Figure 2 illustrates how QTL information over multiple gene expression traits (or any other molecular phenotype) can be used to order a given gene expression trait with respect to other gene expression traits.
Such patterns capture multiple related single nucleotide polymorphisms (SNPs) that jointly influence multiple-related expression traits.
The p-values were corrected for multiple testing across all expression traits and positions using Benjamini and Hochberg procedure [ 21].
An ontology analysis using the Panther system demonstrated that, by reference to the list of 12,808 genes expressed in monocytes, the set of 465 expression traits affected by multiple sources of variability was enriched in "Immunity and defense" genes (69 obsexpected.8 exPected, P = 4.5×10−6), especially in the sub-categories of "Macrophage-mediated immunity" (18/3.6, P = 7.5×10−6).
The preceding analyses revealed that each risk factor was associated with a large number of expression traits, thus emphasizing the multiple inter-relations existing between the transcriptomic and risk factor profiles of an individual.
Testing for putative QTL positions in 470 expression traits produced a lot of nominal P-values that required multiple test corrections.
Thus far, eQTL studies have analyzed gene expression traits measured primarily by DNA microarrays in liver [ 9, 14– 16], multiple blood cell types [ 17– 27], brain regions [ 24, 28– 31], endothelial cells [ 32], stomach [ 9], skin [ 33], and adipose [ 9, 19].
We initially explored marginal associations between genotypes and phenotypic and expression traits through whole-genome scans, and then, in genomic regions with multiple significant hits, we assessed gene-phenotype network reconstruction using causal structural learning algorithms.
As this relies on a pair-wise test between each single feature and each single gene, it is not capable of modeling the synergistic effect from multiple features to an expression trait.
If multiple pathways existed for any expression trait, all were tested and the minimum P value was used as the representative P value.
