Complement genes contribute sex-biased vulnerability in diverse disorders.

Many common illnesses, for reasons that have not been identified, differentially affect men and women. For instance, the autoimmune diseases systemic lupus erythematosus (SLE) and Sjögren's syndrome affect nine times more women than men1, whereas schizophrenia affects men with greater frequency and severity relative to women2. All three illnesses have their strongest common genetic associations in the major histocompatibility complex (MHC) locus, an association that in SLE and Sjögren's syndrome has long been thought to arise from alleles of the human leukocyte antigen (HLA) genes at that locus3-6. Here we show that variation of the complement component 4 (C4) genes C4A and C4B, which are also at the MHC locus and have been linked to increased risk for schizophrenia7, generates 7-fold variation in risk for SLE and 16-fold variation in risk for Sjögren's syndrome among individuals with common C4 genotypes, with C4A protecting more strongly than C4B in both illnesses. The same alleles that increase risk for schizophrenia greatly reduce risk for SLE and Sjögren's syndrome. In all three illnesses, C4 alleles act more strongly in men than in women: common combinations of C4A and C4B generated 14-fold variation in risk for SLE, 31-fold variation in risk for Sjögren's syndrome, and 1.7-fold variation in schizophrenia risk among men (versus 6-fold, 15-fold and 1.26-fold variation in risk among women, respectively). At a protein level, both C4 and its effector C3 were present at higher levels in cerebrospinal fluid and plasma8,9 in men than in women among adults aged between 20 and 50 years, corresponding to the ages of differential disease vulnerability. Sex differences in complement protein levels may help to explain the more potent effects of C4 alleles in men, women's greater risk of SLE and Sjögren's syndrome and men's greater vulnerability to schizophrenia. These results implicate the complement system as a source of sexual dimorphism in vulnerability to diverse illnesses.

Genome-wide association study identifies multiple HLA loci for sarcoidosis susceptibility.

Sarcoidosis is a complex systemic disease. Our study aimed to (1) identify novel alleles associated with sarcoidosis susceptibility; (2) provide an in-depth evaluation of HLA alleles and sarcoidosis susceptibility and (3) integrate genetic and transcription data to identify risk loci that may more directly impact disease pathogenesis. We report a genome-wide association study of 1335 sarcoidosis cases and 1264 controls of European descent (EA) and investigate associated alleles in a study of African Americans (AA: 1487 cases and 1504 controls). The EA and AA cohort was recruited from multiple United States sites. HLA alleles were imputed and tested for association with sarcoidosis susceptibility. Expression quantitative locus and colocalization analysis were performed using a subset of subjects with transcriptome data. Forty-nine SNPs in the HLA region in HLA-DRA, -DRB9, -DRB5, -DQA1 and BRD2 genes were significantly associated with sarcoidosis susceptibility in EA, rs3129888 was also a risk variant for sarcoidosis in AA. Classical HLA alleles DRB1*0101, DQA1*0101 and DQB1*0501, which are highly correlated, were also associated with sarcoidosis. rs3135287 near HLA-DRA was associated with HLA-DRA expression in peripheral blood mononuclear cells and bronchoalveolar lavage from subjects and lung tissue and whole blood from GTEx. We identified six novel SNPs (out of the seven SNPs representing the 49 significant SNPs) and nine HLA alleles associated with sarcoidosis susceptibility in the largest EA population. We also replicated our findings in an AA population. Our study reiterates the potential role of antigen recognition and/or presentation HLA class II genes in sarcoidosis pathogenesis.

Deep Resequencing of the 1q22 Locus in Non-Lobar Intracerebral Hemorrhage.

OBJECTIVE: Genome-wide association studies have identified 1q22 as a susceptibility locus for cerebral small vessel diseases, including non-lobar intracerebral hemorrhage (ICH) and lacunar stroke. In the present study, we performed targeted high-depth sequencing of 1q22 in ICH cases and controls to further characterize this locus and prioritize potential causal mechanisms, which remain unknown. METHODS: A total of 95,000 base pairs spanning 1q22, including SEMA4A, SLC25A44, and PMF1/PMF1-BGLAP were sequenced in 1,055 spontaneous ICH cases (534 lobar and 521 non-lobar) and 1,078 controls. Firth regression and Rare Variant Influential Filtering Tool analysis were used to analyze common and rare variants, respectively. Chromatin interaction analyses were performed using Hi-C, chromatin immunoprecipitation followed by sequencing, and chromatin interaction analysis with paired-end tag databases. Multivariable Mendelian randomization assessed whether alterations in gene-specific expression relative to regionally co-expressed genes at 1q22 could be causally related to ICH risk. RESULTS: Common and rare variant analyses prioritized variants in SEMA4A 5'-UTR and PMF1 intronic regions, overlapping with active promoter and enhancer regions based on ENCODE annotation. Hi-C data analysis determined that 1q22 is spatially organized in a single chromatin loop, and that the genes therein belong to the same topologically associating domain. Chromatin immunoprecipitation followed by sequencing and chromatin interaction analysis with paired-end tag data analysis highlighted the presence of long-range interactions between the SEMA4A-promoter and PMF1-enhancer regions prioritized by association testing. Multivariable Mendelian randomization analyses demonstrated that PMF1 overexpression could be causally related to non-lobar ICH risk. INTERPRETATION: Altered promoter-enhancer interactions leading to PMF1 overexpression, potentially dysregulating polyamine catabolism, could explain demonstrated associations with non-lobar ICH risk at 1q22, offering a potential new target for prevention of ICH and cerebral small vessel disease. ANN NEUROL 2024;95:325-337.

COVID-19 and systemic lupus erythematosus genetics: A balance between autoimmune disease risk and protection against infection.

Genome wide association studies show there is a genetic component to severe COVID-19. We find evidence that the genome-wide genetic association signal with severe COVID-19 is correlated with that of systemic lupus erythematosus (SLE), having formally tested this using genetic correlation analysis by LD score regression. To identify the shared associated loci and gain insight into the shared genetic effects, using summary level data we performed meta-analyses, a local genetic correlation analysis and fine-mapping using stepwise regression and functional annotation. This identified multiple loci shared between the two traits, some of which exert opposing effects. The locus with most evidence of shared association is TYK2, a gene critical to the type I interferon pathway, where the local genetic correlation is negative. Another shared locus is CLEC1A, where the direction of effects is aligned, that encodes a lectin involved in cell signaling, and the anti-fungal immune response. Our analyses suggest that several loci with reciprocal effects between the two traits have a role in the defense response pathway, adding to the evidence that SLE risk alleles are protective against infection.

A Genomic Risk Score Identifies Individuals at High Risk for Intracerebral Hemorrhage.

BACKGROUND: Intracerebral hemorrhage (ICH) has an estimated heritability of 29%. We developed a genomic risk score for ICH and determined its predictive power in comparison to standard clinical risk factors. METHODS: We combined genome-wide association data from individuals of European ancestry for ICH and related traits in a meta-genomic risk score ([metaGRS]; 2.6 million variants). We tested associations with ICH and its predictive performance in addition to clinical risk factors in a held-out validation dataset (842 cases and 796 controls). We tested associations with risk of incident ICH in the population-based UK Biobank cohort (486 784 individuals, 1526 events, median follow-up 11.3 years). RESULTS: One SD increment in the metaGRS was significantly associated with 31% higher odds for ICH (95% CI, 1.16-1.48) in age-, sex- and clinical risk factor-adjusted models. The metaGRS identified individuals with almost 5-fold higher odds for ICH in the top score percentile (odds ratio, 4.83 [95% CI, 1.56-21.2]). Predictive models for ICH incorporating the metaGRS in addition to clinical predictors showed superior performance compared to the clinical risk factors alone (c-index, 0.695 versus 0.686). The metaGRS showed similar associations for lobar and nonlobar ICH, independent of the known APOE risk locus for lobar ICH. In the UK Biobank, the metaGRS was associated with higher risk of incident ICH (hazard ratio, 1.15 [95% CI, 1.09-1.21]). The associations were significant within both a relatively high-risk population of antithrombotic medications users, as well as among a relatively low-risk population with a good control of vascular risk factors and no use of anticoagulants. CONCLUSIONS: We developed and validated a genomic risk score that predicts lifetime risk of ICH beyond established clinical risk factors among individuals of European ancestry. Whether implementation of the score in risk prognostication models for high-risk populations, such as patients under antithrombotic treatment, could improve clinical decision making should be explored in future studies.

Identification of influential rare variants in aggregate testing using random forest importance measures.

Aggregate tests of rare variants are often employed to identify associated regions compared to sequentially testing each individual variant. When an aggregate test is significant, it is of interest to identify which rare variants are "driving" the association. We recently developed the rare variant influential filtering tool (RIFT) to identify influential rare variants and showed RIFT had higher true positive rates compared to other published methods. Here we use importance measures from the standard random forest (RF) and variable importance weighted RF (vi-RF) to identify influential variants. For very rare variants (minor allele frequency [MAF] < 0.001), the vi-RF:Accuracy method had the highest median true positive rate (TPR = 0.24; interquartile range [IQR]: 0.13, 0.42) followed by the RF:Accuracy method (TPR = 0.16; IQR: 0.07, 0.33) and both were superior to RIFT (TPR = 0.05; IQR: 0.02, 0.15). Among uncommon variants (0.001 < MAF < 0.03), the RF methods had higher true positive rates than RIFT while observing comparable false positive rates. Finally, we applied the RF methods to a targeted resequencing study in idiopathic pulmonary fibrosis (IPF), in which the vi-RF approach identified eight and seven variants in TERT and FAM13A, respectively. In summary, the vi-RF provides an improved, objective approach to identifying influential variants following a significant aggregate test. We have expanded our previously developed R package RIFT to include the random forest methods.

Analysis of Trans-Ancestral SLE Risk Loci Identifies Unique Biologic Networks and Drug Targets in African and European Ancestries.

Systemic lupus erythematosus (SLE) is a multi-organ autoimmune disorder with a prominent genetic component. Individuals of African ancestry (AA) experience the disease more severely and with an increased co-morbidity burden compared to European ancestry (EA) populations. We hypothesize that the disparities in disease prevalence, activity, and response to standard medications between AA and EA populations is partially conferred by genomic influences on biological pathways. To address this, we applied a comprehensive approach to identify all genes predicted from SNP-associated risk loci detected with the Immunochip. By combining genes predicted via eQTL analysis, as well as those predicted from base-pair changes in intergenic enhancer sites, coding-region variants, and SNP-gene proximity, we were able to identify 1,731 potential ancestry-specific and trans-ancestry genetic drivers of SLE. Gene associations were linked to upstream and downstream regulators using connectivity mapping, and predicted biological pathways were mined for candidate drug targets. Examination of trans-ancestral pathways reflect the well-defined role for interferons in SLE and revealed pathways associated with tissue repair and remodeling. EA-dominant genetic drivers were more often associated with innate immune and myeloid cell function pathways, whereas AA-dominant pathways mirror clinical findings in AA subjects, suggesting disease progression is driven by aberrant B cell activity accompanied by ER stress and metabolic dysfunction. Finally, potential ancestry-specific and non-specific drug candidates were identified. The integration of all SLE SNP-predicted genes into functional pathways revealed critical molecular pathways representative of each population, underscoring the influence of ancestry on disease mechanism and also providing key insight for therapeutic selection.

Sex differences in human adipose tissue gene expression and genetic regulation involve adipogenesis.

Sex differences in adipose tissue distribution and function are associated with sex differences in cardiometabolic disease. While many studies have revealed sex differences in adipocyte cell signaling and physiology, there is a relative dearth of information regarding sex differences in transcript abundance and regulation. We investigated sex differences in subcutaneous adipose tissue transcriptional regulation using omic-scale data from ∼3000 geographically and ethnically diverse human samples. We identified 162 genes with robust sex differences in expression. Differentially expressed genes were implicated in oxidative phosphorylation and adipogenesis. We further determined that sex differences in gene expression levels could be related to sex differences in the genetics of gene expression regulation. Our analyses revealed sex-specific genetic associations, and this finding was replicated in a study of 98 inbred mouse strains. The genes under genetic regulation in human and mouse were enriched for oxidative phosphorylation and adipogenesis. Enrichment analysis showed that the associated genetic loci resided within binding motifs for adipogenic transcription factors (e.g., PPARG and EGR1). We demonstrated that sex differences in gene expression could be influenced by sex differences in genetic regulation for six genes (e.g., FADS1 and MAP1B). These genes exhibited dynamic expression patterns during adipogenesis and robust expression in mature human adipocytes. Our results support a role for adipogenesis-related genes in subcutaneous adipose tissue sex differences in the genetic and environmental regulation of gene expression.

Metabolomic profiling of glucose homeostasis in African Americans: the Insulin Resistance Atherosclerosis Family Study (IRAS-FS).

INTRODUCTION: African Americans are at increased risk for type 2 diabetes. OBJECTIVES: This work aimed to examine metabolomic signature of glucose homeostasis in African Americans. METHODS: We used an untargeted liquid chromatography-mass spectrometry metabolomic approach to comprehensively profile 727 plasma metabolites among 571 African Americans from the Insulin Resistance Atherosclerosis Family Study (IRAS-FS) and investigate the associations between these metabolites and both the dynamic (SI, insulin sensitivity; AIR, acute insulin response; DI, disposition index; and SG, glucose effectiveness) and basal (HOMA-IR and HOMA-B) measures of glucose homeostasis using univariate and regularized regression models. We also compared the results with our previous findings in the IRAS-FS Mexican Americans. RESULTS: We confirmed increased plasma metabolite levels of branched-chain amino acids and their metabolic derivatives, 2-aminoadipate, 2-hydroxybutyrate, glutamate, arginine and its metabolic derivatives, carbohydrate metabolites, and medium- and long-chain fatty acids were associated with insulin resistance, while increased plasma metabolite levels in the glycine, serine and threonine metabolic pathway were associated with insulin sensitivity. We also observed a differential ancestral effect of glutamate on glucose homeostasis with significantly stronger effects observed in African Americans than those previously observed in Mexican Americans. CONCLUSION: We extended the observations that metabolites are useful biomarkers in the identification of prediabetes in individuals at risk of type 2 diabetes in African Americans. We revealed, for the first time, differential ancestral effect of certain metabolites (i.e., glutamate) on glucose homeostasis traits. Our study highlights the need for additional comprehensive metabolomic studies in well-characterized multiethnic cohorts.

Transcriptome-wide analyses of adipose tissue in outbred rats reveal genetic regulatory mechanisms relevant for human obesity.

Transcriptomic analysis in metabolically active tissues allows a systems genetics approach to identify causal genes and networks involved in metabolic disease. Outbred heterogeneous stock (HS) rats are used for genetic mapping of complex traits, but to-date, a systems genetics analysis of metabolic tissues has not been done. We investigated whether adiposity-associated genes and gene coexpression networks in outbred heterogeneous stock (HS) rats overlap those found in humans. We analyzed RNAseq data from adipose tissue of 415 male HS rats, correlated these transcripts with body weight (BW) and compared transcriptome signatures to two human cohorts: the "African American Genetics of Metabolism and Expression" and "Metabolic Syndrome in Men." We used weighted gene coexpression network analysis to identify adiposity-associated gene networks and mediation analysis to identify genes under genetic control whose expression drives adiposity. We identified 554 orthologous "consensus genes" whose expression correlates with BW in the rat and with body mass index (BMI) in both human cohorts. Consensus genes fell within eight coexpressed networks and were enriched for genes involved in immune system function, cell growth, extracellular matrix organization, and lipid metabolic processes. We identified 19 consensus genes for which genetic variation may influence BW via their expression, including those involved in lipolysis (e.g., Hcar1), inflammation (e.g., Rgs1), adipogenesis (e.g., Tmem120b), or no previously known role in obesity (e.g., St14 and Ms4a6a). Strong concordance between HS rat and human BW/BMI associated transcripts demonstrates translational utility of the rat model, while identification of novel genes expands our knowledge of the genetics underlying obesity.

Antifibrotic factor KLF4 is repressed by the miR-10/TFAP2A/TBX5 axis in dermal fibroblasts: insights from twins discordant for systemic sclerosis.

OBJECTIVES: Systemic sclerosis (SSc) is a complex disease of unknown aetiology in which inflammation and fibrosis lead to multiple organ damage. There is currently no effective therapy that can halt the progression of fibrosis or reverse it, thus studies that provide novel insights into disease pathogenesis and identify novel potential therapeutic targets are critically needed. METHODS: We used global gene expression and genome-wide DNA methylation analyses of dermal fibroblasts (dFBs) from a unique cohort of twins discordant for SSc to identify molecular features of this pathology. We validated the findings using in vitro, ex vivo and in vivo models. RESULTS: Our results revealed distinct differentially expressed and methylated genes, including several transcription factors involved in stem cell differentiation and developmental programmes (KLF4, TBX5, TFAP2A and homeobox genes) and the microRNAs miR-10a and miR-10b which target several of these deregulated genes. We show that KLF4 expression is reduced in SSc dFBs and its expression is repressed by TBX5 and TFAP2A. We also show that KLF4 is antifibrotic, and its conditional knockout in fibroblasts promotes a fibrotic phenotype. CONCLUSIONS: Our data support a role for epigenetic dysregulation in mediating SSc susceptibility in dFBs, illustrating the intricate interplay between CpG methylation, miRNAs and transcription factors in SSc pathogenesis, and highlighting the potential for future use of epigenetic modifiers as therapies.

Intrinsic DNA topology as a prioritization metric in genomic fine-mapping studies.

In genomic fine-mapping studies, some approaches leverage annotation data to prioritize likely functional polymorphisms. However, existing annotation resources can present challenges as many lack information for novel variants and/or may be uninformative for non-coding regions. We propose a novel annotation source, sequence-dependent DNA topology, as a prioritization metric for fine-mapping. DNA topology and function are well-intertwined, and as an intrinsic DNA property, it is readily applicable to any genomic region. Here, we constructed and applied Minor Groove Width (MGW) as a prioritization metric. Using an established MGW-prediction method, we generated a MGW census for 199 038 197 SNPs across the human genome. Summarizing a SNP's change in MGW (ΔMGW) as a Euclidean distance, ΔMGW exhibited a strongly right-skewed distribution, highlighting the infrequency of SNPs that generate dissimilar shape profiles. We hypothesized that phenotypically-associated SNPs can be prioritized by ΔMGW. We tested this hypothesis in 116 regions analyzed by a Massively Parallel Reporter Assay and observed enrichment of large ΔMGW for functional polymorphisms (P = 0.0007). To illustrate application in fine-mapping studies, we applied our MGW-prioritization approach to three non-coding regions associated with systemic lupus erythematosus. Together, this study presents the first usage of sequence-dependent DNA topology as a prioritization metric in genomic association studies.

Identification of Influential Variants in Significant Aggregate Rare Variant Tests.

INTRODUCTION: Studies that examine the role of rare variants in both simple and complex disease are increasingly common. Though the usual approach of testing rare variants in aggregate sets is more powerful than testing individual variants, it is of interest to identify the variants that are plausible drivers of the association. We present a novel method for prioritization of rare variants after a significant aggregate test by quantifying the influence of the variant on the aggregate test of association. METHODS: In addition to providing a measure used to rank variants, we use outlier detection methods to present the computationally efficient Rare Variant Influential Filtering Tool (RIFT) to identify a subset of variants that influence the disease association. We evaluated several outlier detection methods that vary based on the underlying variance measure: interquartile range (Tukey fences), median absolute deviation, and SD. We performed 1,000 simulations for 50 regions of size 3 kb and compared the true and false positive rates. We compared RIFT using the Inner Tukey to 2 existing methods: adaptive combination of p values (ADA) and a Bayesian hierarchical model (BeviMed). Finally, we applied this method to data from our targeted resequencing study in idiopathic pulmonary fibrosis (IPF). RESULTS: All outlier detection methods observed higher sensitivity to detect uncommon variants (0.001 < minor allele frequency, MAF > 0.03) compared to very rare variants (MAF <0.001). For uncommon variants, RIFT had a lower median false positive rate compared to the ADA. ADA and RIFT had significantly higher true positive rates than that observed for BeviMed. When applied to 2 regions found previously associated with IPF including 100 rare variants, we identified 6 polymorphisms with the greatest evidence for influencing the association with IPF. DISCUSSION: In summary, RIFT has a high true positive rate while maintaining a low false positive rate for identifying polymorphisms influencing rare variant association tests. This work provides an approach to obtain greater resolution of the rare variant signals within significant aggregate sets; this information can provide an objective measure to prioritize variants for follow-up experimental studies and insight into the biological pathways involved.

Factors Associated With Participation in Clinical Trials Among Patients With Lupus.

BACKGROUND/OBJECTIVE: Participation rates for clinical trials, including lupus trials, in the United States are low, but are even lower for underrepresented minorities. The impact of underrepresentation in trials can be far-reaching and is problematic because female subjects of color with lupus experience greater morbidity and mortality. As such, the overarching goal of this study was to characterize the factors that influence participation in lupus clinical trials. METHODS: The Lupus and Allied Diseases Association, the Lupus Foundation of America, and the Lupus Research Alliance collected data for their externally led Patient-Focused Drug Development Initiative-for the purpose of understanding and improving the rates of participation in lupus-related clinical trials. Participants completed a 46-question survey (in English or Spanish) electronically or on paper, which was distributed online or at lupus events. Logistic regression was used to test whether demographic and disease characteristics were associated with participation in past lupus trials. RESULTS: Data were available for 2220 respondents. Black respondents with lupus were more likely, than their White and Hispanic counterparts, to have participated in past clinical trials (p < 0.05). Although not statistically significant, Hispanic respondents were also more likely to have participated than their White counterparts (odds ratio, 1.40; 95% confidence interval, 0.96-2.11). Both demographic (ie, race/ethnicity) and medical (ie, disease severity defined as more organ involvement) factors seem to be important determinants of participation in clinical trials (p < 0.05). CONCLUSIONS: Combining the results from this study and prior research provides insight into recruitment strategies to increase participation rates of historically underrepresented minorities.

Obstructive Sleep Apnea as a Risk Factor for Intracerebral Hemorrhage.

BACKGROUND AND PURPOSE: To determine whether obstructive sleep apnea (OSA) is associated with intracerebral hemorrhage (ICH) risk, we assessed premorbid OSA exposure of patients with nontraumatic ICH and matched controls. METHODS: Ethnic/Racial Variations of Intracerebral Hemorrhage is a multicenter, case-control study evaluating risk factors for ICH that recruited 3000 cases with ICH and 3000 controls. OSA status was ascertained using the Berlin Questionnaire as a surrogate for premorbid OSA. We performed logistic regression analyses to evaluate the association between OSA and ICH. RESULTS: Two thousand and sixty-four (71%) cases and 1516 (52%) controls were classified as having OSA by the Berlin Questionnaire. Cases with OSA were significantly more likely to be male and have hypertension, heart disease, hyperlipidemia, and higher body mass index compared with those without OSA. OSA was more common among cases compared with controls (71% versus 52%, odds ratio, 2.28 [95% CI, 2.05-2.55]). In a multivariable logistic regression model, OSA was associated with increased risk for ICH (odds ratio, 1.47 [95% CI, 1.29-1.67]). CONCLUSIONS: OSA is a risk factor for ICH.

Hierarchicell: an R-package for estimating power for tests of differential expression with single-cell data.

BACKGROUND: Study design is a critical aspect of any experiment, and sample size calculations for statistical power that are consistent with that study design are central to robust and reproducible results. However, the existing power calculators for tests of differential expression in single-cell RNA-seq data focus on the total number of cells and not the number of independent experimental units, the true unit of interest for power. Thus, current methods grossly overestimate the power. RESULTS: Hierarchicell is the first single-cell power calculator to explicitly simulate and account for the hierarchical correlation structure (i.e., within sample correlation) that exists in single-cell RNA-seq data. Hierarchicell, an R-package available on GitHub, estimates the within sample correlation structure from real data to simulate hierarchical single-cell RNA-seq data and estimate power for tests of differential expression. This multi-stage approach models gene dropout rates, intra-individual dispersion, inter-individual variation, variable or fixed number of cells per individual, and the correlation among cells within an individual. Without modeling the within sample correlation structure and without properly accounting for the correlation in downstream analysis, we demonstrate that estimates of power are falsely inflated. Hierarchicell can be used to estimate power for binary and continuous phenotypes based on user-specified number of independent experimental units (e.g., individuals) and cells within the experimental unit. CONCLUSIONS: Hierarchicell is a user-friendly R-package that provides accurate estimates of power for testing hypotheses of differential expression in single-cell RNA-seq data. This R-package represents an important addition to single-cell RNA analytic tools and will help researchers design experiments with appropriate and accurate power, increasing discovery and improving robustness and reproducibility.

Genetic influences on susceptibility to rheumatoid arthritis in African-Americans.

Large meta-analyses of rheumatoid arthritis (RA) susceptibility in European (EUR) and East Asian (EAS) populations have identified >100 RA risk loci, but genome-wide studies of RA in African-Americans (AAs) are absent. To address this disparity, we performed an analysis of 916 AA RA patients and 1392 controls and aggregated our data with genotyping data from >100 000 EUR and Asian RA patients and controls. We identified two novel risk loci that appear to be specific to AAs: GPC5 and RBFOX1 (PAA < 5 × 10-9). Most RA risk loci are shared across different ethnicities, but among discordant loci, we observed strong enrichment of variants having large effect sizes. We found strong evidence of effect concordance for only 3 of the 21 largest effect index variants in EURs. We used the trans-ethnic fine-mapping algorithm PAINTOR3 to prioritize risk variants in >90 RA risk loci. Addition of AA data to those of EUR and EAS descent enabled identification of seven novel high-confidence candidate pathogenic variants (defined by posterior probability > 0.8). In summary, our trans-ethnic analyses are the first to include AAs, identified several new RA risk loci and point to candidate pathogenic variants that may underlie this common autoimmune disease. These findings may lead to better ways to diagnose or stratify treatment approaches in RA.

Admission Hemoglobin Levels Are Associated With Functional Outcome in Spontaneous Intracerebral Hemorrhage.

OBJECTIVES: To test the hypothesis that admission hemoglobin levels are associated with outcome in primary, nontraumatic intracerebral hemorrhage. DESIGN: Individual patient data meta-analysis of three studies of intracerebral hemorrhage. SETTING: Two randomized clinical trials and one multiethnic observational study. PATIENTS: Patients with spontaneous, nontraumatic intracerebral hemorrhage. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Our exposure of interest was admission hemoglobin levels and the primary outcome was 3-month postintracerebral hemorrhage-dichotomized modified Rankin Scale (0-3 vs 4-6). Intermediate outcomes were admission hematoma volume and hematoma expansion defined as 6 mL or 33% increase in hemorrhage size on repeat CT. A total of 4,172 intracerebral hemorrhage patients were included in the study (mean age 63 [sd = 14]; female sex 1,668 [40%]). Each additional g/dL of admission hemoglobin was associated with 14% (odds ratio, 0.86; 95% CI, 0.82-0.91) and 7% (odds ratio, 0.93; 95% CI, 0.88-0.98) reductions in the risk of poor outcome in unadjusted and adjusted analyses, respectively. Dose-response analyses indicated a linear relationship between admission hemoglobin levels and poor outcome across the entire evaluated range (test-for-trend p < 0.001). No consistent associations were found between the admission hemoglobin levels and hematoma volume or hematoma expansion. CONCLUSIONS: Higher hemoglobin levels are associated with better outcome in intracerebral hemorrhage. Further research is needed to evaluate admission hemoglobin levels as both a therapeutic target and predictor of outcome.

Arg206Cys substitution in DNASE1L3 causes a defect in DNASE1L3 protein secretion that confers risk of systemic lupus erythematosus.

OBJECTIVES: To determine if the polymorphism encoding the Arg206Cys substitution in DNASE1L3 explains the association of the DNASE1L3/PXK gene locus with systemic lupus erythematosus (SLE) and to examine the effect of the Arg206Cys sequence change on DNASE1L3 protein function. METHODS: Conditional analysis for rs35677470 was performed on cases and controls with European ancestry from the SLE Immunochip study, and genotype and haplotype frequencies were compared. DNASE1L3 protein levels were measured in cells and supernatants of HEK293 cells and monocyte-derived dendritic cells expressing recombinant and endogenous 206Arg and 206Cys protein variants. RESULTS: Conditional analysis on rs35677470 eliminated the SLE risk association signal for lead single-nucleotide polymorphisms (SNPs) rs180977001 and rs73081554, which are found to tag the same risk haplotype as rs35677470. The modest effect sizes of the SLE risk genotypes (heterozygous risk OR=1.14 and homozygous risk allele OR=1.68) suggest some DNASE1L3 endonuclease enzyme function is retained. An SLE protective signal in PXK (lead SNP rs11130643) remained following conditioning on rs35677470. The DNASE1L3 206Cys risk variant maintained enzymatic activity, but secretion of the artificial and endogenous DNASE1L3 206Cys protein was substantially reduced. CONCLUSIONS: SLE risk association in the DNASE1L3 locus is dependent on the missense SNP rs35677470, which confers a reduction in DNASE1L3 protein secretion but does not eliminate its DNase enzyme function.

Combined genetic analysis of juvenile idiopathic arthritis clinical subtypes identifies novel risk loci, target genes and key regulatory mechanisms.

OBJECTIVES: Juvenile idiopathic arthritis (JIA) is the most prevalent form of juvenile rheumatic disease. Our understanding of the genetic risk factors for this disease is limited due to low disease prevalence and extensive clinical heterogeneity. The objective of this research is to identify novel JIA susceptibility variants and link these variants to target genes, which is essential to facilitate the translation of genetic discoveries to clinical benefit. METHODS: We performed a genome-wide association study (GWAS) in 3305 patients and 9196 healthy controls, and used a Bayesian model selection approach to systematically investigate specificity and sharing of associated loci across JIA clinical subtypes. Suggestive signals were followed-up for meta-analysis with a previous GWAS (2751 cases/15 886 controls). We tested for enrichment of association signals in a broad range of functional annotations, and integrated statistical fine-mapping and experimental data to identify target genes. RESULTS: Our analysis provides evidence to support joint analysis of all JIA subtypes with the identification of five novel significant loci. Fine-mapping nominated causal single nucleotide polymorphisms with posterior inclusion probabilities ≥50% in five JIA loci. Enrichment analysis identified RELA and EBF1 as key transcription factors contributing to disease risk. Our integrative approach provided compelling evidence to prioritise target genes at six loci, highlighting mechanistic insights for the disease biology and IL6ST as a potential drug target. CONCLUSIONS: In a large JIA GWAS, we identify five novel risk loci and describe potential function of JIA association signals that will be informative for future experimental works and therapeutic strategies.