieser Arbeit konnten keine eindeutigen Assoziationen zwischen der JIA bzw. einer ihrer Subgruppen mit einem der beiden untersuchten Promotorpolymorphismen gefunden werden. Es zeigte sich jedoch, dass in unseren Kontrollgruppen, auch im Vergleich zu unseren Patientengruppen, eine sehr unterschiedliche Verteilung der beiden SNPs vorliegt. Dabei zeigte sich interessanterweise dass die Verwendung unterschiedlicher Kontrollgruppen (bezüglich ihrer regionalen Herkunft) von Kaukasiern das Signifikanzniveau erheblich veränderten. Die Ergebnisse unterstreichen die Bedeutung der Auswahl von Patienten- und Kontrollgruppen bezüglich ihrer regionalen Zusammensetzung und genetischen Abstammung bei Krankheitsassoziationsstudien.
Neuroblastoma (NB) is an aggressive, poorly immunogenic tumor in childhood. Therapy for high-risk NB remains challenging. Immunotherapy with anti-GD 2 antibody ch14.18/CHO effectively prolongs the survival of NB patients.
Killer-immunoglobulin-like receptor (KIR)/human leucocyte antigen (HLA) mismatch and Fc gamma receptor (FCGR) polymorphisms are reported to affect antibody-dependent cellular cytotoxicity (ADCC) induced by monoclonal antibodies. To determine whether FCGR polymorphisms and KIR/HLA mismatch are associated with the survival following ch14.18-based immunotherapy, genotyping methods that allow for genotype determination of FCGR2A, -3A, -3B, KIR2DL1, 2DL2, 2DL3, and 3DL1 have been established and applied to the analysis of 53 NB patients treated with ch14.18/CHO.
High-affinity polymorphisms of FCGR2A (H131) and FCGR3A (V158) were associated with improved survival. Importantly, patients displaying both the FCGR3A-V158 and FCGR2A-H131 alleles exhibited significantly improved event-free survival. No association was found between KIR/HLA genotypes or FCGR3B alleles and patients’ survival in our patient cohort.
In conclusion, impact of FCGR2A and -3A genotypes in response to ch14.18/CHO immunotherapy in combination with IL2 was demonstrated. FCGR2A and -3A might therefore provide a prognostic marker when conducting ch14.18/CHO-based immunotherapy.
Genome-wide association studies (GWAS) are used to identify genetic markers linked with at least partially heritable diseases or phenotypes without prior knowledge of any disease-associated genetic loci. In summer 2008, all individuals of the population based cohort Study of Health in Pomerania (SHIP) were individually genotyped using the Affymetrix Genome-Wide Human SNP Array 6.0 microarray. The aim of this work was to establish an efficient workflow for GWAS using the more than 4000 individually genotyped samples of the SHIP cohort as well as pooled samples, focusing exclusively on analyzing genetic variations based on single nucleotide polymorphisms (SNPs). Firstly, an optimal array platform for the genotyping analysis had to be chosen that detected most of the available genetic variants at a high level of accuracy. Secondly, extensive quality controls had to be performed starting from DNA extraction and including tests of the generated array data by the analysis software to obtain the most reliable data for the subsequent association studies. For the identification of loci with smaller genetic influences, individual cohorts were meta-analyzed in large nationally and internationally organized consortia (e.g. CHARGE, BPGen, HaemGen, GIANT, CKD Gen). To participate in those meta-analyses, a comparable common set of genetic data had to be generated. This was done by imputation of the data generated by individual array-based genotyping on the basis of a reference panel using chromosomal linkage information. Due to the extensive phenotype information in the SHIP study, it was possible to perform many genome-wide discovery analyses and replication studies of possible susceptibility loci in a short time once the genetic data was available and processed. This resulted in the necessity to set up an efficient workflow for storing the huge amount of genetic data, converting it into different formats readable for specific analysis software, performing the association analyses and processing the results into a human-readable and clear format. This included replications, GWAS and meta-analyses of several cohorts. Many susceptibility loci were newly identified in different association studies with the SHIP data included and were subsequently published. In this work, genetic association studies with the SHIP data included were performed and published on blood pressure, uric acid concentrations, cardiac structure and function, lipid metabolism, hematological parameters, kidney functions, smoking quantity, circulating IGF-I and IGFBP-3 concentrations and thyroid volume including the risk of goiter development. Besides the SHIP cohort, there was a need to use other, especially patient cohorts for GWAS. Since no genotype information from these patient cohorts was available and the individual genotyping of many probands is still expensive and therefore often not affordable, we established the cost-effective allelotyping method that relied on pooling of DNA samples prior to the hybridization with microarrays. After estimating the pooling-specific error of a case-control allelotyping study, the allelotyping approach was used for identifying genetic susceptibility loci associated with aggressive periodontitis. If not referring to work of collaborators, all statistical analyses, data handling and in silico work concerning the SHIP data described in this context was performed by the author of this dissertation.
Ziel meiner Arbeit war es, die evolutionären Beziehungen innerhalb und zwischen den verschiedenen Arten der Möwen (Laridae) genauer zu untersuchen. Der Großteil der Untersuchungen in dieser Arbeit basiert auf DNA-Sequenzen - mitochondriale Regionen sowie nukleare Intronequenzen. Bei einem molekulare n Ansatz wie in meiner Arbeit ist es von enormer Wichtigkeit, einen umfassenden und nicht zu kleinen Datensatz zu behandeln. Dabei wurde auch darauf geachtet, dass die ausgewählten Sequenzen homolog sind und das Alignment robust ist. Meine Arbeit gliedert sich in sechs Schwerpunkte, auf die ich nun näher eingehen möchte. 1. Phylogenie der Möwen Die vorliegende Arbeit erreichte das gesetzte Ziel einer verbesserten Phylogenierekonstruktion in den Laridae und zeigt deutlich die Mängel der bisherigen molekularer Studien (mit zu wenigen Taxa oder zu kleinen und uninformativen Datensätzen). Sicher bestätigt werden kann in dieser Studie die Unterteilung in eine basale Möwengruppe, bestehend aus sieben Gattungen, sowie der Gattung Larus mit sechs voneinander genetisch differenzierten Gruppen. Eine gute Stützung erfahren alle Gruppen der Larus-Gattung. Schwerer ist aber erwartungsgemäß die genauere Erstellung der Verwandtschaftsbeziehungen der jüngsten Taxa. Zu ihrer Abgrenzung werden weitere Marker benötigt. Entdeckt wurde in der Studie ein Signal (Deletion in den LDH - Sequenzen), das entscheidend zur Bestimmung der Gruppenmitglieder der basalen, nicht-Larus Möwengattungen beiträgt. 2. AFLP-Untersuchung in der Gruppe der Großmöwen Bei der von Vos et al. (1995) entwickelten Methode der AFLP (engl. für amplified fragment length polymorphism)-Analyse ist kein Vorwissen der untersuchten Gen(om)sequenz notwendig. Es gelang mit der AFLP-Untersuchung dieser Arbeit die sieben untersuchten Großmöwentaxa voneinander autosomal zu differenzieren und drei mitochondrial biphyletisch auftretenden Taxa (argentatus, hyperboreus und marinus) zu näher zu charakterisieren. Die Eismöwe (hyperboreus) erhielt ihre Clade 1 - Haplotypen von argentatus-Individuen aus Nordeuropa und die Mantelmöwe (marinus) ihre Clade 2 - Haplotypen von nordamerikanischen Arten, vermutlich smithsonianus. Die europäischen Silbermöwen (argentatus) zeigen beide mitochondrialen Clades in allen untersuchten Kolonien mit einem geographischen Gradienten in deren Verteilung. Hier scheinen Vorläufer der Heringsmöwen ihre Clade 2 Mitochondriengenome in die argentatus-Populationen eingebracht zu haben, die anschließend in einer sekundären Ausbreitungswelle über das vollständige Verbreitungsgebiet verteilt wurden. Autosomal erscheinen sogar vier Genlinien, die auf noch mehr Ausbreitungswellen verweisen. 3. Populationsstudien in Dominikanermöwen (L. dominicanus) Nach einer Publikation von Jiguet (2002) werden bei Dominikanermöwen vier Unterarten unterschieden. Die in dieser Arbeit ermittelten Sequenzen der Gene Cyt b, ND 2 und HVR I zeigen eine klare Differenzierung der untersuchten Kolonien. Die Ursprünge der Dominikanermöwen liegen demnach in Südafrika. Von dort erfolgte die Besiedlung von Argentinien, der Kerguelen-Inseln und der Antarktis in mehreren Ausbreitungswellen. In Chile wurde der südamerikanische Kontinent in einem sehr rezenteren Migrationsereignis zum zweiten Mal kolonisiert. Die dort gefundenen Haplotypen sind den südafrikanischen noch sehr ähnlich. Am jüngsten sind die Populationen Neuseelands und der Chatham-Inseln. 4. Populationsstudie in der Sturmmöwe (L. canus) Ganz anders zeigte sich die genetische Differenzierung für dieselben Gene bei der Sturmmöwe (L. canus) und ihren phänotypisch deutlich unterscheidbaren vier Unterarten. Im mitochondrialen Netzwerk bilden die paläarktischen Taxa canus, heinei und kamtschatschensis eine panmiktische Population. Anders das vierte Taxon brachyrhynchus. Dieses nordamerikanische Taxon unterscheidet sich mitochondrial signifikant von den paläarktischen Individuen. 5. und 6. SNP-Analyse in Großmöwen und Ausblick auf geplante weiterführende Untersuchungen Das Detektieren variabler Nukleotidpositionen (Punktmutationen), die SNPs genannt werden, ist von grundlegender Bedeutung für die weitere Untersuchung der molekularen Evolution. In Rahmen dieser Arbeit wurden 32000 Fragmente mittels der CROPS-Analyse untersucht, dabei wurden in 7400 variablen Fragmenten 11000 SNPs gefunden, 24000 Fragmenten ließen keinerlei genetische Variationen erkennen. Somit zeigt sich in eine Rate von einer variablen Position (SNP) in ~500 Nukleotiden, was mit denen in Säugetieren und Menschen vergleichbar ist. Zukünftig mit diesem umfangreichen Basiswissen eine groß angelegte SNP-Typisierung geplant mit dem Ziel autosomale und sexchromosomale SNPs vergleichend zu analysieren. Des Weiteren können die SNP-Daten auch mit mitochondrialen Daten verglichen werden.
Humanity is plagued by many diseases. Beside environmental influences, many --- if not all --- diseases are also subject to genetic predisposition and then display molecular alterations such as proteomic or metabolic aberrations. The elucidation of the molecular principles underlying human diseases is one of the prime goals of biomedical research. To this end, there has been an advent of large-scale omics profiling studies. While the field of molecular biology has experienced tremendous development, data analysis remains a bottleneck. In the context of this thesis, we developed a number of analysis strategies for different types of omics data resulting from different experimental settings. These include approaches for associations studies for plasma miRNAs and time-resolved plasma omics data. Furthermore, we devised analyses of different RNA-Seq transcriptome profiling studies coping with problems such as lack of replicates or multifactorial experimental design. We also designed machine learning frameworks for the identification of discriminatory biomolecular signatures analysing case-control or time-to-event data. All of the strategies mentioned above were developed and applied in the contexts of multi-disciplinary endeavours. They aided in the identification of plasma miRNAs associated with age, sex, and BMI as well as plasma miRNAs bearing potential as diagnostic biomarkers for non-alcoholic fatty liver disease (NAFLD). This thesis significantly contributed to a study demonstrating the utility of plasma miRNAs as prognostic biomarkers for major cardiovascular events such as ST-elevation myocardial infarction. Our approaches for analysing RNA-Seq data aided in the characterisation of murine models for Alzheimers disease and the transcriptional response of human gingiva fibroblasts to ionizing radiation exposure. Furthermore, the developed approaches were applied for studying a human model for thyrotoxicosis and for the successful identification of a multi-omics plasma biomarker signature of thyroid status. We are only beginning to understand the molecular principles underlying human diseases. The approaches and results presented in this thesis will contribute to improved understanding of biomolecular processes involved in common diseases such as Alzheimers disease, NAFLD, and cardiovascular diseases.