Genetics of circulating inflammatory proteins identifies drivers of immune-mediated disease risk and therapeutic targets.

Circulating proteins have important functions in inflammation and a broad range of diseases. To identify genetic influences on inflammation-related proteins, we conducted a genome-wide protein quantitative trait locus (pQTL) study of 91 plasma proteins measured using the Olink Target platform in 14,824 participants. We identified 180 pQTLs (59 cis, 121 trans). Integration of pQTL data with eQTL and disease genome-wide association studies provided insight into pathogenesis, implicating lymphotoxin-α in multiple sclerosis. Using Mendelian randomization (MR) to assess causality in disease etiology, we identified both shared and distinct effects of specific proteins across immune-mediated diseases, including directionally discordant effects of CD40 on risk of rheumatoid arthritis versus multiple sclerosis and inflammatory bowel disease. MR implicated CXCL5 in the etiology of ulcerative colitis (UC) and we show elevated gut CXCL5 transcript expression in patients with UC. These results identify targets of existing drugs and provide a powerful resource to facilitate future drug target prioritization.

Human Disease Variation in the Light of Population Genomics.

Identifying the causes of similarities and differences in genetic disease prevalence among humans is central to understanding disease etiology. While present-day humans are not strongly differentiated, vast amounts of genomic data now make it possible to study subtle patterns of genetic variation. This allows us to trace our genomic history thousands of years into the past and its implications for the distribution of disease-associated variants today. Genomic analyses have shown that demographic processes shaped the distribution and frequency of disease-associated variants over time. Furthermore, local adaptation to new environmental conditions-including pathogens-has generated strong patterns of differentiation at particular loci. Researchers are also beginning to uncover the genetic architecture of complex diseases, affected by many variants of small effect. The field of population genomics thus holds great potential for providing further insights into the evolution of human disease.

CD49a Expression Defines Tissue-Resident CD8+ T Cells Poised for Cytotoxic Function in Human Skin.

Tissue-resident memory T (Trm) cells form a heterogeneous population that provides localized protection against pathogens. Here, we identify CD49a as a marker that differentiates CD8+ Trm cells on a compartmental and functional basis. In human skin epithelia, CD8+CD49a+ Trm cells produced interferon-γ, whereas CD8+CD49a- Trm cells produced interleukin-17 (IL-17). In addition, CD8+CD49a+ Trm cells from healthy skin rapidly induced the expression of the effector molecules perforin and granzyme B when stimulated with IL-15, thereby promoting a strong cytotoxic response. In skin from patients with vitiligo, where melanocytes are eradicated locally, CD8+CD49a+ Trm cells that constitutively expressed perforin and granzyme B accumulated both in the epidermis and dermis. Conversely, CD8+CD49a- Trm cells from psoriasis lesions predominantly generated IL-17 responses that promote local inflammation in this skin disease. Overall, CD49a expression delineates CD8+ Trm cell specialization in human epithelial barriers and correlates with the effector cell balance found in distinct inflammatory skin diseases.

Portability of 245 polygenic scores when derived from the UK Biobank and applied to 9 ancestry groups from the same cohort.

The low portability of polygenic scores (PGSs) across global populations is a major concern that must be addressed before PGSs can be used for everyone in the clinic. Indeed, prediction accuracy has been shown to decay as a function of the genetic distance between the training and test cohorts. However, such cohorts differ not only in their genetic distance but also in their geographical distance and their data collection and assaying, conflating multiple factors. In this study, we examine the extent to which PGSs are transferable between ancestries by deriving polygenic scores for 245 curated traits from the UK Biobank data and applying them in nine ancestry groups from the same cohort. By restricting both training and testing to the UK Biobank data, we reduce the risk of environmental and genotyping confounding from using different cohorts. We define the nine ancestry groups at a sub-continental level, based on a simple, robust, and effective method that we introduce here. We then apply two different predictive methods to derive polygenic scores for all 245 phenotypes and show a systematic and dramatic reduction in portability of PGSs trained using Northwestern European individuals and applied to nine ancestry groups. These analyses demonstrate that prediction already drops off within European ancestries and reduces globally in proportion to genetic distance. Altogether, our study provides unique and robust insights into the PGS portability problem.

Evaluation of polygenic prediction methodology within a reference-standardized framework.

The predictive utility of polygenic scores is increasing, and many polygenic scoring methods are available, but it is unclear which method performs best. This study evaluates the predictive utility of polygenic scoring methods within a reference-standardized framework, which uses a common set of variants and reference-based estimates of linkage disequilibrium and allele frequencies to construct scores. Eight polygenic score methods were tested: p-value thresholding and clumping (pT+clump), SBLUP, lassosum, LDpred1, LDpred2, PRScs, DBSLMM and SBayesR, evaluating their performance to predict outcomes in UK Biobank and the Twins Early Development Study (TEDS). Strategies to identify optimal p-value thresholds and shrinkage parameters were compared, including 10-fold cross validation, pseudovalidation and infinitesimal models (with no validation sample), and multi-polygenic score elastic net models. LDpred2, lassosum and PRScs performed strongly using 10-fold cross-validation to identify the most predictive p-value threshold or shrinkage parameter, giving a relative improvement of 16-18% over pT+clump in the correlation between observed and predicted outcome values. Using pseudovalidation, the best methods were PRScs, DBSLMM and SBayesR. PRScs pseudovalidation was only 3% worse than the best polygenic score identified by 10-fold cross validation. Elastic net models containing polygenic scores based on a range of parameters consistently improved prediction over any single polygenic score. Within a reference-standardized framework, the best polygenic prediction was achieved using LDpred2, lassosum and PRScs, modeling multiple polygenic scores derived using multiple parameters. This study will help researchers performing polygenic score studies to select the most powerful and predictive analysis methods.

Genetic Landscape of the ACE2 Coronavirus Receptor.

BACKGROUND: SARS-CoV-2, the causal agent of COVID-19, enters human cells using the ACE2 (angiotensin-converting enzyme 2) protein as a receptor. ACE2 is thus key to the infection and treatment of the coronavirus. ACE2 is highly expressed in the heart and respiratory and gastrointestinal tracts, playing important regulatory roles in the cardiovascular and other biological systems. However, the genetic basis of the ACE2 protein levels is not well understood. METHODS: We have conducted the largest genome-wide association meta-analysis of plasma ACE2 levels in >28 000 individuals of the SCALLOP Consortium (Systematic and Combined Analysis of Olink Proteins). We summarize the cross-sectional epidemiological correlates of circulating ACE2. Using the summary statistics-based high-definition likelihood method, we estimate relevant genetic correlations with cardiometabolic phenotypes, COVID-19, and other human complex traits and diseases. We perform causal inference of soluble ACE2 on vascular disease outcomes and COVID-19 severity using mendelian randomization. We also perform in silico functional analysis by integrating with other types of omics data. RESULTS: We identified 10 loci, including 8 novel, capturing 30% of the heritability of the protein. We detected that plasma ACE2 was genetically correlated with vascular diseases, severe COVID-19, and a wide range of human complex diseases and medications. An X-chromosome cis-protein quantitative trait loci-based mendelian randomization analysis suggested a causal effect of elevated ACE2 levels on COVID-19 severity (odds ratio, 1.63 [95% CI, 1.10-2.42]; P=0.01), hospitalization (odds ratio, 1.52 [95% CI, 1.05-2.21]; P=0.03), and infection (odds ratio, 1.60 [95% CI, 1.08-2.37]; P=0.02). Tissue- and cell type-specific transcriptomic and epigenomic analysis revealed that the ACE2 regulatory variants were enriched for DNA methylation sites in blood immune cells. CONCLUSIONS: Human plasma ACE2 shares a genetic basis with cardiovascular disease, COVID-19, and other related diseases. The genetic architecture of the ACE2 protein is mapped, providing a useful resource for further biological and clinical studies on this coronavirus receptor.

Imputed gene expression risk scores: a functionally informed component of polygenic risk.

Integration of functional genomic annotations when estimating polygenic risk scores (PRS) can provide insight into aetiology and improve risk prediction. This study explores the predictive utility of gene expression risk scores (GeRS), calculated using imputed gene expression and transcriptome-wide association study (TWAS) results. The predictive utility of GeRS was evaluated using 12 neuropsychiatric and anthropometric outcomes measured in two target samples: UK Biobank and the Twins Early Development Study. GeRS were calculated based on imputed gene expression levels and TWAS results, using 53 gene expression-genotype panels, termed single nucleotide polymorphism (SNP)-weight sets, capturing expression across a range of tissues. We compare the predictive utility of elastic net models containing GeRS within and across SNP-weight sets, and models containing both GeRS and PRS. We estimate the proportion of SNP-based heritability attributable to cis-regulated gene expression. GeRS significantly predicted a range of outcomes, with elastic net models combining GeRS across SNP-weight sets improving prediction. GeRS were less predictive than PRS, but models combining GeRS and PRS improved prediction for several outcomes, with relative improvements ranging from 0.3% for height (P = 0.023) to 4% for rheumatoid arthritis (P = 5.9 × 10-8). The proportion of SNP-based heritability attributable to cis-regulated expression was modest for most outcomes, even when restricting GeRS to colocalized genes. GeRS represent a component of PRS and could be useful for functional stratification of genetic risk. Only in specific circumstances can GeRS substantially improve prediction over PRS alone. Future research considering functional genomic annotations when estimating genetic risk is warranted.

Auxilin is a novel susceptibility gene for congenital heart block which directly impacts fetal heart function.

OBJECTIVE: Neonatal lupus erythematosus (NLE) may develop after transplacental transfer of maternal autoantibodies with cardiac manifestations (congenital heart block, CHB) including atrioventricular block, atrial and ventricular arrhythmias, and cardiomyopathies. The association with anti-Ro/SSA antibodies is well established, but a recurrence rate of only 12%-16% despite persisting maternal autoantibodies suggests that additional factors are required for CHB development. Here, we identify fetal genetic variants conferring risk of CHB and elucidate their effects on cardiac function. METHODS: A genome-wide association study was performed in families with at least one case of CHB. Gene expression was analysed by microarrays, RNA sequencing and PCR and protein expression by western blot, immunohistochemistry, immunofluorescence and flow cytometry. Calcium regulation and connectivity were analysed in primary cardiomyocytes and cells induced from pleuripotent stem cells. Fetal heart performance was analysed by Doppler/echocardiography. RESULTS: We identified DNAJC6 as a novel fetal susceptibility gene, with decreased cardiac expression of DNAJC6 associated with the disease risk genotype. We further demonstrate that fetal cardiomyocytes deficient in auxilin, the protein encoded by DNAJC6, have abnormal connectivity and Ca2+ homoeostasis in culture, as well as decreased cell surface expression of the Cav1.3 calcium channel. Doppler echocardiography of auxilin-deficient fetal mice revealed cardiac NLE abnormalities in utero, including abnormal heart rhythm with atrial and ventricular ectopias, as well as a prolonged atrioventricular time intervals. CONCLUSIONS: Our study identifies auxilin as the first genetic susceptibility factor in NLE modulating cardiac function, opening new avenues for the development of screening and therapeutic strategies in CHB.

Early prediction of clinical response to anti-TNF treatment using multi-omics and machine learning in rheumatoid arthritis.

OBJECTIVES: Advances in immunotherapy by blocking TNF have remarkably improved treatment outcomes for Rheumatoid arthritis (RA) patients. Although treatment specifically targets TNF, the downstream mechanisms of immune suppression are not completely understood. The aim of this study was to detect biomarkers and expression signatures of treatment response to TNF inhibition. METHODS: Peripheral blood mononuclear cells (PBMCs) from 39 female patients were collected before anti-TNF treatment initiation (day 0) and after 3 months. The study cohort included patients previously treated with MTX who failed to respond adequately. Response to treatment was defined based on the EULAR criteria and classified 23 patients as responders and 16 as non-responders. We investigated differences in gene expression in PBMCs, the proportion of cell types and cell phenotypes in peripheral blood using flow cytometry and the level of proteins in plasma. Finally, we used machine learning models to predict non-response to anti-TNF treatment. RESULTS: The gene expression analysis in baseline samples revealed notably higher expression of the gene EPPK1 in future responders. We detected the suppression of genes and proteins following treatment, including suppressed expression of the T cell inhibitor gene CHI3L1 and its protein YKL-40. The gene expression results were replicated in an independent cohort. Finally, machine learning models mainly based on transcriptomic data showed high predictive utility in classifying non-response to anti-TNF treatment in RA. CONCLUSIONS: Our integrative multi-omics analyses identified new biomarkers for the prediction of response, found pathways influenced by treatment and suggested new predictive models of anti-TNF treatment in RA patients.

Arginase 1 is upregulated at admission in patients with ST-elevation myocardial infarction.

BACKGROUND: The mechanisms underlying rupture of a coronary atherosclerotic plaque and development of myocardial ischemia-reperfusion injury in ST-elevation myocardial infarction (STEMI) remain unresolved. Increased arginase 1 activity leads to reduced nitric oxide (NO) production and increased formation of reactive oxygen species due to uncoupling of the NO-producing enzyme endothelial NO synthase (eNOS). This contributes to endothelial dysfunction, plaque instability and increased susceptibility to ischemia-reperfusion injury in acute myocardial infarction. OBJECTIVE: The purpose of this study was to test the hypothesis that arginase gene and protein expression are upregulated in patients with STEMI. METHODS: Two cohorts of patients with STEMI were included. In the first cohort (n = 51), expression of arginase and NO-synthases as well as arginase 1 protein levels were determined and compared to a healthy control group (n = 45). In a second cohort (n = 68), plasma arginase 1 levels and infarct size were determined using cardiac magnetic resonance imaging. RESULTS: Expression of the gene encoding arginase 1 was significantly elevated at admission and 24-48 h after STEMI but not 3 months post STEMI, in comparison with the control group. Expression of the genes encoding arginase 2 and endothelial NO synthase (NOS3) were unaltered. Arginase 1 protein levels were elevated at admission, 24 h post STEMI and remained elevated for up to 6 months. No significant correlation between plasma arginase 1 protein levels and infarct size was observed. CONCLUSION: The markedly increased gene and protein expression of arginase 1 already at admission indicates a role of arginase 1 in the development of STEMI.

KLF12 Regulates Mouse NK Cell Proliferation.

NK cells are innate lymphocytes that play an integral role in tumor rejection and viral clearance. Unlike their other lymphocyte counterparts, NK cells have the unique ability to recognize and lyse target cells without prior exposure. However, there are no known NK cell-specific genes that are exclusively expressed by all NK cells. Therefore, identification of NK cell-specific genes would allow a better understanding of why NK cells are unique cytotoxic lymphocytes. From the Immunological Genome (ImmGen) Consortium studies, we identified kruppel-like factor 12 (Klf12), encoding a novel transcription factor, preferentially expressed in C57BL/6 mouse NK cells. KLF12 was dispensable for NK cell development, IFN-γ production, degranulation, and proliferation in Klf12 knockout mice. RNA-sequencing analysis revealed increased expression of Btg3, an antiproliferative gene, in KLF12-deficient NK cells compared with wild-type NK cells. Interestingly, competitive mixed bone marrow chimeric mice exhibited reduced development of KLF12-deficient NK cells, altered IFN-γ production and degranulation, and impairment of NK cell proliferation in vitro and in vivo in response to mouse CMV infection. KLF12-deficient NK cells from bone marrow chimeric mice also expressed higher levels of the IL-21R, which resulted in increased IL-21R signaling and correlated with greater inhibition of NK cell proliferation. Furthermore, IL-21 induced Btg3 expression, which correlated with arrested NK cell maturation and proliferation. In summary, we found that KLF12 regulates mouse NK cell proliferation potentially by regulating expression of Btg3 via IL-21.

Review and Consensus on Pharmacogenomic Testing in Psychiatry.

The implementation of pharmacogenomic (PGx) testing in psychiatry remains modest, in part due to divergent perceptions of the quality and completeness of the evidence base and diverse perspectives on the clinical utility of PGx testing among psychiatrists and other healthcare providers. Recognizing the current lack of consensus within the field, the International Society of Psychiatric Genetics assembled a group of experts to conduct a narrative synthesis of the PGx literature, prescribing guidelines, and product labels related to psychotropic medications as well as the key considerations and limitations related to the use of PGx testing in psychiatry. The group concluded that to inform medication selection and dosing of several commonly-used antidepressant and antipsychotic medications, current published evidence, prescribing guidelines, and product labels support the use of PGx testing for 2 cytochrome P450 genes (CYP2D6, CYP2C19). In addition, the evidence supports testing for human leukocyte antigen genes when using the mood stabilizers carbamazepine (HLA-A and HLA-B), oxcarbazepine (HLA-B), and phenytoin (CYP2C9, HLA-B). For valproate, screening for variants in certain genes (POLG, OTC, CSP1) is recommended when a mitochondrial disorder or a urea cycle disorder is suspected. Although barriers to implementing PGx testing remain to be fully resolved, the current trajectory of discovery and innovation in the field suggests these barriers will be overcome and testing will become an important tool in psychiatry.

EBI3 regulates the NK cell response to mouse cytomegalovirus infection.

Natural killer (NK) cells are key mediators in the control of cytomegalovirus infection. Here, we show that Epstein-Barr virus-induced 3 (EBI3) is expressed by human NK cells after NKG2D or IL-12 plus IL-18 stimulation and by mouse NK cells during mouse cytomegalovirus (MCMV) infection. The induction of EBI3 protein expression in mouse NK cells is a late activation event. Thus, early activation events of NK cells, such as IFNγ production and CD69 expression, were not affected in EBI3-deficient (Ebi3-/- ) C57BL/6 (B6) mice during MCMV infection. Furthermore, comparable levels of early viral replication in spleen and liver were observed in MCMV-infected Ebi3-/- and wild-type (WT) B6 mice. Interestingly, the viral load in salivary glands and oral lavage was strongly decreased in the MCMV-infected Ebi3-/- B6 mice, suggesting that EBI3 plays a role in the establishment of MCMV latency. We detected a decrease in the sustained IL-10 production by NK cells and lower serum levels of IL-10 in the MCMV-infected Ebi3-/- B6 mice. Furthermore, we observed an increase in dendritic cell maturation markers and an increase in activated CD8+ T cells. Thus, EBI3 dampens the immune response against MCMV infection, resulting in prolonged viral persistence.

Mapping of 79 loci for 83 plasma protein biomarkers in cardiovascular disease.

Recent advances in highly multiplexed immunoassays have allowed systematic large-scale measurement of hundreds of plasma proteins in large cohort studies. In combination with genotyping, such studies offer the prospect to 1) identify mechanisms involved with regulation of protein expression in plasma, and 2) determine whether the plasma proteins are likely to be causally implicated in disease. We report here the results of genome-wide association (GWA) studies of 83 proteins considered relevant to cardiovascular disease (CVD), measured in 3,394 individuals with multiple CVD risk factors. We identified 79 genome-wide significant (p<5e-8) association signals, 55 of which replicated at P<0.0007 in separate validation studies (n = 2,639 individuals). Using automated text mining, manual curation, and network-based methods incorporating information on expression quantitative trait loci (eQTL), we propose plausible causal mechanisms for 25 trans-acting loci, including a potential post-translational regulation of stem cell factor by matrix metalloproteinase 9 and receptor-ligand pairs such as RANK-RANK ligand. Using public GWA study data, we further evaluate all 79 loci for their causal effect on coronary artery disease, and highlight several potentially causal associations. Overall, a majority of the plasma proteins studied showed evidence of regulation at the genetic level. Our results enable future studies of the causal architecture of human disease, which in turn should aid discovery of new drug targets.

Genome-wide association analysis of self-reported events in 6135 individuals and 252 827 controls identifies 8 loci associated with thrombosis.

Thrombotic diseases are among the leading causes of morbidity and mortality in the world. To add insights into the genetic regulation of thrombotic disease, we conducted a genome-wide association study (GWAS) of 6135 self-reported blood clots events and 252 827 controls of European ancestry belonging to the 23andMe cohort of research participants. Eight loci exceeded genome-wide significance. Among the genome-wide significant results, our study replicated previously known venous thromboembolism (VTE) loci near the F5, FGA-FGG, F11, F2, PROCR and ABO genes, and the more recently discovered locus near SLC44A2 In addition, our study reports for the first time a genome-wide significant association between rs114209171, located upstream of the F8 structural gene, and thrombosis risk. Analyses of expression profiles and expression quantitative trait loci across different tissues suggested SLC44A2, ILF3 and AP1M2 as the three most plausible candidate genes for the chromosome 19 locus, our only genome-wide significant thrombosis-related locus that does not harbor likely coagulation-related genes. In addition, we present data showing that this locus also acts as a novel risk factor for stroke and coronary artery disease (CAD). In conclusion, our study reveals novel common genetic risk factors for VTE, stroke and CAD and provides evidence that self-reported data on blood clots used in a GWAS yield results that are comparable with those obtained using clinically diagnosed VTE. This observation opens up the potential for larger meta-analyses, which will enable elucidation of the genetics of thrombotic diseases, and serves as an example for the genetic study of other diseases.

Meta-analysis of 65,734 individuals identifies TSPAN15 and SLC44A2 as two susceptibility loci for venous thromboembolism.

Venous thromboembolism (VTE), the third leading cause of cardiovascular mortality, is a complex thrombotic disorder with environmental and genetic determinants. Although several genetic variants have been found associated with VTE, they explain a minor proportion of VTE risk in cases. We undertook a meta-analysis of genome-wide association studies (GWASs) to identify additional VTE susceptibility genes. Twelve GWASs totaling 7,507 VTE case subjects and 52,632 control subjects formed our discovery stage where 6,751,884 SNPs were tested for association with VTE. Nine loci reached the genome-wide significance level of 5 × 10(-8) including six already known to associate with VTE (ABO, F2, F5, F11, FGG, and PROCR) and three unsuspected loci. SNPs mapping to these latter were selected for replication in three independent case-control studies totaling 3,009 VTE-affected individuals and 2,586 control subjects. This strategy led to the identification and replication of two VTE-associated loci, TSPAN15 and SLC44A2, with lead risk alleles associated with odds ratio for disease of 1.31 (p = 1.67 × 10(-16)) and 1.21 (p = 2.75 × 10(-15)), respectively. The lead SNP at the TSPAN15 locus is the intronic rs78707713 and the lead SLC44A2 SNP is the non-synonymous rs2288904 previously shown to associate with transfusion-related acute lung injury. We further showed that these two variants did not associate with known hemostatic plasma markers. TSPAN15 and SLC44A2 do not belong to conventional pathways for thrombosis and have not been associated to other cardiovascular diseases nor related quantitative biomarkers. Our findings uncovered unexpected actors of VTE etiology and pave the way for novel mechanistic concepts of VTE pathophysiology.

ClusterSignificance: a bioconductor package facilitating statistical analysis of class cluster separations in dimensionality reduced data.

SUMMARY: Multi-dimensional data generated via high-throughput experiments is increasingly used in conjunction with dimensionality reduction methods to ascertain if resulting separations of the data correspond with known classes. This is particularly useful to determine if a subset of the variables, e.g. genes in a specific pathway, alone can separate samples into these established classes. Despite this, the evaluation of class separations is often subjective and performed via visualization. Here we present the ClusterSignificance package; a set of tools designed to assess the statistical significance of class separations downstream of dimensionality reduction algorithms. In addition, we demonstrate the design and utility of the ClusterSignificance package and utilize it to determine the importance of long non-coding RNA expression in the identity of multiple hematological malignancies. AVAILABILITY AND IMPLEMENTATION: ClusterSignificance is an R package available via Bioconductor (https://bioconductor.org/packages/ClusterSignificance) under GPL-3. CONTACT: dan.grander@ki.se. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Systematic approach demonstrates enrichment of multiple interactions between non-HLA risk variants and HLA-DRB1 risk alleles in rheumatoid arthritis.

OBJECTIVE: In anti-citrullinated protein antibody positive rheumatoid arthritis (ACPA-positive RA), a particular subset of HLA-DRB1 alleles, called shared epitope (SE) alleles, is a highly influential genetic risk factor. Here, we investigated whether non-HLA single nucleotide polymorphisms (SNP), conferring low disease risk on their own, interact with SE alleles more frequently than expected by chance and if such genetic interactions influence the HLA-DRB1 SE effect concerning risk to ACPA-positive RA. METHODS: We computed the attributable proportion (AP) due to additive interaction at genome-wide level for two independent ACPA-positive RA cohorts: the Swedish epidemiological investigation of rheumatoid arthritis (EIRA) and the North American rheumatoid arthritis consortium (NARAC). Then, we tested for differences in the AP p value distributions observed for two groups of SNPs, non-associated and associated with disease. We also evaluated whether the SNPs in interaction with HLA-DRB1 were cis-eQTLs in the SE alleles context in peripheral blood mononuclear cells from patients with ACPA-positive RA (SE-eQTLs). RESULTS: We found a strong enrichment of significant interactions (AP p<0.05) between the HLA-DRB1 SE alleles and the group of SNPs associated with ACPA-positive RA in both cohorts (Kolmogorov-Smirnov test D=0.35 for EIRA and D=0.25 for NARAC, p<2.2e-16 for both). Interestingly, 564 out of 1492 SNPs in consistent interaction for both cohorts were significant SE-eQTLs. Finally, we observed that the effect size of HLA-DRB1 SE alleles for disease decreases from 5.2 to 2.5 after removal of the risk alleles of the two top interacting SNPs (rs2476601 and rs10739581). CONCLUSION: Our data demonstrate that there are massive genetic interactions between the HLA-DRB1 SE alleles and non-HLA genetic variants in ACPA-positive RA.