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  • Review Article
  • Published:

Network propagation: a universal amplifier of genetic associations

Nature Reviews Genetics volume 18, pages 551–562 (2017)Cite this article

Subjects

Key Points

  • Network propagation transforms a short list of candidate genes into a genome-wide profile of gene scores that are based on proximity to candidates in a gene network.

  • This transformation greatly improves the power of genetic association, providing a universal amplifier for genetic analysis.

  • Mathematically, the technique of network propagation is simplifying and unifying.

  • Network propagation methods can be used to identify genes and genetic modules that underlie human disease.

Abstract

Biological networks are powerful resources for the discovery of genes and genetic modules that drive disease. Fundamental to network analysis is the concept that genes underlying the same phenotype tend to interact; this principle can be used to combine and to amplify signals from individual genes. Recently, numerous bioinformatic techniques have been proposed for genetic analysis using networks, based on random walks, information diffusion and electrical resistance. These approaches have been applied successfully to identify disease genes, genetic modules and drug targets. In fact, all these approaches are variations of a unifying mathematical machinery — network propagation — suggesting that it is a powerful data transformation method of broad utility in genetic research.

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Figure 1: Schematic illustration of network propagation.
Figure 2: Network propagation for discovery and prioritization of disease genes.
Figure 3: Overview of approaches that use network propagation.
Figure 4: Applications of network propagation to analyse cancer data.

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Acknowledgements

The authors gratefully acknowledge J. Huang and M. Ruffalo for assistance with figures for this manuscript. They also thank E. Eisenberg for assistance with references for this manuscript. This work was initiated while the authors attended a Network Biology workshop as part of a semester on Algorithmic Challenges in Genomics at the Simons Institute for the Theory of Computing at University of California, Berkeley, USA.

Author information

Authors and Affiliations

  1. Department of Computer Science, Tufts University, Medford, 02155, Massachusetts, USA

    Lenore Cowen

  2. University of California San Diego, La Jolla, 92093, California, USA

    Trey Ideker

  3. Department of Computer Science, Princeton University, Princeton, 08540, New Jersey, USA

    Benjamin J. Raphael

  4. Blavatnik School of Computer Science, Tel Aviv University, Tel Aviv, 69978, Israel

    Roded Sharan

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  1. Lenore Cowen
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  2. Trey Ideker
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  4. Roded Sharan
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Correspondence to Roded Sharan.

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Competing interests

B.J.R. is a founder of Medley Genomics. The other authors declare they have no competing interests.

PowerPoint slides

Glossary

Nodes

The objects modelled by a network. In biological networks, nodes can represent proteins, genes, metabolites, RNA molecules, or even diseases and phenotypes.

Edges

Relationships between pairs of nodes in a network, for example, molecular interactions between the genes or proteins that correspond to these nodes. Two nodes sharing an edge are said to be adjacent, neighbours, or directly connected by it.

Network propagation

A family of stochastic processes that trace the flow of information through a network over time.

Random walks

Mathematical formalization of the paths resulting from taking successive random steps. Classical examples of random walks are Brownian motion, the fortune of a gambler flipping a coin or fluctuations of the stock market. In the context of networks, a random walk typically describes a process in which a 'walker' moves from one node to another with a probability that is proportional to the weight of the edge connecting the nodes.

Kernels

Symmetric similarity functions with the property that one can assign vectors (in some abstract space) to its arguments such that the similarity of two elements is the dot-product between their corresponding vectors.

Disease module

A network module, the member genes of which are associated with a particular disease.

False positives

Error in prediction whereby negative examples are predicted to be positive. For example, when predicting disease genes, a false positive would correspond to a non-disease gene that is wrongly predicted to be disease-related.

False negatives

Error in prediction whereby positive examples are predicted to be negative. For example, when predicting disease genes, a false negative would correspond to a disease gene that is missed and predicted to be unrelated.

Edge weight

An abstract measure of the 'strength' of the connection between a pair of nodes in a network, typically represented as a real number between 0 and 1.

Adjacency matrix

A matrix representation of a network such that the (i,j) entry denotes whether nodes i and j are adjacent (in which case its value is 1) or not (value 0).

Orthology

The evolutionary relationship between two genes in two species that have descended from a common ancestor.

Classifier

A machine-learning algorithm that predicts the class of a sample given some characteristics of it. For example, a classifier can aim to distinguish between disease and non-disease genes based on their network proximity to known disease or non-disease genes.

Network modules

Regions of a network with some topological property; for example, a set of nodes that densely interact with one another.

Node degree

The number of other nodes that are adjacent (that is, directly connected) to a node.

Similarity matrix

A matrix with rows and columns that represent the same set of objects such that the (i,j) entry denotes some similarity measure (for example, as obtained from network propagation) between the corresponding elements.

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Cowen, L., Ideker, T., Raphael, B. et al. Network propagation: a universal amplifier of genetic associations. Nat Rev Genet 18, 551–562 (2017). https://doi.org/10.1038/nrg.2017.38

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