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Accurate modeling of confounding variation in eQTL studies leads to a great increase in power to detect trans-regulatory effects


Expression quantitative trait loci (eQTL) studies are an integral tool to investigate the genetic component of gene expression variation. A major challenge in the analysis of such studies are hidden confounding factors, such as unobserved covariates or unknown environmental influences. These factors can induce a pronounced artifactual correlation structure in the expression profiles, which may create spurious false associations or mask real genetic association signals.Here, we report PANAMA (Probabilistic ANAlysis of genoMic dAta), a novel probabilistic model to account for confounding factors within an eQTL analysis. In contrast to previous methods, PANAMA learns hidden factors jointly with the effect of prominent genetic regulators. As a result, PANAMA can more accurately distinguish between true genetic association signals and confounding variation.We applied our model and compared it to existing methods on a variety of datasets and biological systems. PANAMA consistently performs better than alternative methods, and finds in particular substantially more trans regulators. Importantly, PANAMA not only identified a greater number of associations, but also yields hits that are biologically more plausible and can be better reproduced between independent studies.

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Correspondence to Nicolo Fusi.

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Fusi, N., Stegle, O. & Lawrence, N. Accurate modeling of confounding variation in eQTL studies leads to a great increase in power to detect trans-regulatory effects. Nat Prec (2011).

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  • QTL
  • Expression profiling
  • Quantitative trait loci


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