Book contents
- Frontmatter
- Contents
- List of contributors
- 1 An introduction to systems genetics
- 2 Computational paradigms for analyzing genetic interaction networks
- 3 Mapping genetic interactions across many phenotypes in metazoan cells
- 4 Genetic interactions and network reliability
- 5 Synthetic lethality and chemoresistance in cancer
- 6 Joining the dots: network analysis of gene perturbation data
- 7 High-content screening in infectious diseases: new drugs against bugs
- 8 Inferring genetic architecture from systems genetics studies
- 9 Bayesian inference for model selection: an application to aberrant signalling pathways in chronic myeloid leukaemia
- 10 Dynamic network models of protein complexes
- 11 Phenotype state spaces and strategies for exploring them
- 12 Automated behavioural fingerprinting of Caenorhabditis elegans mutants
- Index
- Plate Section
- References
3 - Mapping genetic interactions across many phenotypes in metazoan cells
Published online by Cambridge University Press: 05 July 2015
- Frontmatter
- Contents
- List of contributors
- 1 An introduction to systems genetics
- 2 Computational paradigms for analyzing genetic interaction networks
- 3 Mapping genetic interactions across many phenotypes in metazoan cells
- 4 Genetic interactions and network reliability
- 5 Synthetic lethality and chemoresistance in cancer
- 6 Joining the dots: network analysis of gene perturbation data
- 7 High-content screening in infectious diseases: new drugs against bugs
- 8 Inferring genetic architecture from systems genetics studies
- 9 Bayesian inference for model selection: an application to aberrant signalling pathways in chronic myeloid leukaemia
- 10 Dynamic network models of protein complexes
- 11 Phenotype state spaces and strategies for exploring them
- 12 Automated behavioural fingerprinting of Caenorhabditis elegans mutants
- Index
- Plate Section
- References
Summary
Interactions between genes can be experimentally determined by combining multiple mutations and identifying combinations where the resulting phenotype differs from the expected one. Such genetic interactions, for example measured in yeast for cell proliferation and growth phenotypes, provided intricate insights into the genetic architecture and interplay of pathways. Due to the lack of comprehensive deletion libraries, similar experiments in higher eukaryotic cells have been challenging. Recently, we and others described methods to perform systematic, comprehensive double-perturbation analyses in Drosophila and human cells using RNA interference. We also introduced methods to use multiple phenotypes to map genetic interactions across a broad spectrum of processes.
This chapter focuses on the systematic mapping of genetic interactions and the use of image-based phenotypes to improve genetic interaction calling. It also describes experimental approaches for the analysis of genetic interactions in human cells and discusses concepts to expand genetic interaction mapping towards a genomic scale.
A short history of genetic interaction analysis
Using quantitative traits to map genetic interactions has a long tradition in Drosophila. In the 1960s and 1970s, Dobzhansky, Rendel, and others used externally visible pheno-types or overall fitness to study non-mendelian inheritance and dissect the heritability of complex traits (Fig. 3.1a). One of the underlying assumptions was that genetic loci in the Drosophila genome interact to shape complex phenotypes or buffer detrimental alleles.
In 1965, Dobzhansky and colleagues analyzed epistatic interactions between the components of genetic variants in Drosophila. They crossed flies carrying mutant alleles into a wild-type background obtained from a natural habitat and found that the combination of particular chromosomes showed synthetic sick phenotypes, whereas both chromosomes alone did not. This for the first time demonstrated the presence of bi-chromosomal synthetic interactions in Drosophila populations. Similarly, Rendel and colleagues demonstrated the existence of epistatic modifiers in Drosophila by analysis of scute alleles, which reduce the number of scutellar bristles on the dorsal thorax from four to an average of one.
- Type
- Chapter
- Information
- Systems GeneticsLinking Genotypes and Phenotypes, pp. 36 - 50Publisher: Cambridge University PressPrint publication year: 2015