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Identification of therapeutic targets from genome-wide association studies (GWAS) requires insights into downstream functional consequences. We harmonized 8,613 RNA-sequencing samples from 14 brain datasets to create the MetaBrain resource and performed cis- and trans-expression quantitative trait locus (eQTL) meta-analyses in multiple brain region- and ancestry-specific datasets (n ≤ 2,759). Many of the 16,169 cortex cis-eQTLs were tissue-dependent when compared with blood cis-eQTLs. We inferred brain cell types for 3,549 cis-eQTLs by interaction analysis. We prioritized 186 cis-eQTLs for 31 brain-related traits using Mendelian randomization and co-localization including 40 cis-eQTLs with an inferred cell type, such as a neuron-specific cis-eQTL (CYP24A1) for multiple sclerosis. We further describe 737 trans-eQTLs for 526 unique variants and 108 unique genes. We used brain-specific gene-co-regulation networks to link GWAS loci and prioritize additional genes for five central nervous system diseases. This study represents a valuable resource for post-GWAS research on central nervous system diseases.

Fine-mapping and functional studies implicate rs117701653, a common non-coding variant in theCD28/CTLA4/ICOSlocus, as a contributor to risk for rheumatoid arthritis and type 1 diabetes. Using DNA pulldown, mass spectrometry, genome editing and eQTL analysis, we establish that the disease-associated allele reduces affinity for the inhibitory chromosomal regulator SMCHD1 to drive expression of inducible T-cell costimulator (ICOS), enhancing memory CD4+T cell ICOS expression in individuals bearing the risk allele. Higher ICOS expression is paralleled by an increase in circulating T peripheral helper (Tph) cells, and in rheumatoid arthritis patients, of blood and joint fluid Tph cells and circulating plasmablasts, suggesting a causal link. Indeed, ICOS ligation accelerates T cell differentiation into CXCR5-PD-1highTph cells producing IL-21 and CXCL13, as does carriage of the rs117701653 risk allele. Thus, mechanistic dissection of a causal non-coding variant in human autoimmunity discloses a new pathway through which ICOS regulates Tph abundance.

Genetic testing in patients with suspected hereditary kidney disease may not reveal the genetic cause for the disorder as potentially pathogenic variants can reside in genes that are not yet known to be involved in kidney disease. To help identify these genes, we have developed KidneyNetwork, that utilizes tissue-specific expression to predict kidney-specific gene functions.KidneyNetwork is a novel method that we used to enrich a kidney RNA-sequencing co-expression network of 878 samples with a multi-tissue network of 31,499 samples. It then uses expression patterns to predict which genes have a kidney-related function and which (disease) phenotypes might result from variants in these genes, based on established gene-phenotype associations. We applied KidneyNetwork to prioritize rare variants in exome sequencing data from 13 kidney disease patients without a genetic diagnosis.KidneyNetwork can accurately predict kidney-specific gene functions and (kidney disease) phenotypes for disease-associated genes. Applying it to exome sequencing data of kidney disease patients allowed us to highlight a convincing candidate gene for kidney and liver cysts: ALG6.We present KidneyNetwork, a kidney-specific co-expression network that accurately predicts which genes have kidney-specific functions and can result in kidney disease. We show the added value of KidneyNetwork by applying it to kidney disease patients without a molecular diagnosis and consequently, we propose ALG6 as candidate gene in one of these patients. We designed an easy-to-use online interface that allows clinicians and researchers to use gene expression and co-regulation data and gene-phenotype connections to accelerate advances in hereditary kidney disease diagnosis and research.

Genetic variants identified through genome-wide association studies (GWAS) are typically non-coding and exert small regulatory effects on downstream genes, but which downstream genes are ultimately impacted and how they confer risk remains mostly unclear. Conversely, variants that cause rare Mendelian diseases are often coding and have a more direct impact on disease development. We demonstrate that common and rare genetic diseases can be linked by studying the gene regulatory networks impacted by common disease-associated variants. We implemented this in the ‘Downstreamer’ method and applied it to 44 GWAS traits and find that predicted downstream “key genes” are enriched with Mendelian disease genes, e.g. key genes for height are enriched for genes that cause skeletal abnormalities and Ehlers-Danlos syndromes. We find that 82% of these key genes are located outside of GWAS loci, suggesting that they result from complex trans regulation rather than being impacted by disease-associated variants in cis. Finally, we discuss the challenges in reconstructing gene regulatory networks and provide a roadmap to improve identification of these highly connected genes for common traits and diseases.

Despite significant progress in annotating the genome with experimental methods, much of the regulatory noncoding genome remains poorly defined. Here we assert that regulatory elements may be characterized by leveraging local epigenomic signatures where specific transcription factors (TFs) are bound. To link these two features, we introduce IMPACT, a genome annotation strategy that identifies regulatory elements defined by cell-state-specific TF binding profiles, learned from 515 chromatin and sequence annotations. We validate IMPACT using multiple compelling applications. First, IMPACT distinguishes between bound and unbound TF motif sites with high accuracy (average AUPRC 0.81, SE 0.07; across 8 tested TFs) and outperforms state-of-the-art TF binding prediction methods, MocapG, MocapS, and Virtual ChIP-seq. Second, in eight tested cell types, RNA polymerase II IMPACT annotations capture more cis-eQTL variation than sequence-based annotations, such as promoters and TSS windows (25% average increase in enrichment). Third, integration with rheumatoid arthritis (RA) summary statistics from European (N = 38,242) and East Asian (N = 22,515) populations revealed that the top 5% of CD4+ Treg IMPACT regulatory elements capture 85.7% of RA h2, the most comprehensive explanation for RA h2 to date. In comparison, the average RA h2 captured by compared CD4+ T histone marks is 42.3% and by CD4+ T specifically expressed gene sets is 36.4%. Lastly, we find that IMPACT may be used in many different cell types to identify complex trait associated regulatory elements.

Trait-associated genetic variants affect complex phenotypes primarily via regulatory mechanisms on the transcriptome. To investigate the genetics of gene expression, we performed cis- and trans-expression quantitative trait locus (eQTL) analyses using blood-derived expression from 31,684 individuals through the eQTLGen Consortium. We detected cis-eQTL for 88% of genes, and these were replicable in numerous tissues. Distal trans-eQTL (detected for 37% of 10,317 trait-associated variants tested) showed lower replication rates, partially due to low replication power and confounding by cell type composition. However, replication analyses in single-cell RNA-seq data prioritized intracellular trans-eQTL. Trans-eQTL exerted their effects via several mechanisms, primarily through regulation by transcription factors. Expression of 13% of the genes correlated with polygenic scores for 1,263 phenotypes, pinpointing potential drivers for those traits. In summary, this work represents a large eQTL resource, and its results serve as a starting point for in-depth interpretation of complex phenotypes.Analyses of expression profiles from whole blood of 31,684 individuals identify cis-expression quantitative trait loci (eQTL) effects for 88% of genes and trans-eQTL effects for 37% of trait-associated variants.

Epistasis has rarely been shown among natural polymorphisms in human traits; this research using advanced computation and gene expression data reveals many instances of epistasis between common single nucleotide polymorphisms in humans, with epistasis and the direction of its effect replicating in independent cohorts. Although frequently demonstrated in model organisms and domesticated species, very few examples of epistasis — where the effect of one polymorphism on a trait depends on other polymorphisms present in the genome — have been demonstrated in humans. Using advanced computation and a gene expression study design, these authors reveal hundreds of instances of epistasis between common single nucleotide polymorphisms (SNPs) in humans. They show that epistasis and direction of its effect replicate in independent cohorts. Epistatic networks of three or more SNPs are shown to influence the expression levels of many genes, whereby one cis-acting SNP is modulated by several trans-acting SNPs. Epistasis is the phenomenon whereby one polymorphism’s effect on a trait depends on other polymorphisms present in the genome. The extent to which epistasis influences complex traits1 and contributes to their variation2,3 is a fundamental question in evolution and human genetics. Although often demonstrated in artificial gene manipulation studies in model organisms4,5, and some examples have been reported in other species6, few examples exist for epistasis among natural polymorphisms in human traits7,8. Its absence from empirical findings may simply be due to low incidence in the genetic control of complex traits2,3, but an alternative view is that it has previously been too technically challenging to detect owing to statistical and computational issues9. Here we show, using advanced computation10 and a gene expression study design, that many instances of epistasis are found between common single nucleotide polymorphisms (SNPs). In a cohort of 846 individuals with 7,339 gene expression levels measured in peripheral blood, we found 501 significant pairwise interactions between common SNPs influencing the expression of 238 genes (P

The host's gene expression and gene regulatory response to pathogen exposure can be influenced by a combination of the host's genetic background, the type of and exposure time to pathogens. Here we provide a detailed dissection of this using single-cell RNA-sequencing of 1.3M peripheral blood mononuclear cells from 120 individuals, longitudinally exposed to three different pathogens. These analyses indicate that cell-type-specificity is a more prominent factor than pathogen-specificity regarding contexts that affect how genetics influences gene expression (i.e., eQTL) and co-expression (i.e., co-expression QTL). In monocytes, the strongest responder to pathogen stimulations, 71.4% of the genetic variants whose effect on gene expression is influenced by pathogen exposure (i.e., response QTL) also affect the co-expression between genes. This indicates widespread, context-specific changes in gene expression level and its regulation that are driven by genetics. Pathway analysis on the CLEC12A gene that exemplifies cell-type-, exposure-time- and genetic-background-dependent co-expression interactions, shows enrichment of the interferon (IFN) pathway specifically at 3-h post-exposure in monocytes. Similar genetic background-dependent association between IFN activity and CLEC12A co-expression patterns is confirmed in systemic lupus erythematosus by in silico analysis, which implies that CLEC12A might be an IFN-regulated gene. Altogether, this study highlights the importance of context for gaining a better understanding of the mechanisms of gene regulation in health and disease.

Gene expression and its regulation can be context-dependent. To dissect this, using samples from 120 individuals, we single-cell RNA-sequenced 1.3M peripheral blood mononuclear cells exposed to three different pathogens at two time points or left unexposed. This revealed thousands of cell type-specific expression changes (eQTLs) and pathogen-induced expression changes (response QTLs) that are influenced by genetic variation. In monocytes, the strongest responder to pathogen stimulations, genetics also affected co-expression of 71.4% of these eQTL genes. For example, the pathogen recognition receptor CLEC12A showed many such co-expression interactions, but only in monocytes after 3h pathogen stimulation. Further analysis linked this to interferon-regulating transcription factors, a finding that we recapitulated in an independent cohort of patients with systemic lupus erythematosus, a condition characterized by increased interferon activity. Altogether, this study highlights the importance of context for gaining a better understanding of the mechanisms of gene regulation in health and disease.

Background and Aims Genetic testing in patients with suspected hereditary kidney disease does not always reveal the genetic cause for the patient's disorder. Potentially pathogenic variants can reside in genes that are not known to be involved in kidney disease, which makes it difficult to prioritize and interpret the relevance of these variants. As such, there is a clear need for methods that predict the phenotypic consequences of gene expression in a way that is as unbiased as possible. To help identify candidate genes we have developed KidneyNetwork, in which tissue-specific expression is utilized to predict kidney-specific gene functions. Method We combined gene co-expression in 878 publicly available kidney RNA-sequencing samples with the co-expression of a multi-tissue RNA-sequencing dataset of 31,499 samples to build KidneyNetwork. The expression patterns were used to predict which genes have a kidney-related function, and which (disease) phenotypes might be caused when these genes are mutated. By integrating the information from the HPO database, in which known phenotypic consequences of disease genes are annotated, with the gene co-expression network we obtained prediction scores for each gene per HPO term. As proof of principle, we applied KidneyNetwork to prioritize variants in exome-sequencing data from 13 kidney disease patients without a genetic diagnosis. Results We assessed the prediction performance of KidneyNetwork by comparing it to GeneNetwork, a multi-tissue co-expression network we previously developed. In KidneyNetwork, we observe a significantly improved prediction accuracy of kidney-related HPO-terms, as well as an increase in the total number of significantly predicted kidney-related HPO-terms (figure 1). To examine its clinical utility, we applied KidneyNetwork to 13 patients with a suspected hereditary kidney disease without a genetic diagnosis. Based on the HPO terms “Renal cyst” and “Hepatic cysts”, combined with a list of potentially damaging variants in one of the undiagnosed patients with mild ADPKD/PCLD, we identified ALG6 as a new candidate gene. ALG6 bears a high resemblance to other genes implicated in this phenotype in recent years. Through the 100,000 Genomes Project and collaborators we identified three additional patients with kidney and/or liver cysts carrying a suspected deleterious variant in ALG6. Conclusion We present KidneyNetwork, a kidney specific co-expression network that accurately predicts what genes have kidney-specific functions and may result in kidney disease. Gene-phenotype associations of genes unknown for kidney-related phenotypes can be predicted by KidneyNetwork. We show the added value of KidneyNetwork by applying it to exome sequencing data of kidney disease patients without a molecular diagnosis and consequently we propose ALG6 as a promising candidate gene. KidneyNetwork can be applied to clinically unsolved kidney disease cases, but it can also be used by researchers to gain insight into individual genes to better understand kidney physiology and pathophysiology. Acknowledgments This research was made possible through access to the data and findings generated by the 100,000 Genomes Project; http://www.genomicsengland.co.uk.

BackgroundGenetic testing in patients with suspected hereditary kidney disease may not reveal the genetic cause for the disorder as potentially pathogenic variants can reside in genes that are not yet known to be involved in kidney disease. To help identify these genes, we have developed KidneyNetwork, that utilizes tissue-specific expression to predict kidney-specific gene functions.MethodsKidneyNetwork is a co-expression network built upon a combination of 878 kidney RNA-sequencing samples and a multi-tissue dataset of 31,499 samples. It uses expression patterns to predict which genes have a kidney-related function and which (disease) phenotypes might result from variants in these genes. We applied KidneyNetwork to prioritize rare variants in exome sequencing data from 13 kidney disease patients without a genetic diagnosis.ResultsKidneyNetwork can accurately predict kidney-specific gene functions and (kidney disease) phenotypes for disease-associated genes. Applying it to exome sequencing data of kidney disease patients allowed us to identify a promising candidate gene for kidney and liver cysts: ALG6.ConclusionWe present KidneyNetwork, a kidney-specific co-expression network that accurately predicts which genes have kidney-specific functions and can result in kidney disease. We show the added value of KidneyNetwork by applying it to kidney disease patients without a molecular diagnosis and consequently, we propose ALG6 as candidate gene in one of these patients. KidneyNetwork can be applied to clinically unsolved kidney disease cases, but it can also be used by researchers to gain insight into individual genes in order to better understand kidney physiology and pathophysiology.Significance statementGenetic testing in patients with suspected hereditary kidney disease may not reveal the genetic cause for the patient’s disorder. Potentially pathogenic variants can reside in genes not yet known to be involved in kidney disease, making it difficult to interpret the relevance of these variants. This reveals a clear need for methods to predict the phenotypic consequences of genetic variation in an unbiased manner. Here we describe KidneyNetwork, a tool that utilizes tissue-specific expression to predict kidney-specific gene functions. Applying KidneyNetwork to a group of undiagnosed cases identified ALG6 as a candidate gene in cystic kidney and liver disease. In summary, KidneyNetwork can aid the interpretation of genetic variants and can therefore be of value in translational nephrogenetics and help improve the diagnostic yield in kidney disease patients.

Gaining insight into the downstream consequences of non-coding variants is an essential step towards the identification of therapeutic targets from genome-wide association study (GWAS) findings. Here we have harmonized and integrated 8,727 RNA-seq samples with accompanying genotype data from multiple brain-regions from 14 datasets. This sample size enabled us to perform both cis- and trans-expression quantitative locus (eQTL) mapping. Upon comparing the brain cortex cis-eQTLs (for 12,307 unique genes at FDRcis-eQTL analysis (n=31,684 samples), we observed that brain eQTLs are more tissue specific than previously assumed.We inferred the brain cell type for 1,515 cis-eQTLs by using cell type proportion information. We conducted Mendelian Randomization on 31 brain-related traits using cis-eQTLs as instruments and found 159 significant findings that also passed colocalization. Furthermore, two multiple sclerosis (MS) findings had cell type specific signals, a neuron-specific cis-eQTL for CYP24A1 and a macrophage specific cis-eQTL for CLECL1.To further interpret GWAS hits, we performed trans-eQTL analysis. We identified 2,589 trans-eQTLs (at FDRWe also generated a brain-specific gene-coregulation network that we used to predict which genes have brain-specific functions, and to perform a novel network analysis of Alzheimer’s disease (AD), amyotrophic lateral sclerosis (ALS), multiple sclerosis (MS) and Parkinson’s disease (PD) GWAS data. This resulted in the identification of distinct sets of genes that show significantly enriched co-regulation with genes inside the associated GWAS loci, and which might reflect drivers of these diseases.

Contains fulltext : 231681.pdf (Publisher’s version ) (Closed access) To study the effect of host genetics on gut microbiome composition, the MiBioGen consortium curated and analyzed genome-wide genotypes and 16S fecal microbiome data from 18,340 individuals (24 cohorts). Microbial composition showed high variability across cohorts: only 9 of 410 genera were detected in more than 95% of samples. A genome-wide association study of host genetic variation regarding microbial taxa identified 31 loci affecting the microbiome at a genome-wide significant (P

Additional file 11: Figure S1A. Heatmap of extended model predictors. A heatmap of Spearman correlations between all cell type predictors used in the extended model (EM). Figure S1B. Correlations of selected variable counts with age. The Spearman correlations of selected variables - including measured or imputed cell counts - with age are presented, colored per cohort (see legend). See Results section for details. Figure S2. Correlation of Z-scores associated with IM and EM genes. A Pearson correlation of the Z-scores associated with both significant and not significant IM and EM genes is shown. The 45° diagonal is presented as dashed, the correlation line is in red. See Results section for details. Figure S3. Cohort-related, gene-specific ρ values. A) QQ plots used to evaluate the distribution pattern of cohort-related, gene-specific ρ values. B) Gene expression residuals decrease with the EM. Homoscedasticity was evaluated by correlating gene expression residuals from every model with age, and the absolute Spearman ⍴ values obtained after meta-analysis are reported for all genes minus the shared genes significantly associated with aging. See Figure 2 in the main text and Methods for details. Statistical significance was assessed with a paired, one-tailed Wilcoxon test. The stars indicate statistical significance: *** P ≤ 0.001, ** P ≤ 0.01, * P ≤ 0.05. LL, LifeLines DEEP; LLS, Leiden Longevity Study; NTR, Netherlands Twin Registry; RS, Rotterdam Study; EM, extended model; IM, initial model; IM no age, IM without age as covariate; EM no age, EM without age as covariate. Figure S4. scRNA-seq data-derived t-SNE plots reveal that IM-related aging genes are more likely cell type-specific marker genes. Mean expression levels of cell type marker genes among aging-related genes identified in the Initial Model (IM, left) and in the Extended Model (EM, right) are plotted. Where applicable, IM- and EM-related intensities for same cell types plots were compared through a Wilcoxon test, always observing a P ≤ 2.2⨯10−16. For details regarding cell population-specific regions, refer to [20]. Figure S5. Heatmap of gene expression residuals correlations for EM downregulated aging-related genes. Downregulated EM aging-related genes were clustered based on the correlations of gene expression residuals and highly correlating clusters were identified and highlighted with a yellow border. See Results section for details. Figure S6. Expression values of platelet-related genes across age. The four cohorts are colored separately and the Spearman correlations and P-values are calculated for each cohort independently. Figure S7. Correlations between (residual) gene expression levels of the genes from platelet-related cluster 1 and measured platelet levels. A) Spearman correlations between gene expression levels and measured platelets, B) Spearman correlations between gene expression residuals from the extended model and measured platelets. Figure S8. scRNA-seq data-derived t-SNE plots reveal that PF4 and PPBP are specifically expressed in megakaryocytes. Mean expression levels of platelet marker genes PF4 and PPBP are plotted. For details regarding cell population-specific regions, refer to [20].

BACKGROUND: Recent genome-wide association studies (GWASs) identified the first genetic loci associated with attention-deficit/hyperactivity disorder (ADHD) and autism spectrum disorder (ASD). The next step is to use these results to increase our understanding of the biological mechanisms involved. Most of the identified variants likely influence gene regulation. The aim of the current study is to shed light on the mechanisms underlying the genetic signals and prioritize genes by integrating GWAS results with gene expression and DNA methylation (DNAm) levels.METHODS: We applied summary-data-based Mendelian randomization to integrate ADHD and ASD GWAS data with fetal brain expression and methylation quantitative trait loci, given the early onset of these disorders. We also analyzed expression and methylation quantitative trait loci datasets of adult brain and blood, as these provide increased statistical power. We subsequently used summary-data-based Mendelian randomization to investigate if the same variant influences both DNAm and gene expression levels.RESULTS: We identified multiple gene expression and DNAm levels in fetal brain at chromosomes 1 and 17 that were associated with ADHD and ASD, respectively, through pleiotropy at shared genetic variants. The analyses in brain and blood showed additional associated gene expression and DNAm levels at the same and additional loci, likely because of increased statistical power. Several of the associated genes have not been identified in ADHD and ASD GWASs before.CONCLUSIONS: Our findings identified the genetic variants associated with ADHD and ASD that likely act through gene regulation. This facilitates prioritization of candidate genes for functional follow-up studies.

To study the effect of host genetics on gut microbiome composition, the MiBioGen consortium curated and analyzed genome-wide genotypes and 16S fecal microbiome data from 18,340 individuals (24 cohorts). Microbial composition showed high variability across cohorts: only 9 out of 410 genera were detected in more than 95% samples. A genome-wide association study (GWAS) of host genetic variation in relation to microbial taxa identified 31 loci affecting microbiome at a genome-wide significant (P−8) threshold. One locus, the lactase (LCT) gene locus, reached study-wide significance (GWAS signal P=1.28×10−20), and it showed an age-dependent association with Bifidobacterium abundance. Other associations were suggestive (1.95×10−10−8) but enriched for taxa showing high heritability and for genes expressed in the intestine and brain. A phenome-wide association study and Mendelian randomization identified enrichment of microbiome trait loci in the metabolic, nutrition and environment domains and suggested the microbiome has causal effects in ulcerative colitis and rheumatoid arthritis.

Background: Exposure to environmental endocrine disrupting chemicals (EDCs) may play an important role in the epidemic of metabolic diseases. Epigenetic alterations may functionally link EDCs with gene expression and metabolic traits. Objectives: We aimed to evaluate metabolic-related effects of the exposure to endocrine disruptors including five parabens, three bisphenols, and 13 metabolites of nine phthalates as measured in 24-hour urine on epigenome-wide DNA methylation. Methods: A blood-based epigenome-wide association study was performed in 622 participants from the Lifelines DEEP cohort using Illumina Infinium HumanMethylation450 methylation data and EDC excretions in 24-hour urine. Out of the 21 EDCs, 13 compounds were detected in >75% of the samples and, together with bisphenol F, were included in these analyses. Furthermore, we explored the putative function of identified methylation markers and their correlations with metabolic traits. Results: We found 20 differentially methylated cytosine-phosphate-guanines (CpGs) associated with 10 EDCs at suggestive p-value < 1 × 10−6, of which four, associated with MEHP and MEHHP, were genome-wide significant (Bonferroni-corrected p-value < 1.19 × 10−7). Nine out of 20 CpGs were significantly associated with at least one of the tested metabolic traits, such as fasting glucose, glycated hemoglobin, blood lipids, and/or blood pressure. 18 out of 20 EDC-associated CpGs were annotated to genes functionally related to metabolic syndrome, hypertension, obesity, type 2 diabetes, insulin resistance and glycemic traits. Conclusions: The identified DNA methylation markers for exposure to the most common EDCs provide suggestive mechanism underlying the contributions of EDCs to metabolic health. Follow-up studies are needed to unravel the causality of EDC-induced methylation changes in metabolic alterations.

Circulating proteins are vital in human health and disease and are frequently used as biomarkers for clinical decision-making or as targets for pharmacological intervention. By mapping and replicating protein quantitative trait loci (pQTL) for 90 cardiovascular proteins in over 30,000 individuals, we identified 467 pQTLs for 85 proteins. The pQTLs were used in combination with other sources of information to evaluate known drug targets, and suggest new target candidates or repositioning opportunities, underpinned by a) causality assessment using Mendelian randomization, b) pathway mapping usingtrans-pQTL gene assignments, and c) protein-centric polygenic risk scores enabling matching of plausible target mechanisms to sub-groups of individuals enabling precision medicine.

Genome-wide association studies (GWAS) have identified many disease-associated noncoding variants, but cannot distinguish functional single-nucleotide polymorphisms (fSNPs) from others that reside incidentally within risk loci. To address this challenge, we developed an unbiased high-throughput screen that employs type IIS enzymatic restriction to identify fSNPs that allelically modulate the binding of regulatory proteins. We coupled this approach, termed SNP-seq, with flanking restriction enhanced pulldown (FREP) to identify regulation of CD40 by three disease-associated fSNPs via four regulatory proteins, RBPJ, RSRC2 and FUBP-1/TRAP150. Applying this approach across 27 loci associated with juvenile idiopathic arthritis, we identified 148 candidate fSNPs, including two that regulate STAT4 via the regulatory proteins SATB2 and H1.2. Together, these findings establish the utility of tandem SNP-seq/FREP to bridge the gap between GWAS and disease mechanism.