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Genetics. 2011 Apr;187(4):1163-70. doi: 10.1534/genetics.110.123273. Epub 2011 Jan 17.

A Bayesian framework for inference of the genotype-phenotype map for segregating populations.

Author information

1
The Jackson Laboratory, Bar Harbor, Maine 04609, USA.

Abstract

Complex genetic interactions lie at the foundation of many diseases. Understanding the nature of these interactions is critical to developing rational intervention strategies. In mammalian systems hypothesis testing in vivo is expensive, time consuming, and often restricted to a few physiological endpoints. Thus, computational methods that generate causal hypotheses can help to prioritize targets for experimental intervention. We propose a Bayesian statistical method to infer networks of causal relationships among genotypes and phenotypes using expression quantitative trait loci (eQTL) data from genetically randomized populations. Causal relationships between network variables are described with hierarchical regression models. Prior distributions on the network structure enforce graph sparsity and have the potential to encode prior biological knowledge about the network. An efficient Monte Carlo method is used to search across the model space and sample highly probable networks. The result is an ensemble of networks that provide a measure of confidence in the estimated network topology. These networks can be used to make predictions of system-wide response to perturbations. We applied our method to kidney gene expression data from an MRL/MpJ × SM/J intercross population and predicted a previously uncharacterized feedback loop in the local renin-angiotensin system.

PMID:
21242536
PMCID:
PMC3070524
DOI:
10.1534/genetics.110.123273
[Indexed for MEDLINE]
Free PMC Article

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