Breaking the code: Statistical methods and methodological issues in psychiatric genetics

by S. Stringer

Institution: Universiteit van Amsterdam
Year: 2015
Record ID: 1252424
Full text PDF: http://hdl.handle.net/11245/1.444819


The genome-wide association (GWA) era has confirmed the heritability of many psychiatric disorders, most notably schizophrenia. Thousands of genetic variants with individually small effect sizes cumulatively constitute a large contribution to the heritability of psychiatric disorders. This thesis consists of three parts. Part I introduces genome-wide and candidate gene approaches currently used to study the effect of common genetic variants in psychiatric disorders. Part II discusses two empirical studies in psychiatric genetics. The first study is a large GWA meta-analysis involving life-time cannabis use. The second study involves a polygenic risk analysis focusing on the genetic overlap between schizophrenia and immune disorders. Finally, part III discusses three types of assumptions that are typically violated in psychiatric genetics, possibly resulting in biased results. For example, in genome-wide studies only one genetic variant is analyzed at a time, implicitly assuming that other genetic variants do not contribute to disease risk or time to disease onset. For complex disorders this assumption is clearly violated, since these disorders are by definition influenced by many genetic variants. In summary, research in psychiatric genetics has revealed large complexity of the genetic architecture of psychiatric disorders, posing enormous statistical challenges. With respect to understanding the genetic basis of psychiatric disorders we are only at the very beginning of a long but exciting journey.