Discovering genetic interactions based on natural genetic variation

Complex traits can be attributed to the effect of two or more genes and their interaction with each other as well as the environment. Unraveling the genetic cause of these traits, especially with regard to disease etiology, is a major goal of current research in statistical genetics. Much effort has been invested in the development of methods detecting genetic loci that are linked to variation of disease traits or intermediate molecular phenotypes such as gene expression levels. A very important aspect to be considered in the modeling of genotype-phenotype associations is that genes often interact with each other in a non-additive fashion, a phenomenon called epistasis. A special case of an epistatic interaction is an allele incompatibility, which is characterized by the inviability of all individuals carrying a certain combination of alleles at two distinct loci in the genome. The relevance and distribution of allele incompatibilities has not been investigated on a genome-wide scale in mammals. In this thesis, I propose a method for inferring allele incompatibilities that is exclusively based on DNA sequence information. We make use of genome-wide SNP data of parent-child trios and inspect 3×3 contingency tables for detecting pairs of alleles from different genomic positions that are under-represented in the population. Our method detected substantially more imbalanced allele pairs than what we got in simulations assuming no interactions. We could validate a significant number of the interactions with external data and we found that interacting loci are enriched for genes involved in developmental processes. Genes do not only interact with one another, their regulatory activity also depends on the environment or cellular context. The impact of genetic variation on gene expression will therefore also depend on cell types or on the cellular state. This aspect has long been neglected in the inference of genetic loci that are linked to gene expression variation (expression quantitative trait loci, eQTL). There is thus a need to develop methods for analyzing the variation of eQTL between different cell types and to assess the impact of genetic variation on expression dynamics rather than just static expression levels. In the second part of this thesis, I show that defining and detecting eQTL regulating expression dynamics is non-trivial. I propose to distinguish “static”, "conditional” and “dynamic” eQTL and suggest new strategies for mapping these eQTL classes. By using murine mRNA expression data from four stages of hematopoiesis, we demonstrate that eQTL from the above three classes yield associations with different modes of expression regulation. Intriguingly, dynamic and conditional eQTL complement one another although they are based on integration of the same expression data. We reveal substantial effects of individual genetic variation on cell state specific expression regulation