A Bayesian Framework to Account for Complex Non-Genetic Factors in Gene Expression Levels Greatly Increases Power in eQTL Studies
Figure 3
Sensitivity of recovering simulated hidden factor effects and eQTLs for Bayesian and non-Bayesian methods.
(a) Mean-squared error in estimating only the hidden factor contribution. Methods that do not explicitly retain the genetic factors explain them away as hidden global factors, resulting in high error comparable to not accounting for hidden factors at all (Standard). (b) Mean-squared error in estimating the contribution from hidden and genetic factors. (c) Sensitivity of recovering immediate SNP associations. (d) Sensitivity of recovering downstream associations. Seven hidden factors and three transcription factor effects were simulated. For eQTL sensitivity, standard eQTL finding on simulated data (Standard) and same data without the hidden effects (Ideal) are included as comparisons. PCAsig and SVA identified a constant number of hidden components (marked with a diamond shape), thus only a single result (dashed line) is given.