Genetic architecture of spatial electrical biomarkers for cardiac arrhythmia and relationship with cardiovascular disease.

The 3-dimensional spatial and 2-dimensional frontal QRS-T angles are measures derived from the vectorcardiogram. They are independent risk predictors for arrhythmia, but the underlying biology is unknown. Using multi-ancestry genome-wide association studies we identify 61 (58 previously unreported) loci for the spatial QRS-T angle (N = 118,780) and 11 for the frontal QRS-T angle (N = 159,715). Seven out of the 61 spatial QRS-T angle loci have not been reported for other electrocardiographic measures. Enrichments are observed in pathways related to cardiac and vascular development, muscle contraction, and hypertrophy. Pairwise genome-wide association studies with classical ECG traits identify shared genetic influences with PR interval and QRS duration. Phenome-wide scanning indicate associations with atrial fibrillation, atrioventricular block and arterial embolism and genetically determined QRS-T angle measures are associated with fascicular and bundle branch block (and also atrioventricular block for the frontal QRS-T angle). We identify potential biology involved in the QRS-T angle and their genetic relationships with cardiovascular traits and diseases, may inform future research and risk prediction.

Genetic analyses of the electrocardiographic QT interval and its components identify additional loci and pathways.

The QT interval is an electrocardiographic measure representing the sum of ventricular depolarization and repolarization, estimated by QRS duration and JT interval, respectively. QT interval abnormalities are associated with potentially fatal ventricular arrhythmia. Using genome-wide multi-ancestry analyses (>250,000 individuals) we identify 177, 156 and 121 independent loci for QT, JT and QRS, respectively, including a male-specific X-chromosome locus. Using gene-based rare-variant methods, we identify associations with Mendelian disease genes. Enrichments are observed in established pathways for QT and JT, and previously unreported genes indicated in insulin-receptor signalling and cardiac energy metabolism. In contrast for QRS, connective tissue components and processes for cell growth and extracellular matrix interactions are significantly enriched. We demonstrate polygenic risk score associations with atrial fibrillation, conduction disease and sudden cardiac death. Prioritization of druggable genes highlight potential therapeutic targets for arrhythmia. Together, these results substantially advance our understanding of the genetic architecture of ventricular depolarization and repolarization.

Genetic pleiotropy underpinning adiposity and inflammation in self-identified Hispanic/Latino populations.

BACKGROUND: Concurrent variation in adiposity and inflammation suggests potential shared functional pathways and pleiotropic disease underpinning. Yet, exploration of pleiotropy in the context of adiposity-inflammation has been scarce, and none has included self-identified Hispanic/Latino populations. Given the high level of ancestral diversity in Hispanic American population, genetic studies may reveal variants that are infrequent/monomorphic in more homogeneous populations. METHODS: Using multi-trait Adaptive Sum of Powered Score (aSPU) method, we examined individual and shared genetic effects underlying inflammatory (CRP) and adiposity-related traits (Body Mass Index [BMI]), and central adiposity (Waist to Hip Ratio [WHR]) in HLA participating in the Population Architecture Using Genomics and Epidemiology (PAGE) cohort (N = 35,871) with replication of effects in the Cameron County Hispanic Cohort (CCHC) which consists of Mexican American individuals. RESULTS: Of the > 16 million SNPs tested, variants representing 7 independent loci were found to illustrate significant association with multiple traits. Two out of 7 variants were replicated at statistically significant level in multi-trait analyses in CCHC. The lead variant on APOE (rs439401) and rs11208712 were found to harbor multi-trait associations with adiposity and inflammation. CONCLUSIONS: Results from this study demonstrate the importance of considering pleiotropy for improving our understanding of the etiology of the various metabolic pathways that regulate cardiovascular disease development.

Predicted gene expression in ancestrally diverse populations leads to discovery of susceptibility loci for lifestyle and cardiometabolic traits.

One mechanism by which genetic factors influence complex traits and diseases is altering gene expression. Direct measurement of gene expression in relevant tissues is rarely tenable; however, genetically regulated gene expression (GReX) can be estimated using prediction models derived from large multi-omic datasets. These approaches have led to the discovery of many gene-trait associations, but whether models derived from predominantly European ancestry (EA) reference panels can map novel associations in ancestrally diverse populations remains unclear. We applied PrediXcan to impute GReX in 51,520 ancestrally diverse Population Architecture using Genomics and Epidemiology (PAGE) participants (35% African American, 45% Hispanic/Latino, 10% Asian, and 7% Hawaiian) across 25 key cardiometabolic traits and relevant tissues to identify 102 novel associations. We then compared associations in PAGE to those in a random subset of 50,000 White British participants from UK Biobank (UKBB50k) for height and body mass index (BMI). We identified 517 associations across 47 tissues in PAGE but not UKBB50k, demonstrating the importance of diverse samples in identifying trait-associated GReX. We observed that variants used in PrediXcan models were either more or less differentiated across continental-level populations than matched-control variants depending on the specific population reflecting sampling bias. Additionally, variants from identified genes specific to either PAGE or UKBB50k analyses were more ancestrally differentiated than those in genes detected in both analyses, underlining the value of population-specific discoveries. This suggests that while EA-derived transcriptome imputation models can identify new associations in non-EA populations, models derived from closely matched reference panels may yield further insights. Our findings call for more diversity in reference datasets of tissue-specific gene expression.

Multi-ethnic genome-wide association analyses of white blood cell and platelet traits in the Population Architecture using Genomics and Epidemiology (PAGE) study.

BACKGROUND: Circulating white blood cell and platelet traits are clinically linked to various disease outcomes and differ across individuals and ancestry groups. Genetic factors play an important role in determining these traits and many loci have been identified. However, most of these findings were identified in populations of European ancestry (EA), with African Americans (AA), Hispanics/Latinos (HL), and other races/ethnicities being severely underrepresented. RESULTS: We performed ancestry-combined and ancestry-specific genome-wide association studies (GWAS) for white blood cell and platelet traits in the ancestrally diverse Population Architecture using Genomics and Epidemiology (PAGE) Study, including 16,201 AA, 21,347 HL, and 27,236 EA participants. We identified six novel findings at suggestive significance (P < 5E-8), which need confirmation, and independent signals at six previously established regions at genome-wide significance (P < 2E-9). We confirmed multiple previously reported genome-wide significant variants in the single variant association analysis and multiple genes using PrediXcan. Evaluation of loci reported from a Euro-centric GWAS indicated attenuation of effect estimates in AA and HL compared to EA populations. CONCLUSIONS: Our results highlighted the potential to identify ancestry-specific and ancestry-agnostic variants in participants with diverse backgrounds and advocate for continued efforts in improving inclusion of racially/ethnically diverse populations in genetic association studies for complex traits.

Comparison of adaptive multiple phenotype association tests using summary statistics in genome-wide association studies.

Genome-wide association studies have been successful mapping loci for individual phenotypes, but few studies have comprehensively interrogated evidence of shared genetic effects across multiple phenotypes simultaneously. Statistical methods have been proposed for analyzing multiple phenotypes using summary statistics, which enables studies of shared genetic effects while avoiding challenges associated with individual-level data sharing. Adaptive tests have been developed to maintain power against multiple alternative hypotheses because the most powerful single-alternative test depends on the underlying structure of the associations between the multiple phenotypes and a single nucleotide polymorphism (SNP). Here we compare the performance of six such adaptive tests: two adaptive sum of powered scores (aSPU) tests, the unified score association test (metaUSAT), the adaptive test in a mixed-models framework (mixAda) and two principal-component-based adaptive tests (PCAQ and PCO). Our simulations highlight practical challenges that arise when multivariate distributions of phenotypes do not satisfy assumptions of multivariate normality. Previous reports in this context focus on low minor allele count (MAC) and omit the aSPU test, which relies less than other methods on asymptotic and distributional assumptions. When these assumptions are not satisfied, particularly when MAC is low and/or phenotype covariance matrices are singular or nearly singular, aSPU better preserves type I error, sometimes at the cost of decreased power. We illustrate this trade-off with multiple phenotype analyses of six quantitative electrocardiogram traits in the Population Architecture using Genomics and Epidemiology (PAGE) study.

Multi-Ethnic Genome-Wide Association Study of Decomposed Cardioelectric Phenotypes Illustrates Strategies to Identify and Characterize Evidence of Shared Genetic Effects for Complex Traits.

BACKGROUND: We examined how expanding electrocardiographic trait genome-wide association studies to include ancestrally diverse populations, prioritize more precise phenotypic measures, and evaluate evidence for shared genetic effects enabled the detection and characterization of loci. METHODS: We decomposed 10 seconds, 12-lead electrocardiograms from 34 668 multi-ethnic participants (15% Black; 30% Hispanic/Latino) into 6 contiguous, physiologically distinct (P wave, PR segment, QRS interval, ST segment, T wave, and TP segment) and 2 composite, conventional (PR interval and QT interval) interval scale traits and conducted multivariable-adjusted, trait-specific univariate genome-wide association studies using 1000-G imputed single-nucleotide polymorphisms. Evidence of shared genetic effects was evaluated by aggregating meta-analyzed univariate results across the 6 continuous electrocardiographic traits using the combined phenotype adaptive sum of powered scores test. RESULTS: We identified 6 novels (CD36, PITX2, EMB, ZNF592, YPEL2, and BC043580) and 87 known loci (adaptive sum of powered score test P<5×10-9). Lead single-nucleotide polymorphism rs3211938 at CD36 was common in Blacks (minor allele frequency=10%), near monomorphic in European Americans, and had effects on the QT interval and TP segment that ranked among the largest reported to date for common variants. The other 5 novel loci were observed when evaluating the contiguous but not the composite electrocardiographic traits. Combined phenotype testing did not identify novel electrocardiographic loci unapparent using traditional univariate approaches, although this approach did assist with the characterization of known loci. CONCLUSIONS: Despite including one-third as many participants as published electrocardiographic trait genome-wide association studies, our study identified 6 novel loci, emphasizing the importance of ancestral diversity and phenotype resolution in this era of ever-growing genome-wide association studies.

Ancestry-specific associations identified in genome-wide combined-phenotype study of red blood cell traits emphasize benefits of diversity in genomics.

BACKGROUND: Quantitative red blood cell (RBC) traits are highly polygenic clinically relevant traits, with approximately 500 reported GWAS loci. The majority of RBC trait GWAS have been performed in European- or East Asian-ancestry populations, despite evidence that rare or ancestry-specific variation contributes substantially to RBC trait heritability. Recently developed combined-phenotype methods which leverage genetic trait correlation to improve statistical power have not yet been applied to these traits. Here we leveraged correlation of seven quantitative RBC traits in performing a combined-phenotype analysis in a multi-ethnic study population. RESULTS: We used the adaptive sum of powered scores (aSPU) test to assess combined-phenotype associations between ~ 21 million SNPs and seven RBC traits in a multi-ethnic population (maximum n = 67,885 participants; 24% African American, 30% Hispanic/Latino, and 43% European American; 76% female). Thirty-nine loci in our multi-ethnic population contained at least one significant association signal (p < 5E-9), with lead SNPs at nine loci significantly associated with three or more RBC traits. A majority of the lead SNPs were common (MAF > 5%) across all ancestral populations. Nineteen additional independent association signals were identified at seven known loci (HFE, KIT, HBS1L/MYB, CITED2/FILNC1, ABO, HBA1/2, and PLIN4/5). For example, the HBA1/2 locus contained 14 conditionally independent association signals, 11 of which were previously unreported and are specific to African and Amerindian ancestries. One variant in this region was common in all ancestries, but exhibited a narrower LD block in African Americans than European Americans or Hispanics/Latinos. GTEx eQTL analysis of all independent lead SNPs yielded 31 significant associations in relevant tissues, over half of which were not at the gene immediately proximal to the lead SNP. CONCLUSION: This work identified seven loci containing multiple independent association signals for RBC traits using a combined-phenotype approach, which may improve discovery in genetically correlated traits. Highly complex genetic architecture at the HBA1/2 locus was only revealed by the inclusion of African Americans and Hispanics/Latinos, underscoring the continued importance of expanding large GWAS to include ancestrally diverse populations.

Socioeconomic burden of pain in rheumatic disease.

Socioeconomic inequities in the health outcomes of rheumatic diseases, including pain, have been well documented across countries and study designs. Nevertheless, health disparities remain surprisingly-poorly understood in the rheumatic diseases, owing both to the complex nature of those disorders, and to methodological challenges surrounding the evaluation of social class and of its ties to health. Methodological difficulties in measuring SES can complicate interpretation of results to understand mechanisms of these associations. Current research on associations between SES and pain in rheumatic diseases are summarised in this article. Our review indicates that inequalities in pain in patients with OA and RA with low individual SES are strong and well-established, although associations in other rheumatic conditions and with community or childhood SES are less well-established. Further, the range of proposed mechanisms underlying disparities is broad, encompassing numerous indicators of SES, such as occupation, income, and education and varying widely by disease.

Socioeconomic factors and self-reported health outcomes in African Americans with rheumatoid arthritis from the Southeastern United States: the contribution of childhood socioeconomic status.

BACKGROUND: There is abundant evidence that low socioeconomic status (SES) is associated with worse health outcomes among people with Rheumatoid Arthritis (RA); however, the influence of socioeconomic disadvantage in early life has yet to be studied within that population. METHODS: Data originated from the cross-sectional arm of the Consortium Evaluation of African-Americans with Rheumatoid Arthritis (CLEAR II), which recruited African-Americans with RA from six sites in the Southeastern United States. We used linear regression models to evaluate associations of parental homeownership status and educational level at participant time of birth with participant-reported fatigue (Visual Analog scale, cm), pain (Visual Analog scale, cm), disability (Health Assessment Questionnaire) and helplessness (Rheumatology Attitudes Index), independently of participant homeownership status and educational level. Models included random effects to account for intra-site correlations, and were adjusted for variables identified using backward selection, from: age, disease-duration, sex, medication use, body-mass index, smoking history. RESULTS: Our sample included 516 CLEAR II participants with full data on demographics and covariates. 89% of participants were women, the mean age was 54.7 years and mean disease duration was 10.8 years. In age adjusted models, parental non-homeownership was associated with greater fatigue (ß = 0.75, 95% CI = 0.36-1.14), disability (ß = 0.12, 95% CI = 0.04-0.19) and helplessness (ß = 0.12, 95% CI = 0.03-0.21), independently of participant homeownership and education; parental education had a further small influence on self-reported fatigue (ß = 0.20, 95% CI = 0.15-0.24). CONCLUSIONS: Parental homeownership, and to a small extent parental education, had modest but meaningful relationships with self-reported health among CLEAR II participants.

Socioeconomic disparities in the health of african americans with rheumatoid arthritis from the southeastern United States.

OBJECTIVE: To examine cross-sectional baseline data from the Consortium for the Longitudinal Evaluation of African Americans with Early Rheumatoid Arthritis registry for the association between socioeconomic status (SES) with clinical and self-report health outcomes. METHODS: We analyzed data on 937 African Americans who provided comprehensive sociodemographic data in addition to self-reported health outcomes. SES measures included educational attainment, homeownership, household income, and occupation. Outcomes included measures of disease activity, joint damage, autoantibody status, and self-reported measures. Multivariable linear, logistic, and zero-inflated Poisson regression models were used to estimate associations of each SES measure with rheumatoid arthritis (RA) outcomes, controlling for sex, age, disease duration, comorbid conditions, body mass index, smoking, methotrexate/leflunomide use, and biologic agent use. RESULTS: The mean age was 54 years, 86% were women, and the mean RA disease duration was 7.8 years. Approximately 24% had less than a high school degree, 56% had a nonprofessional occupation, 75% had a household income ≤$30,000, and 55% were nonhomeowners. In multivariable regression models, significantly increased associations of disease activity measures and self-reported health outcomes were observed with low household income (≤$30,000/year) and nonhomeownership. Education less than high school was primarily associated with self-reported health outcomes. Among participants with disease duration <2 years, associations of SES were confined to self-reported measures. CONCLUSION: Our results indicate significant socioeconomic disparities in self-reported physical and mental health, clinical disease activity measures, and autoantibody status among African Americans with RA not explained by differences in demographics, medication use, and health behaviors.

Independent associations of childhood and current socioeconomic status with risk of self-reported doctor-diagnosed arthritis in a family-medicine cohort of North-Carolinians.

BACKGROUND: Associations of socioeconomic status (SES) with the prevalence of various forms of arthritis are well documented. Increasing evidence suggests that SES during childhood is a lasting determinant of health, but its association with the onset of arthritis remains unclear. METHODS: Cross-sectional data on 1276 participants originated from 22 family practices in North-Carolina, USA. We created 4-level (high, medium, low, lowest) current SES and childhood SES summary scores based on parental and participant education, occupation and homeownership. We investigated associations of individual SES characteristics, summary scores and SES trajectories (e.g. high/low) with self-reported arthritis in logistic regression models progressively adjusted for race and gender, age, then BMI, and clustered by family practice. RESULTS: We found evidence for independent associations of both childhood and current SES with the reporting of arthritis across our models. In covariate-adjusted models simultaneously including current and childhood SES, compared with high SES participants in the lowest childhood SES category (OR = 1.39 [95% CI = 1.04, 1.85]) and those in the low (OR = 1.66 [95% CI = 1.14, 2.42]) and lowest (OR = 2.08 [95% CI = 1.16, 3.74]) categories of current SES had significantly greater odds of having self-reported arthritis. CONCLUSIONS: Current SES and childhood SES are both associated with the odds of reporting arthritis within this primary-care population, although the possibly superseding influence of existing circumstances must be noted. BMI was a likely mechanism in the association of childhood SES with arthritis onset, and research is needed to elucidate further pathways linking the socioeconomic environment across life-stages and the development of rheumatic diseases.

Independent influences of current and childhood socioeconomic status on health outcomes in a North Carolina family practice sample of arthritis patients.

OBJECTIVE: Compelling evidence suggests that socioeconomic status (SES) is a determinant of health outcomes among persons with arthritis. SES in early life has likewise been associated with various aspects of health, but the connection between childhood SES and health among people with arthritis remains to be investigated. The purpose of this study was to determine the influences of current and childhood SES on self-reported disability, depression, and physical and mental health among people with self-reported doctor-diagnosed arthritis. METHODS: Data originated from a North Carolina network of primary care centers. Participants with self-reported arthritis with complete sociodemographic and relevant health information were retained in our sample (n = 782). We created summary measures for current and childhood SES from indicators of education, occupation, and homeownership, using parental SES as a proxy for participants' childhood SES. Linear regression models were used to assess the associations between health outcomes and SES variables separately and together, adjusting for key covariates. RESULTS: Lower childhood and current SES scores were associated with worse disability and physical health. Current SES was furthermore associated with mental health and depressive symptoms. Associations of low current and childhood SES with health outcomes remained significant when concurrently included in a linear model. CONCLUSION: Childhood and current SES are both determinants of health among persons with arthritis. This underscores the importance of childhood SES as a determinant of adult health among individuals with arthritis. Further studies should focus on these associations in different populations and across different types of arthritis.