Modeling causality for pairs of phenotypes in system genetics

Elias Chaibub Neto, Aimee T. Broman, Mark P. Keller, Alan D. Attie, Bin Zhang, Jun Zhu, Brian S. Yandell

Research output: Contribution to journalArticlepeer-review

29 Scopus citations


Current efforts in systems genetics have focused on the development of statistical approaches that aim to disentangle causal relationships among molecular phenotypes in segregating populations. Reverse engineering of transcriptional networks plays a key role in the understanding of gene regulation. However, transcriptional regulation is only one possible mechanism, as methylation, phosphorylation, direct protein-protein interaction, transcription factor binding, etc., can also contribute to gene regulation. These additional modes of regulation can be interpreted as unobserved variables in the transcriptional gene network and can potentially affect its reconstruction accuracy. We develop tests of causal direction for a pair of phenotypes that may be embedded in a more complicated but unobserved network by extending Vuong's selection tests for misspecified models. Our tests provide a significance level, which is unavailable for the widely used AIC and BIC criteria. We evaluate the performance of our tests against the AIC, BIC, and a recently published causality inference test in simulation studies. We compare the precision of causal calls using biologically validated causal relationships extracted from a database of 247 knockout experiments in yeast. Our model selection tests are more precise, showing greatly reduced false-positive rates compared to the alternative approaches. In practice, this is a useful feature since follow-up studies tend to be time consuming and expensive and, hence, it is important for the experimentalist to have causal predictions with low false-positive rates.

Original languageEnglish
Pages (from-to)1003-1013
Number of pages11
Issue number3
StatePublished - 2013
Externally publishedYes


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