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Systems genetics analysis of cancer susceptibility: from mouse models to humans

Nature Reviews Genetics volume 10, pages 651–657 (2009)Cite this article

Abstract

Genetic studies of cancer susceptibility have shown that most heritable risk cannot be explained by the main effects of common alleles. This may be due to unknown gene–gene or gene–environment interactions and the complex roles of many genes at different stages of cancer. Studies using mouse models of cancer suggest that methods that integrate genetic analysis and genomic networks with knowledge of cancer biology can help to extend our understanding of heritable cancer susceptibility.

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Figure 1: Application of systems genetics to cancer susceptibility.
Figure 2: Using systems genetics to identify candidate loci and genes.
Figure 3: Germline and environmental effects identified through systems genetics.

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Acknowledgements

This work was supported by the National Cancer Institute (grant U01 CA84244) and the US Department of Energy (DE-FG02-03ER63630). A.B. acknowledges support from the Barbara Bass Bakar Chair of Cancer Genetics. We thank members of the Balmain laboratory, R. Akhurst and H. Quigley for their comments.

Author information

Authors and Affiliations

  1. David Quigley and Allan Balmain are at the Helen Diller Family Comprehensive Cancer Center, University of California San Francisco, San Francisco, California 94115, USA.,

    David Quigley & Allan Balmain

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  1. David Quigley
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  2. Allan Balmain
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Corresponding author

Correspondence to Allan Balmain.

Related links

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FURTHER INFORMATION

Balmain Laboratory web page

WebQTL

Jackson Laboratory Mouse Genome Informatics resources

Glossary

Comparative genomic hybridization

A method of identifying changes in DNA copy number that compares a test genome with a reference genome. Frequently used in cancer research to identify genomic amplifications and deletions associated with tumour activity.

Congenic

A strain that is produced by a breeding strategy in which recombinants between two inbred strains are backcrossed to produce a strain that carries a single genomic segment from one strain on the genetic background of the other.

Expression QTL

(eQTL). A QTL for which the trait under control is gene expression. An eQTL may affect a gene near the locus (acting in cis) or it may act at a distance (acting in trans).

Heuristic algorithm

An algorithm that produces an approximate solution to a problem that is not guaranteed to be correct in the strict sense. Useful for problems for which no exact solution can be found in a reasonable period of time.

MicroRNA

A form of ssRNA typically 2025 nucleotides long that is involved in regulating the expression of other genes, either through inhibiting protein translation or degrading a target mRNA transcript through a process that is similar to RNAi.

Mutual information

An information-theoretic measurement of the dependence between two random variables. The more knowledge one variables value conveys about the other variables value, the greater the mutual information.

Network

A set of nodes and a set of edges that connect those nodes, formally called a graph. Many problems can be represented using graphs and there is a large mathematical and computer science literature dealing with their properties.

Probabilistic graphic model

A directed acyclic graph in which edges represent probabilistic assertions about relationships between nodes. Edge relationships can be inferred from available data using algorithms that discover the most likely relationships.

QTL

A genomic locus where allelic variation is associated with the value of a continuously varying trait.

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Quigley, D., Balmain, A. Systems genetics analysis of cancer susceptibility: from mouse models to humans. Nat Rev Genet 10, 651–657 (2009). https://doi.org/10.1038/nrg2617

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