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

Probabilistic Latent Variable Models in Statistical Genomics

In this thesis, we propose different probabilistic latent variable mod- els to identify and capture the hidden structure present in commonly studied genomics datasets. We start by investigating how to cor- rect for unwanted correlations due to hidden confounding factors in gene expression data. This is particularly important in expression quantitative trait loci (eQTL) studies, where the goal is to identify associations between genetic variants and gene expression levels. We start with a na¨ ıve approach, which estimates the latent factors from the gene expression data alone, ignoring the genetics, and we show that it leads to a loss of signal in the data. We then highlight how, thanks to the formulation of our model as a probabilistic model, it is straightforward to modify it in order to take into account the specific properties of the data. In particular, we show that in the na¨ ıve ap- proach the latent variables ”explain away” the genetic signal, and that this problem can be avoided by jointly inferring these latent variables while taking into account the genetic information. We then extend this, so far additive, model to additionally detect interactions between the latent variables and the genetic markers. We show that this leads to a better reconstruction of the latent space and that it helps dis- secting latent variables capturing general confounding factors (such as batch effects) from those capturing environmental factors involved in genotype-by-environment interactions. Finally, we investigate the effects of misspecifications of the noise model in genetic studies, show- ing how the probabilistic framework presented so far can be easily ex- tended to automatically infer non-linear monotonic transformations of the data such that the common assumption of Gaussian distributed residuals is respected

Gaussian Processes for Big Data

We introduce stochastic variational inference for Gaussian process models. This enables the application of Gaussian process (GP) models to data sets containing millions of data points. We show how GPs can be vari- ationally decomposed to depend on a set of globally relevant inducing variables which factorize the model in the necessary manner to perform variational inference. Our ap- proach is readily extended to models with non-Gaussian likelihoods and latent variable models based around Gaussian processes. We demonstrate the approach on a simple toy problem and two real world data sets.Comment: Appears in Proceedings of the Twenty-Ninth Conference on Uncertainty in Artificial Intelligence (UAI2013