While genetic variants are known to be associated with overall gene abundance in stimulated immune cells, less is known about their effects on alternative isoform usage. By analyzing RNA-seq profiles of monocyte-derived dendritic cells from 243 individuals, we uncovered thousands of unannotated isoforms synthesized in response to influenza infection and type 1 interferon stimulation. We identified more than a thousand quantitative trait loci (QTLs) associated with alternate isoform usage (isoQTLs), many of which are independent of expression QTLs (eQTLs) for the same gene. Compared with eQTLs, isoQTLs are enriched for splice sites and untranslated regions, but depleted of sequences upstream of annotated transcription start sites. Both eQTLs and isoQTLs explain a significant proportion of the disease heritability attributed to common genetic variants. At the ERAP2 locus, we shed light on the function of the gene and how two frequent, highly differentiated haplotypes with intermediate frequencies could be maintained by balancing selection. At baseline and following type 1 interferon stimulation, the major haplotype is associated with low ERAP2 expression caused by nonsense-mediated decay, while the minor haplotype, known to increase Crohn's disease risk, is associated with high ERAP2 expression. In response to influenza infection, we found two uncharacterized isoforms expressed from the major haplotype, likely the result of multiple perfectly linked variants affecting the transcription and splicing at the locus. Thus, genetic variants at a single locus could modulate independent gene regulatory processes in innate immune responses and, in the case of ERAP2, may confer a historical fitness advantage in response to virus.

The evolutionary history of a gene helps predict its function and relationship to phenotypic traits. Although sequence conservation is commonly used to decipher gene function and assess medical relevance, methods for functional inference from comparative expression data are lacking. Here, we use RNA-seq across seven tissues from 17 mammalian species to show that expression evolution across mammals is accurately modeled by the Ornstein-Uhlenbeck process, a commonly proposed model of continuous trait evolution. We apply this model to identify expression pathways under neutral, stabilizing, and directional selection. We further demonstrate novel applications of this model to quantify the extent of stabilizing selection on a gene's expression, parameterize the distribution of each gene's optimal expression level, and detect deleterious expression levels in expression data from individual patients. Our work provides a statistical framework for interpreting expression data across species and in disease.