Limited statistical evidence for shared genetic effects of eQTLs and autoimmune-disease-associated loci in three major immune-cell types

Abstract

Most autoimmune-disease-risk effects identified by genome-wide association studies (GWAS) localize to open chromatin with gene-regulatory activity. GWAS loci are also enriched in expression quantitative trait loci (eQTLs), thus suggesting that most risk variants alter gene expression. However, because causal variants are difficult to identify, and cis-eQTLs occur frequently, it remains challenging to identify specific instances of disease-relevant changes to gene regulation. Here, we used a novel joint likelihood framework with higher resolution than that of previous methods to identify loci where autoimmune-disease risk and an eQTL are driven by a single shared genetic effect. Using eQTLs from three major immune subpopulations, we found shared effects in only ∼25% of the loci examined. Thus, we show that a fraction of gene-regulatory changes suggest strong mechanistic hypotheses for disease risk, but we conclude that most risk mechanisms are not likely to involve changes in basal gene expression.

Figure 1. Only a minority of disease associations share genetic effects with eQTLs across three immune cell subpopulations

(a) We find strong evidence that approximately 75% of eQTLs are driven by distinct genetic effects (orange) to 260 disease risk associations across 154 ImmunoChip regions. The proportion of shared effects (green) we are able to detect is less than 25%, even for relatively strong eQTLs with nominal association p < 10⁻⁵. We find no compelling evidence for either shared or distinct associations for a small proportion of disease-eQTL pairs (gray). (b) The median number of loci with at least one shared effect eQTL in any cell type (blue line) at more liberal significance thresholds remains constant after false positive adjustment, further supporting this conclusion. The shaded area represents the lower and upper expectation bounds for disease-eQTL pairs driven by the same causal variant. Only 31–47% of multiple sclerosis associations and 30–45% of inflammatory bowel disease associations are consistent with eQTL effects. Equivalent data for the other diseases are presented in Supplementary Figure 19.

Figure 2. A multiple sclerosis association on chromosome 12 is consistent with eQTLs for METTL21B in both CD4⁺ T cells and CD14⁺ monocytes

(a) A genome-wide significant association to multiple sclerosis risk (upper panel; shading denotes strength of LD to the most associated variant rs10783847). This association is consistent with eQTLs for METTL21B in CD4⁺ T cells (middle panel) and CD14⁺ monocytes (lower panel, both shaded by LD to rs10783847), but not to eQTL data for any other genes in the region (upper gene track: black boxes denote 31 genes with eQTL data available in addition to METTL21B (red); gray denotes genes which are not reliably detected in our data or do not have eQTL p < 0.01 in the region). (b) Joint likelihood p-values for 32 candidate genes analyzed for this MS association peak in three cell types. Those with FDR < 5% are shown in red. (c) Association p-values for MS risk (x-axis) and eQTLs (y-axis) are strongly correlated for both CD4⁺ T cells (middle panel) and CD14⁺ monocytes (lower panel). (d) Similarly, eQTL association Z statistics scale linearly with LD (r, × axis) to rs10783847, consistent with a model of a single causal variant driving both disease association and eQTL.

Figure 3. Associations to multiple sclerosis, Crohn disease and rheumatoid arthritis (RA) on chromosome 5 are consistent with an eQTL for ANKRD55 in CD4⁺ T cells

(a) Genome-wide significant associations to all three diseases (upper panels) and eQTL data for ANKRD55 (lower panel; shading in all panels proportional to LD to the most associated variant rs71624119). Due to the variable density of ImmunoChip data, the analysis window is small and only overlaps the coding region of ANKRD55, though we test eQTLs for five genes with a transcriptional start site within 1Mb of the the association. (b) Joint likelihood p-values for five candidate genes analyzed for this locus in CD4⁺ T cells. Those with FDR < 5% are shown in red. (c) Association p-values for each disease (x axis) are strongly correlated to those for the ANKRD55 eQTL in CD4⁺ cells (y axis). (d) Similarly, eQTL association Z statistics scale linearly with LD (r, × axis) to rs71624119 for all three diseases, consistent with a model of a single causal variant driving all disease associations and the eQTL.