
==== Front
iScience
iScience
iScience
2589-0042
Elsevier

S2589-0042(24)01940-0
10.1016/j.isci.2024.110715
110715
Article
Genetic association with autoimmune diseases identifies molecular mechanisms of coronary artery disease
Kerns Sophia sophia.kerns@ampelbiosolutions.com
124∗
Owen Katherine A. 12
Daamen Andrea 12
Kain Jessica 123
Grammer Amrie C. 12
Lipsky Peter E. 12
1 AMPEL Biosolutions, LLC, Charlottesville, VA 22903, USA
2 The RILITE Research Institute, Charlottesville, VA 22903, USA
3 Stanford University Department of Genetics, Stanford, CA 94305, USA
∗ Corresponding author sophia.kerns@ampelbiosolutions.com
4 Lead contact

13 8 2024
20 9 2024
13 8 2024
27 9 1107154 6 2024
28 6 2024
8 8 2024
© 2024 The Authors. Published by Elsevier Inc.
2024

https://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Summary

Autoimmune patients have a significantly increased risk of developing coronary artery disease (CAD) compared to the general population. However, autoimmune patients often lack traditional risk factors for CAD and there is increasing recognition of inflammation in CAD development. In this study, we leveraged genome-wide association study (GWAS) data to understand whether there is a genetic relationship between CAD and autoimmunity. Statistical genetic comparison methods were used to identify correlated and causal SNPs between various autoimmune diseases and CAD. Pleiotropic SNPs were identified by cross-phenotype association analysis (CPASSOC) and overlap between GWAS. Causal SNPs were identified using Mendelian Randomization (MR) and Colocalization (COLOC). Using SNP-to-gene mapping, we additionally identified pleiotropic and causal genes and pathways associated between autoimmunity and CAD, which were contextualized by documentation of enrichment in individual cell types identified from coronary atherosclerotic plaques by single-cell RNA sequencing. These results provide insight into potential inflammatory therapeutic targets for CAD.

Graphical abstract

Highlights

• CAD/Autoimmunity share correlated and causal genetic variants

• Variants identify inflammatory pathways likely to contribute to CAD pathogenesis

• Pathway analysis identifies potential inflammatory therapeutic targets for CAD

• CAD/Autoimmunity-predicted genes are overexpressed in cells in human CAD lesions

Immunology; Clinical genetics; Cardiovascular medicine

Subject areas

Immunology
Clinical genetics
Cardiovascular medicine
Published: August 13, 2024
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pmcIntroduction

Worldwide, 3–5% of people have an autoimmune inflammatory disease (AID), and the prevalence of autoimmunity is rising.1,2,3 In general, AIDs are characterized by autoreactivity to specific self-antigens. Each AID, including rheumatoid arthritis (RA), systemic lupus erythematosus (SLE), psoriasis (PSO), type 1 diabetes (T1D), celiac disease (CeD), and primary biliary cholangitis (PBC), presents with a distinct array of clinical and immunologic manifestations. Nonetheless, all exhibit a high degree of genetic correlation with each other, manifested by the observation that one or more AIDs can occur in the same individual or in closely related family members, indicating some underlying shared pathogenic mechanisms across diseases.4

AID patients have a significantly higher risk of cardiovascular disease (CVD) than the general population. This risk is most apparent in SLE and T1D, which tend to affect younger persons.5,6 CVD can occur in AID in the absence of traditional risk factors, such as age, smoking, dyslipidemia and hypertension.7,8,9,10,11 The burden is so great in autoimmune patients that CVD accounts for up to 25% of mortality of SLE patients12 and between 30 and 50% of deaths of RA patients.13 Severe PSO has also been shown to increase the risk of CVD,13 and T1D patients under the age of 45 are five times more likely than the general population to experience a CVD event.14

The most prevalent underlying cardiovascular-related manifestation in AID patients is accelerated atherosclerosis. It has been estimated that up to 40% of SLE patients have coronary atherosclerosis despite being largely young women lacking traditional risk factors for CVD.15,16 Growing evidence indicates that atherosclerosis has a significant inflammatory component, supporting the idea that atherosclerosis and AID could share risk factors.17 An understanding of the pathogenesis of coronary artery disease (CAD) in AID is, therefore, critical to identifying both pathogenic mechanisms and treatment targets to reduce CAD-related morbidity. Moreover, an understanding of the pathogenic mechanisms of CAD in AID might give insight into the potential role of these pathways in CAD without AID as a co-morbidity.

Currently, the basis of the accelerated CVD in AID has not been fully delineated. Certain drugs used to treat autoimmune diseases, such as glucocorticoids, are known to increase the risk of CVD.18,19 There is also evidence of a genetic basis for CVD in autoimmunity,20 including studies that have provided evidence of a genetic causation between SLE, RA, and PSO and CVD.21,22,23 Results indicating a causal association between T1D and CVD are more limited, with one study indicating a causal association between T1D and both coronary and peripheral atherosclerosis but not CAD or CVD.24 Causal associations are lacking between CeD and CAD,25 although CeD patients do have a higher risk of CVD events than the general population.26 In addition to associations with individual autoimmune diseases, the risk of CVD increases as an individual manifests more than one autoimmune disease.5 This suggests that the distinct pathologies of different diseases may contribute different risk elements to CVD in autoimmune patients.27

The availability of large-scale Genome-wide association studies (GWASs) have allowed for the discovery of additional genetic risk loci in CAD as well as in autoimmune diseases individually. However, GWAS are limited in that they focus on identifying variants in a large portion of the patients and cannot account for genetic variation related to more specific disease manifestations. Additionally, collecting this data at a large scale, especially for less prevalent diseases, is time and resource intensive. This limits the ability to identify risk loci specifically associated with CAD in autoimmune patients. Here, we investigated shared genetic risk loci between CAD and SLE, RA, T1D, PSO, PBC, and CeD using a variety of correlative and causal inference methods. We employed cross-phenotype association analysis (CPASSOC) to identify pleiotropic SNPs28 followed by single SNP Mendelian Randomization (ssMR)29 to identify individual SNPs with causal associations with CAD. Finally, we applied colocalization analysis to identify colocalized regions and shared lead SNPs between each disease and CAD.30 After predicting genes from the SNPs associated with each disease, we integrated these results with single-cell RNA sequencing (scRNA-seq) data from human coronary artery plaques to identify individual cell types within atherosclerotic plaques that express genes identified from AID as conveying risk for CAD. The results have generated an understanding of molecular pathways that might contribute to the pathogenesis of CAD.

Results

Genetic correlation between AID and CAD is heavily influenced by the HLA region

To explore the shared genetic predispositions between CAD and AID, we first identified single-nucleotide polymorphisms (SNPs) reaching genome-wide significance (p < 5 × 10−8) in both the CAD (GCST00519631) and AID GWAS, including RA (GCST00231832), PBC (GCST00312933), SLE (GCST00315634), PSO (GCST00552735), T1D (GCST00553636), and CeD (GCST00061237). All GWAS are publicly available (patient populations are summarized in Table S1).31,32,33,34,35,36,37 The number of overlapping SNPs ranged from 221 for T1D, followed by PBC (193), RA (59), PSO (27), SLE (22) and 1 for CeD. For all diseases except T1D and CeD, most SNPs were located in the HLA region spanning chr6:29691116-3305497638 (Table S2) suggesting some degree of shared genetic architecture. Despite the apparent overlap of risk alleles, application of linkage disequilibrium score regression (LDSC)39 failed to identify significant pairwise genetic correlations between CAD and any of the AIDs examined (Figure S1; Table S3), a finding that is likely related to this method’s approach of identifying overall genetic correlations between diseases, but only outside of the HLA region.

Therefore, to further examine the potential correlation between AID and CAD in more depth, we employed CPASSOC, a method that helps identify SNPs that are significant in one or more traits by combining information from multiple datasets.28,40 This method identifies potentially pleiotropic SNPs without the constraint of requiring them to reach genome-wide significance in both datasets. We used the Shet test, which is able to account for the heterogeneity between trait summary statistics, as would be expected in GWAS from different traits.28 Manhattan plots generated for each autoimmune disease show that the majority of SNPs with predicted pleiotropic effects are located on chromosome 6 (Figures 1A–1F). For example, in PBC, 2,711 SNPs were identified by CPASSOC (Figure 1B) of which 1,413 were in the HLA region, and the rest were found on chromosomes 2, 5, 12, and 17. Similarly, between SLE and CAD, 583 out of 653 total CPASSOC-predicted SNPs were located in the HLA region, with the remaining SNPs distributed on chromosomes 1, 2, 7, 10, 11, 12, 15 and 16. Despite the proportionally large contribution of the HLA region to the analysis, a substantial number of SNPs identified were located outside of chromosome 6; this was especially the case for T1D and CeD in which the majority of CPASSOC-predicted SNPs were found throughout the genome (Figures 1E and 1F; Table S4). These initial results show SLE, RA, PSO, and PBC are associated with a number of SNPs that overlap with risk alleles of CAD, many but not all of which reside in the HLA region, whereas CeD and T1D uniquely exhibit SNPs overlapping with those of CAD only outside the HLA region.Figure 1 Predicting Overlapping and Correlated SNPs between Autoimmunity and CAD

(A–F) Manhattan plots of CPASSOC–predicted and overlapping SNPs identified in the indicated autoimmune disease. SNPs shared between each autoimmune disease and CAD are colored, gray SNPs indicate those associated with the autoimmune disease only. Inset pie chart displays the numerical breakdown and chromosome location of significant CPASSOC-predicted SNPs. The red line indicates a significance of p = 5 × 10−8 and the blue line indicates p = 1x10−5. RA, rheumatoid arthritis; PBC, primary biliary cholangitis; SLE, systemic lupus erythematosus; PSO, psoriasis; T1D, type 1 diabetes; CeD, celiac disease, CAD, coronary artery disease.

Identification of disease specific causal variants by single SNP MR

We next identified causal variants between AID and CAD using single-SNP Mendelian Randomization (ssMR). Studies have previously established a causal relationship between certain AIDs (RA, SLE, PSO, and T1D) and CAD using traditional two-sample MR.21,22,23,24 Rather than investigating overall causality, we sought to identify the individual AID-associated SNPs that had a significant causal effect on CAD. To satisfy the assumptions for instrumental variable (IV) selection, we carried out the ssMR analysis on non-HLA SNPs strongly associated (p < 5x10−8) with each secondary disease independently and excluded SNPs weakly associated (p < 1x10−5) with CAD or confounders (including blood pressure, cholesterol, myocardial infarction, LDL) using the phenoscanner tool.41,42 This was followed by stringent LD-clumping (R2 = 0.001, 10000 kb window, 1000G EA reference population) and Steiger filtering to ensure IV independence and directionality. In RA, 27 SNPs were available for analysis after data harmonization and filtering. Of these, 5 SNPs had a positive causal effect on CAD, whereas 2 SNPs had a negative causal effect on CAD (Figure 2A; Table S5). These filtering steps were repeated to generate IVs for PBC, SLE, PSO, T1D and CeD revealing a number of disease-specific SNPs with positive or negative causal effects on CAD (Figures 2B–2F).Figure 2 Predicting causal SNPs between Autoimmunity and CAD using ssMR

(A–F) Forest plots showing beta value +/− standard error for SNPs in RA (A), PBC (B), SLE (C), PSO (D), T1D (E) and CeD (F) with significant causal effects on CAD. Positive causal SNPs in red, negative causal SNPs indicated in blue. RA, rheumatoid arthritis; PBC, primary biliary cholangitis; SLE, systemic lupus erythematosus; PSO, psoriasis; T1D, type 1 diabetes; CeD, celiac disease; CAD, coronary artery disease.

Identification of disease specific causative variants by colocalization analysis

Next, we conducted colocalization analysis to determine shared genetic regions between AID and CAD and to identify individual shared lead SNPs in these regions.30 This analysis, which relies on dividing the genome up into LD blocks and investigating each block for colocalization, provides a more conservative method of evaluating causality between traits than MR.43 This approach identified 3 regions on chromosomes 1 and 15 colocalized between RA and CAD, satisfying the H4 hypothesis (H4>0.75; both traits share the same single causal SNP) (Table 1; Table S6). In a similar manner, 3 regions on chromosomes 2, 12, and 22 were identified as colocalized between PBC and CAD; 3 regions on chromosomes 1, 2, and 12 were identified as colocalized between SLE and CAD; and only a single region was identified as colocalized between PSO and CAD. CeD and CAD had 2 colocalized regions on chromosomes 1 and 12, whereas T1D showed the greatest number of colocalized regions with CAD, with 6 regions on chromosomes 1, 2, 3, 6, 12, and 15.Table 1 Colocalized regions between CAD and autoimmunity

Chr	Region Bounds	Region H4	Lead SNP	SNP position	Disease	
1	108409665–110303931	0.7706	rs646776	109818530	CeD	
1	113273306–114872845	0.8596	rs2476601	114377568	RA	
0.9942	rs6679677	114303808	SLE	
0.8575	rs2476601	114377568	T1D	
1	153180829–154770403	0.9463	rs2228145	154426970	RA	
2	161769733–163503551	0.8051	rs2111485	163110536	PBC	
0.9899	rs1990760	163124051	SLE	
0.7975	rs2111485	163110536	T1D	
3	4516153–46657500	0.9635	rs113010081	46457412	T1D	
6	125424383–127540461	0.9272	rs2045258	126674354	T1D	
12	110336719–113263518	0.9991	rs10774625	111910219	SLE	
0.9885	rs7137828	111932800	T1D	
0.9858	rs3184504	111884608	CeD	
0.9985	rs3184504	111884608	PSO	
12	119754110–122007651	0.9729	rs2244608	121416988	PSO	
15	38530777–37456502	0.8179	rs8043085	38828140	RA	
0.7839	rs56059718	38836777	T1D	
22	29651799–31439918	0.8449	rs13053375	30752942	PBC	
Regions with H4>0.75 between CAD and any AID are listed along with the single lead SNP for the region. The AID with which CAD was colocalized is given in the Disease column.

Identification of CAD-associated and causal genes

To understand the pathways underlying the shared genetics between CAD and each AID, we next identified the most likely genes predicted from each SNP association and generated molecular pathways by grouping based on protein-protein interaction (PPI) mapping.23 We predicted genes associated with each SNP by identifying eQTL associated genes (E-genes) in GTEx,44 genes associated with SNPs in enhancers or promoters using the HACER web tool (T-genes),45 and SNPs in proximal (P-genes) or coding (C-genes) regions using the Ensembl variant effect predictor (VEP).23,46,47,48,49 We found that the combination of causal (ssMR and COLOC) and correlative (general overlap and CPASSOC) methods predicted 142 genes associated with RA and CAD, 202 genes for PBC, 192 genes for SLE, 170 genes for PSO, 61 genes for T1D and 190 genes for CeD (Table S7). For each disease association, a PPI network composed of predicted genes was generated in STRINGdb50 followed by unsupervised MCODE clustering in cytoscape51 and pathway enrichment analysis using the EnrichR web tool (Figure 3).52 Although the majority of genes were derived from correlative methods, causal genes predicted by ssMR and COLOC were also present and distributed throughout each network (Tables S8 and S9). Interestingly, functional enrichment in immunological pathways was observed in each CAD-autoimmune disease network, with RA, PBC, SLE and PSO all dominated by pathways related to Adaptive immune system and MHC protein complex (Figures 3A–3D), whereas T1D was enriched in Chemokine receptor binding (Figure 3E) and CeD was enriched in IFN a/b signaling (Figure 3F). Additional pathways directly related to CAD were also observed across disease associations, including Fatty acid elongation and/or Fatty acid metabolism in RA, PBC, PSO and CeD, Abnormal circulating HDL level in RA, Cholesterol metabolism in PBC, Congestive heart failure in SLE and LDL particle in PSO among others (Table S9. Pathway Results for RA and CAD based on EnrichR, related to Figure 3, Table S10. Pathway Results for PBC and CAD based on EnrichR, related to Figure 3, Table S11. Pathway analysis results for SLE and CAD based on EnrichR, related to Figure 3, Table S12. Pathway Results for PSO and CAD based on EnrichR, related to Figure 3, Table S13. Pathway Results for T1D and CAD based on EnrichR, related to Figure 3, Table S14. Pathway results for CeD and CAD based on EnrichR, related to Figure 3).Figure 3 Clustering and Pathway Analyses of AID and CAD-predicted genes

(A–F) Metaclusters of genes predicted to be involved in both RA (A), PBC (B), SLE (C), PSO (D), T1D (E) and CeD (F) and CAD. Node size indicates the number of genes per cluster; node gradient indicates number of intracluster connections; edge weight indicates the number of intercluster connections; edge gradient indicates connection strength. Clusters are annotated with enriched molecular pathways. RA, rheumatoid arthritis; PBC, primary biliary cholangitis; SLE, systemic lupus erythematosus; PSO, psoriasis; T1D, type 1 diabetes; CeD, celiac disease; CAD, coronary artery disease.

Compared to the other AIDs examined, both T1D and CeD predicted largely distinct SNPs, genes, and pathways shared with CAD. In T1D, 415 SNPs predicted the fewest number of genes (61) that coalesced into 4 clusters with the largest cluster (1) uniquely enriched in the GO Term for Regulation of amide metabolic process (GO:0034248) (Figure 3E; Table S12). CeD predicted 156 SNPs associated with CAD (Figure 3F). Despite predicting fewer associated SNPs between CAD and CeD compared to the other autoimmune diseases, these SNPs were spread across a much greater number of PPI gene clusters than the other diseases. Following unsupervised clustering, there were 10 PPI gene clusters from 190 predicted genes enriched in a variety of biological processes including gastric acid secretion (cluster 4), highly calcium permeable nicotinic acetylcholine receptors (cluster 5), and clathrin derived vesicle budding (cluster 6).

Identification of CAD susceptibility loci predicted from AID

Given the number of shared pathways observed by cluster-specific functional enrichment analysis, we further investigated the overlap between the predicted associations by identifying SNPs and genes that were predicted to be involved in a given AID and CAD. The Upset plot in Figure 4A shows that each AID is predicted to have largely unique SNPs associated with CAD. In fact, a total of 3,832 unique SNPs were identified as associated with any single AID and CAD (Table S15). Importantly, this includes 27 genetic regions identified from any analysis that were detected even though they did not contain SNPs reaching genome wide significance in the CAD GWAS. (p < 5x10−8). An additional 14 genetic regions were identified that lacked an SNP reaching p < 1x10−5, suggesting additional CAD susceptibility regions (Table S16).Figure 4 Overlapping SNP and Gene predictions between CAD and different autoimmune diseases

(A and B) Upset plots showing overlap of predicted SNPs between CAD and autoimmunity (A) and predicted genes associated with CAD and autoimmunity (B).

(C) Clustering and pathway annotations for the 79 genes predicted to overlap between SLE, RA, PSO, and PBC.

(D) Clustering and Pathway analysis for genes predicted to overlap between SLE, PSO, T1D, PBC, and CeD. Genes identified by both causal (MR and COLOC) and correlative (overlap and CPASSOC) methods are outlined in green. Genes identified by only correlative methods are outlined in black. RA, rheumatoid arthritis; PBC, primary biliary cholangitis; SLE, systemic lupus erythematosus; PSO, psoriasis; T1D, type 1 diabetes; CeD, celiac disease; CAD, coronary artery disease.

We also detected 108 SNPs overlapping PBC, T1D and CAD, followed by 77 SNPs overlapping CAD, PSO, RA and PBC. A greater degree of overlap was observed with respect to SNP-predicted genes (Figure 4B), with a single gene, BRAP, involved in MAPK activation, overlapping all 6 AID and 77 genes overlapping between SLE, RA, PSO and PBC. These genes were integrated into a connectivity network and clustered using MCODE to reveal 6 clusters (Figure 4C). Cluster annotations were dominated by processes commonly dysregulated in AID, including a number of class I and II MHC molecules, complement activation, primary immunodeficiency genes, as well as cardiovascular disease pathways (Table S17).

In addition to this larger overlap group, there was a second smaller overlapping group of genes (11 total) from all diseases except RA (Figure 4D). These genes predicted pathways including neurotransmitter clearance, ethanol oxidation, and regulation of amide metabolic process. Interestingly, this group of genes included SH2B3, which has previously been linked to susceptibility to CeD and T1D, as well as genes such as ACAD10, that is involved in the beta-oxidation of fatty acids (Table S18).

scRNA-seq analysis of CAD plaques reveals predicted shared genes enriched in individual cell types

To investigate specific cell types present in the CAD atherosclerotic plaques, we analyzed scRNA-seq data from GSE131778, which includes 8 samples from 4 patients undergoing heart transplants.53 Following filtering and data processing in Cell Ranger54 and Seurat,55 cell type clusters were identified and annotated with the Blueprint Encode cell reference.56,57 This revealed 12 distinct cell clusters, including a number of immune and non-immune cell types, such as endothelial cells and smooth muscle cells (Figure 5A). To determine whether genes linked to AID/CAD exhibited altered expression in atherosclerosis, genes predicted to be associated with RA, PBC, SLE, PSO, T1D, CeD and CAD were matched to DEGs identified in multiple cell types from the scRNA-seq data. For example, 49 RA and CAD-predicted genes were differentially expressed in atherosclerotic cell types, either in these larger annotations or in cellular subtypes (Table S19). Similarly, a further 49 PBC and CAD-predicted genes were differentially expressed, followed by 36 DEGs in SLE, 52 DEGs in PSO, and 58 CeD and CAD-predicted DEGs. The fewest DEGs were found among the T1D and CAD-predicted gene set where only 10 were differentially expressed in any cell type (Table S19).Figure 5 Predicted overlapping AID and CAD genes are enriched in coronary atherosclerotic plaques

(A) UMAP showing cell types from coronary atherosclerotic plaques labeled using the Blueprint Encode reference in SingleR.

(B) Expression weighted cell type enrichment (EWCE) results enrichment of predicted genes in individual cell types from GSE131778. Asterisks (∗) indicate p < 0.05. RA, rheumatoid arthritis; PBC, primary biliary cholangitis; SLE, systemic lupus erythematosus; PSO, psoriasis; T1D, type 1 diabetes; CeD, celiac disease; CAD, coronary artery disease.

To investigate whether the predicted CAD and AID gene sets as a whole were enriched in any of the cell types in atherosclerotic plaques, we applied expression-weighted cell-type enrichment (EWCE) analysis.58 Using the predicted gene lists for each disease as the input, we ran a bootstrap enrichment analysis for each disease in each available cell type. Predicted genes from SLE, RA, PSO, and PBC were over-expressed in B cells, whereas CeD-derived causative genes were over-expressed in macrophages. In contrast, T1D causative genes were only over-expressed in T cells (Figure 5B; Table S20).

Next, we re-clustered and more finely annotated cellular subtypes to identify the cells over-expressing AID-derived CAD causative genes more precisely (Table S20). Cell types were labeled using the Human Primary Cell Atlas (HPCA)59 data rather than the Blueprint Encode Data because of the availability of finer annotations for individual cell types of interest. Sub-clustering B cells revealed a population of germinal center B cells and a population of plasma cells (Figure 6A). While SLE, RA, PSO and PBC-derived CAD predicted genes were over-expressed in the germinal center B cell subset, CeD-derived genes were enriched in plasma cells (Figure 6B). Of the various endothelial cell populations, SLE and PSO-derived CAD predicted genes were over-expressed only in blood vessel endothelial cells and there was a trend for RA-derived genes to be over-expressed as well (Figures 6C and 6D). SLE, PSO and PBC-derived CAD causative genes were over-expressed in M1 macrophages (from the M-CSF/IFNG monocyte labeled subtype) with a trend for RA-derived CAD causative genes to be enriched as well (Figure 6F).Figure 6 Predicted overlapping AID and CAD genes are enriched in cellular subtypes from CAD atherosclerotic plaques

(A, C and E) UMAP of re-clustered B cells (A), endothelial cells (C) and macrophage subtypes (E), labeled using Human Primary Cell Atlas Annotations.

(B, D and F) EWCE results for B cells (B), endothelial cells (D) and macrophages (F). Asterisks (∗) indicate p < 0.05. RA, rheumatoid arthritis; PBC, primary biliary cholangitis; SLE, systemic lupus erythematosus; PSO, psoriasis; T1D, type 1 diabetes; CeD, celiac disease; CAD, coronary artery disease.

In addition to these cell types, we additionally focused on other cell types of interest (monocytes, smooth muscle cells (SMC), T cells, and osteoblasts (Figure S2). Following re-clustering and labeling with finer annotations, one SMC subset was re-identified as fibroblasts and showed enrichment for RA and PSO-derived CAD genes. PBC, T1D and CAD-predicted genes were overexpressed in the stem cell fibroblast population. SLE and CAD-derived genes were over-expressed in NK cells, whereas PSO and PBC-derived CAD causative genes were over-expressed in CCR10+- skin effector T cells.

Discussion

In this analysis, we identified SNPs associated with a variety of AIDs that were predicted to be causative of CAD, identified the likely genes and molecular pathways implicated by these SNPs and confirmed these results by identifying specific cells within CAD atherosclerotic plaques that over-expressed these genes. This approach was predicated on the clinical observation that accelerated atherosclerosis is a co-morbidity of many autoimmune diseases,12,60,61 in addition to previous evidence of heritability of CAD.62 These methods provide an avenue for leveraging GWAS to identify candidate genetic variants and genes associated with individual comorbidities without having to generate additional data, as has been done by newer GWAS.63,64 By determining overlapping or pleiotropic SNPs reaching genome-wide significance in GWAS or in CPASSOC and identifying causal SNPs with MR and COLOC, we were able to predict candidate genes with potential roles in increasing the risk for atherosclerosis. In addition to predicting these genes, we were able to confirm over-expression of these genes in CAD atherosclerotic lesions and also identify specific cells in the lesions in which these genes were over-expressed. These results have not only provided new information on the potential mechanisms of accelerated atherosclerosis in AID, but also provided evidence of additional molecular pathways that might contribute to CAD in general. Given the increasing understanding of atherosclerosis as an inflammatory disease,17 these methods also identify immune/inflammatory pathways that play a role in atherosclerosis.

Identification of overlapping and pleiotropic SNPs allows for the prediction of genes that are relevant in both diseases, although not necessarily through the same mechanisms. We accomplished this initially by identifying SNPs reaching genome-wide significance in both diseases. However, this method confers a limited scope of understanding, as it is biased toward SNPs that are more common in the patient population and ignores rarer variants that can play significant roles in the disease. We also employed CPASSOC to expand our identification of individual genetic variants with pleiotropic effects. This type of cross-trait meta-analysis uncovered numerous SNPs that may not have reached genome-wide significance in both GWAS but did have evidence of pleiotropy between the diseases.

In addition to pleiotropic SNPs, we identified causal SNPs between CAD and each AID using single SNP MR to elucidate shared mechanisms of action. Rather than identifying a global causal estimate, ssMR has the unique ability to evaluate causality of individual SNPs between an exposure and an outcome, determining SNPs with positive and/or negative causal associations between CAD and each AID. Shared causal SNPs were also identified using COLOC which, similar to CPASSOC, does not require that SNPs meet genome-wide significance in both GWAS, allowing for the identification of more associations between two diseases. Finally, SNPs identified using both correlative and causal methods were used to predict likely genes that converge on molecular pathways with biological relevance to both conditions. Immunological pathways related to Antigen processing and presentation and IFN-γ signaling were share among the majority of AIDs in pairwise disease specific comparisons (i.e., RA and CAD, PBC and CAD, etc), as were pathways involved in various aspects of cholesterol and fatty acid metabolism.

Of interest, there was a large group of 77 overlapping genes between SLE, RA, PSO, and PBC that contained a large cluster of HLA and inflammation-associated genes including IFN-γ along with clusters associated with complement and LDL-related pathways. This suggests that the genetic tendency toward systemic inflammation in these diseases confers a significant risk of atherosclerosis. For example, IFNG is secreted by activated macrophages, NK cells, and endothelial cells, as well as by Th1 cells65 and induces the expression of cellular adhesion molecules by endothelial cells to enhance vascular inflammation. Other pathways identified in this overlap group included LDL and complement. Elevated LDL plays a role in increasing adhesive molecule expression in endothelial cells.66 Additionally, excess LDL infiltrates the dysfunctional endothelium and initiates an inflammatory response in artery walls, leading to the accumulation of immune cells in the vascular wall to form lesions.67 Dyslipidemia is commonly observed in AID patients, which could contribute to endothelial damage in this population.68 These results indicate that the downstream results of dyslipidemia in AID patients shared a genetic basis with the CAD-associated LDL pathways. In addition to adaptive immune and LDL pathways in this overlap, we additionally observed complement-associated pathways, including activation of C3 and C5. Complement has a clear involvement in inflammatory diseases. Complement proteins downstream of C3 have been shown to be increased in AID patients, including SLE, RA, and PSO.69,70,71 Hyperactive complement is also considered to be pro-atherogenic,72,73 and activated complement coincides with early fatty streak formation in the arterial intima.72 These results predict a shared genetic basis for complement involvement between AID and CAD.

An additional group of 11 genes overlapped between SLE, PSO, T1D, PBC, and CeD. These 11 genes were related to metabolic pathways, such as ethanol oxidation and regulation of amide metabolic process. Ethanol plays a role in the reactive oxygen species (ROS) formation, that can contribute to oxidative stress and vascular dysfunction.74 ALDH2, one of the overlapping genes, has been implicated in atherosclerosis, with ALDH2 silencing resulting in an unstable plaque with more macrophages and increased inflammation in mice.75 ACAD10, another implicated gene, is involved in the beta-oxidation of fatty acids, which can play a role in atherosclerotic progression by regulating endothelial cell proliferation and function.76,77 These genes implicate pathways that regulate endothelial cells and vascular biology as prevalent across multiple AIDs. Several genes in this group were identified by COLOC or MR, indicating a causal effect. These genes would indicate a shared mechanism of action in both diseases and are of particular interest when considering potential therapeutic implications. In addition to these overlapping gene groups, a number of non-overlapping genes were additionally identified. These genes may be of particular interest when considering the association between specific AID and CAD.

To validate these findings, predicted genes were cross-referenced with genes differentially expressed in scRNA-seq data from human CAD atherosclerotic plaques. Additionally, this analysis permitted us to predict the cell type within the atherosclerotic lesion that was implicated in the genetic association between each AID and CAD. Even though the AID patient populations contain a much higher proportion of young women than the scRNA-seq dataset (3 of the 4 patients in the scRNA-seq atherosclerosis dataset were men, and all 4 were above the age of 5053), the genes predicted from these analyses were over-expressed in multiple cell types of atherosclerotic plaques. Genes and pathways identified as associated with multiple AIDs and CAD may be of particular interest as they are the nexus of specific genetic factors that confer risk across a number of diseases. It is important to emphasize that even though we approached the genetic causation of CAD through the lens of AID, many of the genes and pathways identified were over-expressed in the atherosclerotic lesions of persons without AID as a co-morbidity. These results strongly indicate that these pathways may be of pathogenic importance in all patients with CAD and not just those with co-existent AID.

For example, genes predicted from CAD and SLE, PSO, and PBC were over-expressed by atherosclerotic lesion inflammatory M1 macrophages. Macrophage polarization is critical to atherosclerotic development,66 with both inflammatory M1 and wound-healing M2 macrophages playing a role in the disease. Macrophages can engulf cholesterol to generate foam cells in atherosclerotic plaques, and additionally produce complement and are able to interact with pro-inflammatory Th1 cells.78 An increased M1/M2 ratio has also been associated with AID.79 Additionally, there is evidence that innate immune cells express a proinflammatory phenotype within atherosclerotic plaques.80 This association between macrophage polarization in AID and CAD has potential therapeutic implications. Based on the evidence of a shared involvement of complement between AID and CAD, targeting complement or altering the M1/M2 ratio may provide therapeutic benefit in both AID and atherosclerosis. Interestingly, macrophages were also implicated in CeD, although CeD was associated with the wound healing M2 macrophage phenotype. M2 immunosuppressive cytokines have also been associated with CeD.81 Additional cells over-expressing RA, PBC, SLE, and PSO implicated genes included germinal center B cells and fibroblasts. B cell therapies have been proposed as a potential therapy in atherosclerosis82 and mice with reduced antibody-production capabilities have a reduced plaque size.83 These genes were additionally enriched in atherosclerotic fibroblasts, which are known to regulate inflammation and aid in maintaining plaque structure.84

AID genes predicted from SLE and PSO and CAD were also overexpressed in blood vessel endothelial cells. Endothelial cells are known to be disrupted in AID and can additionally be impacted by LDL and complement. Elevated LDL plays a role in increasing adhesive molecule expression in endothelial cells.66 Additionally, excess LDL infiltrates the dysfunctional endothelium and initiates an inflammatory response in artery walls, leading to the accumulation of immune/inflammatory cells to form lesions.67 Dyslipidemia is commonly observed in AID patients, which could contribute to endothelial damage in this population.68 These results indicate that the downstream results of dyslipidemia in AID patients shared a genetic basis with the CAD-associated LDL pathways.

T1D showed a largely distinct enrichment in the scRNA-seq data as compared to the other AID. T1D-predicted genes were enriched in the general T cell population in addition to fibroblast stem cells. However, the T1D and CAD-predicted genes were not enriched in any individual T cell subtype. T1D derived genes were not over-expressed in any macrophage or B cell types that also over-expressed genes derived from other AID. Previous studies have failed to show an overall causal association between T1D and CAD.24 Other studies have shown that the greatest risk factor for T1D-associated CAD is age,85 a more traditional risk factor for CVD. These indications suggest that T1D may confer risk for CAD through more traditional risk factors other than the genetics underlying autoimmune diseases.

These methods and results provide an avenue to identify existing treatments as candidates for treating CAD in AID patients or in patients without autoimmunity. For example, treatments seeking to modify or regulate complement levels, or M1/M2 ratios may be good candidates for treating or preventing AID-associated CAD in addition to CAD in the general population. A mouse study showed promise using an anti-C5 antibody to improve atherosclerosis and reduce lesion size.86 The anti-inflammatory drug Baicalin has been used to increase the M1/M2 ratio in CVD, resulting in an improvement of cardiac function.87,88 One treatment that has recently been shown to be effective in humans is colchicine, a drug known to have anti-inflammatory properties by inhibiting microtubule assembly.89 Low-dose colchicine was approved as an anti-inflammatory to use in conjunction with statins to further lower risk of cardiovascular events.90 Interestingly, some studies have found that at low doses colchicine works primary by preventing foam cell formation and reducing inflammation related to cholesterol rather than through microtubule alteration.91 Additionally, macrophages, a precursor of foam cells, were implicated as targets of colchicine therapy. Pathways implicated in our results, including those related to inflammation and LDL, may be amenable to treatment with this agent.

Limitations of the study

This analysis has several limitations. First, the autoimmune GWAS used were from predominantly European populations. Further analysis in other ancestries would be beneficial to fully understanding relationships between CAD and AID. Second, analysis was not conducted to determine a possible differential impact of sex, which could be of interest given the significantly higher prevalence of AID in female and of CAD in male patients. The publicly available GWAS data did not allow for stratification by sex. The scRNA-seq data was also limited to explore this question as there was only a single female patient in this dataset. Additionally, pathway analyses were limited by the availability of protein-coding genes and interactions in STRINGdb. Finally, pathway identification is limited by the platforms used for pathway prediction. Despite these limitations, however, this analysis was able to identify SNPs and genes with implications in both AID and CAD and to organize these genes in biological context in atherosclerotic plaques. These kinds of analyses are important to identify potential new pathways of CAD pathogenesis as well as additional treatment targets.

Resource availability

Lead contact

Further information and requests for resources should be directed to the lead contact, Sophia Kerns (sophia.kerns@ampelbiosolutions.com).

Materials availability

This study did not generate any new unique reagents.

Data and code availability

• This study did not generate any new data. All data is from existing, publicly available datasets with data accession numbers listed here and in the key resources table. GWAS for RA (GCST00231832), PBC (GCST00312933), SLE (GCST00315634), PSO (GCST00552735), T1D (GCST00553636), CeD (GCST00061237) and CAD (GCST00519631) were obtained from the GWAS catalog. scRNAseq data was obtained from GEO (GSE13177853).

• All code is publicly available and was obtained from github and implemented in python (LDSC39 Cell Ranger54) or through R packages (CPASSOC,40 TwoSampleMR,29 COLOC,30 Seurat,55 and EWCE58).

• Other software is publicly available and was downloaded from the developer (cytoscape/clusterMaker51) or implemented through the available website (phenoscanner,41,42 GTEx,44 HACER,45 VEP,49 STRINGdB,50enrichR52).

• Any additional information required to reanalyze the data reported in this work paper is available from the lead contact upon request.

Acknowledgments

The work presented in this manuscript was funded by a grant by the RILITE Research Institute.

Author contributions

Conceptualization: P.E.L., A.C.G., and K.A.O; methodology, P.E.L; software, formal analysis, and investigation: S.K.; writing – original draft: S.K., K.A.O., and P.E.L.; writing – review and editing: K.A.O, A.D., J.K., and P.E.L; funding acquisition: P.E.L and A.C.G.; Supervision: P.E.L. and A.C.G.

Declaration of interests

The authors declare no competing interests.

STAR★Methods

Key resources table

REAGENT or RESOURCE	SOURCE	IDENTIFIER	
Deposited Data	
	
Rheumatoid arthritis (RA) GWAS	GWAS catalog (https://www.ebi.ac.uk/gwas/studies/GCST002318)	GCST00231832	
Primary biliary cholangitis (PBC) GWAS	GWAS catalog (https://www.ebi.ac.uk/gwas/studies/GCST003129/)	GCST00312933	
Systemic lupus erythematosus (SLE) GWAS	GWAS catalog (https://www.ebi.ac.uk/gwas/studies/GCST003156)	GCST00315634	
Psoriasis (PSO) GWAS	GWAS Catalog (https://www.ebi.ac.uk/gwas/studies/GCST005527)	GCST00552735	
Type 1 diabetes (T1D) GWAS	GWAS catalog (https://www.ebi.ac.uk/gwas/studies/GCST005536)	GCST00553636	
Celiac disease (CeD) GWAS	GWAS catalog (https://www.ebi.ac.uk/gwas/studies/GCST000612)	GCST00061237	
Coronary Artery Disease (CAD) GWAS	GWAS catalog (https://www.ebi.ac.uk/gwas/studies/GCST005196)	GCST00519631	
Coronary Artery scRNAseq	GEO Database (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE131778)	GSE13177853	
	
Software and Algorithms	
	
LDSC	Bulik-Sullivan et al., 201539	https://github.com/bulik/ldsc; RRID: SCR_022801	
CPASSOC	Zhu et al., 201540	http://hal.case.edu/zhu-web/	
Two Sample MR v.0.5.6	Hemani et al., 201829	https://github.com/MRCIEU/TwoSampleMR; RRID: SCR_019010	
Phenoscanner	Kamat et al., 2019,41 Staley et al., 201642	www.phenoscanner.medschl.cam.ac.uk	
COLOC v5.5.0.1	Wallace et al., 202130	https://github.com/chr1swallace/coloc	
GTEx version 8	Lonsdale et al., 201344	https://www.gtexportal.org/home/; RRID: SCR_001618	
HACER	Wang et al., 201945	https://bioinfo.vanderbilt.edu/AE/HACER/search.html	
VEP	Cunningham et al., 202249	https://useast.ensembl.org/Tools/VEP; RRID: SCR_007931	
STRINGDb v11.5	Szklarczyk et al., 202350	https://string-db.org/; RRID: SCR_005223	
Cytoscape v3.9.1, clusterMaker v1.2.1	Morris et al., 201151	https://cytoscape.org/; RRID: SCR_003032	
EnrichR	Chen et al., 201352	https://maayanlab.cloud/Enrichr/; RRID: SCR_001575	
Cell Ranger v7.0.1	Zheng et al., 201754	https://www.10xgenomics.com/support/software/cell-ranger/latest; RRID: SCR_017344	
Seurat v4.4.0	Butler et al., 201855	https://github.com/satijalab/seurat; RRID: SCR_016341	
EWCE v1.2.0	Skene et al., 201658	https://github.com/NathanSkene/EWCE	

Method details

Linkage disequilibrium score regression (LDSC)

LDSC was used to estimate genome-wide genetic correlations between traits using GWAS summary statistics.39 GWAS summary statistics were prepared for analysis using the “munge_sumstats.py” script provided on github (https://github.com/bulik/ldsc). Using the LDSC software provided on the same github page, munged summary statistics files, and reference data from the Broad google bucket (https://console.cloud.google.com/storage/browser/broad-alkesgroup-public-requester-pays/LDSCORE), including European LD scores ‘eur_w_ld_chr’ or ‘weights_hm3_no_hla’ as weights for analysis excluding the HLA region. The “ldsc.py” script was used with the “—rg” flag and standard parameters to generate correlation estimates between SLE, RA, PSO, T1D, PBC, CeD, and CAD.

Overlap and cross-phenotype association analysis (CPASSOC)

Overlap in significant SNPs from autoimmune and CAD GWAS were identified by filtering SNPs that reached genome-wide significance (p < 5x10−8) in both the CAD GWAS and each of the autoimmune GWAS (SLE, RA, PSO, T1D, PBC, and CeD) individually.

CPASSOC was used to identify potentially pleiotropic SNPs between CAD and RA without regards to causality.40 This method can be used to identify further overlap between each disease and CAD beyond SNPs that reach genome-wide significance in both GWAS. The method was implemented using the downloadable CPASSOC script (http://hal.case.edu/zhu-web/). This method has two available test statistics: the Shom method, which is similar to a fixed effect meta-analysis, and the Shet method, which is better able to account for heterogeneity in summary statistics than the Shom method. We selected the Shet method for this analysis, as heterogeneity would be expected between CAD summary statistics and autoimmune summary statistics. The method requires a correlation matrix, Z scores for all input SNPs, and the sample size of the traits as inputs. Correlation matrices between CAD and each autoimmune disease were calculated by LD-pruning SNPs with r2 = 0.2 and further filtering out SNPs with a summary Z score of greater than 1.96 or less than −1.96 to prevent an artificially high correlation estimate.28 The Shet statistic was then calculated for each SNP. SNPs reaching genome-wide significance by the Shet statistic (p < 5 × 10−8) and suggestive significance (p < 1 × 10−3) in both the given autoimmune disease and CAD summary statistics were considered significant. This was repeated for each additional autoimmune disease.

Single SNP Mendelian Randomization

Given the previous establishment of robust causal estimates between the included autoimmune diseases and CAD, we did not conduct traditional Two-Sample MR analyses. However, we conducted single-snp analyses to identify individual SNPs with causal effects between the selected autoimmune diseases and CAD. Instrumental variables (IVs) were selected for the analysis similarly to a traditional two-sample MR analysis.

MR requires that several assumptions be met: the relevance assumption, the exclusion-restriction assumption, and the independence assumption. To satisfy the first assumption, SNPs significantly associated with RA (p < 5 × 10−8) were obtained from the RA GWAS. To satisfy the exclusion-restriction assumption, SNPs weakly associated (p < 1 × 10−5) with CAD were excluded from the analysis. To satisfy the independence assumption, SNPs weakly associated (p < 1 × 10−5) with any potential confounders (blood pressure, cholesterol, insulin resistance, obesity, smoking, thyroid disease, metabolic syndrome, cardiovascular disease, hypertension, ischemic stroke, myocardial infarction, apolipoproteins, triglyceride, LDL, CRP, HDL) were obtained from the Phenoscanner database (www.phenoscanner.medschl.cam.ac.uk) and removed from the IV list.41,42 LD clumping was employed using the clump data function (R2 = 0.001, 10,000 kb window, 1000G EA reference population) to ensure that IVs were independent from each other.92

Single SNP MR was used to test for SNP-level causal relationships between RA and CAD using the TwoSampleMR package R (v0.5.6, https://github.com/MRCIEU/TwoSampleMR) in R.29 Summary statistics for RA and CAD were manually imported into R and summary statistics were made MR-base compatible using the ‘format data’ command. The ‘allele harmonization’ command was used to ensure that effect estimates of the exposure and outcome are based on matching alleles. Because some SNPs are removed in this step due to completely mismatching alleles, a small number of IVs are excluded from the final analysis. Steiger filtering was carried out to ensure that all IVs were more strongly associated in the exposure to outcome direction than vice versa.29 The single SNP MR method was carried out using the ‘mr singlesnp’ function.

Colocalization (COLOC)

Colocalization was performed using the Bayesian colocalization method COLOC.30 This method evaluates the probability that any given region is colocalized in two traits using 4 hypothesis: H0 is that there is no significant association between the region and either trait; H1 is that there is a significant association between the region and the first trait only; H2 is that there is an association between the region and the second trait only; H3 is that both traits are associated with the region but with different single causal SNPs; and H4 is that both traits are associated with the region and share the same single causal variant. Colocalized SNPs between each autoimmune disease and CAD were determined using the COLOC R package (v5.5.0.1). SNPs present in both the CAD and, for example, RA GWAS were determined and then divided into regions using the LDetect algorithm.93 Beta values, variances, positions, p values, and sample sizes were used as input to the ‘coloc.abf’ function. In regions with a significant H4 hypothesis (pp.H4>0.75) the lead causal SNP was determined as the SNP with the highest individual posterior probability. Default prior probabilities were used for all analyses (p1 = 1 × 10−4, p2 = 1 × 10−4, p12 = 5 × 10−6).

Identification of SNP-predicted genes

Expression quantitative trait loci (eQTLs) were identified using GTEx44 version 8 (GTEXportal.org) and mapped to their associated eQTL expression genes (E-Genes). To find SNPs in enhancers and promoters, and their associated transcription factors and downstream target genes (T-Genes), we queried the atlas of Human Active Enchancers to interpret Regulatory variants (HACER, http://bioinfo.vanderbilt.edu/AE/HACER).45 To find SNPs in exons of protein-coding genes (C-Genes) and include proximal genes (P-Genes, within 5 kb), we queried the human Ensembl genome browser’s variant effect predictor (VEP, ensembl.org/info/docs/tools/vep, GRCh38.p12).49

Network analysis

Protein-protein interaction (PPI) networks of SNP-predicted protein-coding genes were generated by STRING (https://string-db.org, version 11.5),50 and resulting networks were imported into Cytoscape51 (version 3.9.1) for visualization and partitioned with MCODE via the clusterMaker (version 1.2.1) plugin. Metastructures are based on PPI networks. For all metastructures, node gradient shading is proportional to intra-cluster connectivity, cluster size indicates number of genes per cluster and edge weight indicates inter-cluster connections.

Functional gene set analysis

Predicted genes were examined using the EnrichR (https://maayanlab.cloud/Enrichr/)52 web server for molecular pathway enrichment analysis.

Single-cell RNAseq analysis

scRNAseq data from coronary artery atherosclerotic plaques (GSE131778)53 was obtained from the GEO database. Sequencing reads were processed with 10X Genomics Cell Ranger Software (v7.0.1) using the standard pipeline.54 Downstream processing was conducted using the Seurat R package (v4.4.0).55 The samples were imported in R using the ‘Read10x’ function and Seurat objects were created for each using the ‘CreateSeuratObject’ function. To remove low quality reads, cells were filtered by unique molecular identifier (UMI), number of genes available, and percent mitochondrial genes. Cells were filtered from each sample to only include cells with a UMI greater than 500, gene number greater than 300, log10 Genes per UMI greater than 0.8, and mitochondrial gene to overall gene ratio less than 0.2. Genes were additionally filtered to include genes only expressed in 10 or greater cells. Each sample was then normalized and Cell cycle scoring was performed on each sample using the ‘CellCycleScoring’ function. Cells were clustered using the ‘FindClusters’ function, and clusters were labeled using the Blueprint Encode Data reference from the celldex package.56,57 Each cluster was further annotated by reclustering and labeling with the Human Primary Cell Atlas Data,59 which has finer annotations available than the BluePrint Encode reference.

Expression-weighted cell-type enrichment (EWCE) analysis

EWCE analysis was conducted to integrate scRNAseq data with genes predicted to be involved in autoimmunity and CAD through overlap, CPASSOC, ssMR, and COLOC. This analysis was conducted using the EWCE package (v1.2.0).58 Normalized scRNAseq data matrices were extracted from the analyzed scRNAseq data using the ‘GetAssayData’ command from Seurat. The matrices were then analyzed for input into the EWCE analysis using the ‘generate celltype data’ command. The levels of analysis were determined to be the singleR annotations from scRNA analysis. The background for the analysis was all genes contained in the scRNAseq panel. Genes predicted to be associated with both RA and CAD were input in the ‘hits’ parameter. The analysis was run using the ‘bootstrap_enrichment_test’ function, with 10000 reps. Results were considered significant with a p-value of less than 0.05.

Quantification and statistical analysis

Linkage disequilibrium score regression (LDSC)

LDSC uses GWAS summary statistics to estimate genetic correlations between diseases. Results are given as a rg value and were considered significant at p < 0.05. Dispersion is given as standard error. Results are presented in Figure S1and further detailed in Table S3.

Overlap analysis and genome-wide significance

SNPs were identified as reaching genome-wide significance at p < 5 × 10−8. Overlapping SNPs were identified as those SNPs reaching this genome-wide significance threshold in both GWAS. Genome-wide significance thresholds are marked visually in Figure 1 as the red line in Manhattan plots. The blue line in these plots indicates the suggestive significance threshold (p < 1 × 10−5) SNPs reaching these thresholds are additionally listed in Table S2.

Cross-phenotype association analysis (CPASSOC)

CPASSOC is a method of identifying additional SNP associations in multiple diseases. The Shet statistic was used to identify SNPs that are associated with both AIDs and CAD. This statistic uses summary statistic information, including Z scores and GWAS sample sizes, to determine SNPs likely to be associated with both diseases. The significance threshold for the Shet statistic was set at p < 5 × 10−8, with the additional stipulation that SNPs must reach a significance level of p < 1 × 10−3 in each individual GWAS to be considered significant. Results are shown in Figure 2 along with the overlap analysis. Shet results for significant results are listed in Table S4.

Single SNP Mendelian Randomization (ssMR)

ssMR uses Wald’s ratios to identify individual SNPs that are likely to have a causal effect on an outcome disease. Each of these SNPs were filtered to ensure that the assumptions of Mendelian Randomization were met prior to testing SNPs. Results are given as a beta value and a standard error for each SNP, along with a p value. A significance level of p < 0.05 was used to determine significant causal estimates. Significant Results are visualized in Figure 2, and results for all investigated IVs are further detailed in Table S5.

Colocalization (COLOC)

COLOC is a Bayesian statistical method that identifies the probability that a given region of the genome is significantly associated with multiple diseases and whether those diseases share the same lead variant in that region. The method identifies the posterior probability (PP) that any goven region is not associated with either disease (H0), that it is associated with the first disease (H1), that it is associated with the second disease (H2), that it is associated with both diseases but with a different lead variant (H3), and that it is associated with both diseases with the same lead variant (H4). The threshold for a region being considered associated with both diseases was set at PP.H4>0.75. Summaries of significant results are given in Table 1, and more detailed results are given in Table S6.

Expression-weighted cell-type enrichment (EWCE) analysis

EWCE runs a bootstrap enrichment analysis to determine whether a given set of genes is overexpressed in an individual cell type in scRNAseq data as compared to other cell types in the dataset. An EWCE result was considered significant at p < 0.05. These results are indicated by an asterisk in Figures 5 and 6 as well as Figure S2, with asterisks indicating a significant result. Fold changes and p values are additional listed in Table S20.

Supplemental information

Document S1. Figures S1 and S2

Table S1. Statistical Descriptions of Publicly available Datasets used in this analysis, related to all Figures/Tables

Summaries are provided of the definition of cases in the “Patient Population Definition” column. Meta-analyses are marked and are further described by their respective references.

Table S2. Lists of SNPs reaching genome-wide significance (p < 5x10ˆ-8) in both the Autoimmune and the CAD GWAS, related to Figure 1

Table S3. Full Linkage Disequilibrium Score Regression (LDSC) results between CAD and autoimmune diseases, related to Figures 1 and S1

Significant Results are highlighted in red.

Table S4. CPASSOC Results for each autoimmune disease and CAD, related to Figure 1

Table S5. Single SNP MR Results for all diseases and CAD, related to Figure 2

Significant results are highlighted in red.

Table S6. Colocalization results for each Autoimmune disease and CAD, related to Table 1

The lead SNP for each significant region is listed. Region H4 indicates the H4 hypothesis posterior probability for the entire region, SNP indicates the lead SNP for the region, and SNP.PP. H4 indicates that individual SNP’s H4 results. The region bounds are as defined by the LDetect algorithm.

Table S7. Genes predicted to be associated with both CAD and autoimmunity, related to Figure 3

The method by which each gene was identified is listed under the Method column. Repeated genes were identified by more than one method.

Table S8. Predicted Genes organized by Cluster and labeled by prediction method, related to Figure 3

Clusters were calculated using the MCODE algorithm in Cytoscape. Genes are listed twice if they were predicted by multiple methods.

Table S9. Pathway Results for RA and CAD based on EnrichR, related to Figure 3

Table S10. Pathway Results for PBC and CAD based on EnrichR, related to Figure 3

Table S11. Pathway analysis results for SLE and CAD based on EnrichR, related to Figure 3

Table S12. Pathway Results for PSO and CAD based on EnrichR, related to Figure 3

Table S13. Pathway Results for T1D and CAD based on EnrichR, related to Figure 3

Table S14. Pathway results for CeD and CAD based on EnrichR, related to Figure 3

Table S15. All Predicted SNPs by any disease organized by Ldetect Region, related to Figure 4

Table S16. Regions Identified as Associated between any Autoimmune Disease and CAD that does not have an SNP reaching genome-wide significance (p < 5∗10-8) in the CAD GWAS, related to Figure 4

Table S17. Pathway Results for 79 Genes overlapping between SLE, RA, PSO, and PBC from EnrichR, related to Figure 4

Table S18. Pathways from the 11 genes predicted to overlap between SLE, PSO, T1D, PBC, and CeD, related to Figure 4

Table S19. Genes predicted to be involved in autoimmunity and CAD that were significantly differentially expressed in any cell type in scRNA-seq data, related to Figures 5 and 6

Genes are sorted by their cluster, and are listed mutliple times with their results in multiple cell types if applicable. The number of unique DE genes are listed beneath the disease designation.

Table S20. Fold Changes and P values for EWCE results for overall and finer annotations, related to Figures 5 and 6

Supplemental information can be found online at https://doi.org/10.1016/j.isci.2024.110715.
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