Inhalt:
In a genome-wide association study (GWAS), a large number of SNPs are genotyped in a large number of cases (with disease) and controls (without disease) using commercially available high-throughput genotyping platforms. In a GWAS, the genotype data collected in cases and unaffected, population-matched controls are compared. Other than identifying individuals with the disease, there is no requirement for prior biological knowledge of the trait under investigation. This analysis approach has discovered several thousand genetic loci reliably associated to a range of complex phenotypes and diseases. Psychiatric diseases are characterized by affective, behavioral, and cognitive abnormalities. The majority of psychiatric diseases share a common feature, namely that family history of disease is a major risk factor, which is suggestive of a strong genetic component to the underlying disease risk. Epidemiological studies have estimated high heritability values of up to 80% for many common psychiatric diseases, such as schizophrenia and bipolar disorder. Psychiatric research to date has uncovered little about the underlying biology of disease. Commonly used tools in biomedical research, such as suitable in vitro and in vivo model systems, are essentially absent. This makes GWAS the perfect tool for gaining more fundamental insight into the etiology of psychiatric diseases. Moreover, large-scale genotyping or whole-genome sequencing has become more affordable by today's standards. Indeed it is now feasible to analyze tens of thousands of individuals. This is a crucial requirement because large data sets are necessary for a successful GWAS of complex diseases as the apparent effect sizes are small. In this thesis I have described initial results from GWAS for a range of psychiatric diseases. I have identified here which components have been critical for its success and discuss how we learned from both failures and successes. By far, it has become clear that the most important step to scientific progress in human genetics of complex traits is collaboration and exchange of data. In a world where data (in this case genotype data) becomes available in enormous amounts, and where computer power increases significantly via computer clusters and cloud computing, it has been essential to bring everybody together to produce robust findings. In the first half of this thesis (chapter 2-6) I presented GWAS for three single psychiatric diseases: major depressive disorder, bipolar disorder and schizophrenia. The most recent GWAS for schizophrenia, presented in Chapter 6 builds the central piece of this thesis. Here we could demonstrate the remarkable success via extensive collaboration. The second half (chapters 7-9) concentrated on endophenotypes and cross-phenotypes with common variant analysis, where we attempted to dissect commonalities and differences between the classic psychiatric nosologies. In summary the analyses of common variants in psychiatric genetics is the main contributor to a huge scientific success that is in the process of changing the field with many robust genetic findings. It will continue to be a valuable source of information to the biology in psychiatry for the decades to come.
Anmerkung:
Dissertation 2014
Sprache:
Englisch
