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A framework for integrating directed and undirected annotations to build explanatory models of cis-eQTL data

Fig 4

Directed annotations partially explain gene expression variance in GTEx.

The BAGEA model was fit using various GTEx eQTL data (supplemented with GEAUVADIS eQTL data) and with ExPecto-derived directed annotations on genes in the trainig set (chr1,‥,chr15) with a top nominal p-value<10⁻⁷. ExPecto includes 2002 total annotations, of which either 1187 histone and DHS annotations from Roadmap (Roadmap) or 690 non-histone ChIP-Seq from ENCODE (TF) were used. For the Roadmap annotation set we enforced structure on the priors of ω by using the meta-annotations available for cell type and assay type, (group-lasso), while for the (TF annotation set, each ω_i parameter be controlled by its individual υ_i parameter (lasso). For each gene j in the test set (chr16,‥,chr22 and top nominal p-value< 10⁻⁷), we calculated an approximate version of S_j, the squared magnitude of the directed predictor , where the approximation uses external LD information. Further, we calculated an approximate version of , the mean squared error (MSE) when predicting gene expression y_j from . (A) Displayed is the average (approximated) across all genes for each GTEx experiment, and annotation subset. 95% Confidence intervals are computed by bootstrap sampling. (B) For each GTEx experiment and annotation subset, we sorted results by predictor size S_j and and averaged within the top quartile. Displayed is the relationship between the MSE of the predictor and its mean squared magnitude S_j. Averaged S_j, top quartile : The mean value of the directed predictor size S_j in the top quartile on the horizontal axis; Averaged Directed MSE (): The averaged of genes falling into the top quartile in terms of S_j on the vertical axis. The 95% confidence interval for each window was derived by bootstrap sampling. We see that the average squared magnitude S_j is of similar size as the gains in directed MSE suggesting that the BAGEA does not substantially overfit.

doi: https://doi.org/10.1371/journal.pcbi.1007770.g004