@phdthesis{Teumer2011,
  author    = {Alexander Teumer},
  title     = {Microarray-based Genome-Wide Association Studies (GWAS) using data generated by Allelotyping and by individual Genotyping},
  journal    = {Microarray-basierte genomweite Assoziationsstudien (GWAS) unter Verwendung von Daten erzeugt durch Allelotypisierung und individuelle Genotypisierung},
  url       = {https://nbn-resolving.org/urn:nbn:de:gbv:9-000898-3},
  year      = {2011},
  abstract  = {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.},
  language  = {en}
}