Collaborative Cohort of Cohorts for COVID-19 Research (C4R) Study: Study Design.

The Collaborative Cohort of Cohorts for COVID-19 Research (C4R) is a national prospective study of adults at risk for coronavirus disease 2019 (COVID-19) comprising 14 established United States (US) prospective cohort studies. For decades, C4R cohorts have collected extensive data on clinical and subclinical diseases and their risk factors, including behavior, cognition, biomarkers, and social determinants of health. C4R will link this pre-COVID phenotyping to information on SARS-CoV-2 infection and acute and post-acute COVID-related illness. C4R is largely population-based, has an age range of 18-108 years, and broadly reflects the racial, ethnic, socioeconomic, and geographic diversity of the US. C4R is ascertaining severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and COVID-19 illness using standardized questionnaires, ascertainment of COVID-related hospitalizations and deaths, and a SARS-CoV-2 serosurvey via dried blood spots. Master protocols leverage existing robust retention rates for telephone and in-person examinations, and high-quality events surveillance. Extensive pre-pandemic data minimize referral, survival, and recall bias. Data are being harmonized with research-quality phenotyping unmatched by clinical and survey-based studies; these will be pooled and shared widely to expedite collaboration and scientific findings. This unique resource will allow evaluation of risk and resilience factors for COVID-19 severity and outcomes, including post-acute sequelae, and assessment of the social and behavioral impact of the pandemic on long-term trajectories of health and aging.

Sequencing and imputation in GWAS: Cost-effective strategies to increase power and genomic coverage across diverse populations.

A key aim for current genome-wide association studies (GWAS) is to interrogate the full spectrum of genetic variation underlying human traits, including rare variants, across populations. Deep whole-genome sequencing is the gold standard to fully capture genetic variation, but remains prohibitively expensive for large sample sizes. Array genotyping interrogates a sparser set of variants, which can be used as a scaffold for genotype imputation to capture a wider set of variants. However, imputation quality depends crucially on reference panel size and genetic distance from the target population. Here, we consider sequencing a subset of GWAS participants and imputing the rest using a reference panel that includes both sequenced GWAS participants and an external reference panel. We investigate how imputation quality and GWAS power are affected by the number of participants sequenced for admixed populations (African and Latino Americans) and European population isolates (Sardinians and Finns), and identify powerful, cost-effective GWAS designs given current sequencing and array costs. For populations that are well-represented in existing reference panels, we find that array genotyping alone is cost-effective and well-powered to detect common- and rare-variant associations. For poorly represented populations, sequencing a subset of participants is often most cost-effective, and can substantially increase imputation quality and GWAS power.

Cardiovascular Disease Burden and Socioeconomic Correlates: Findings From the Jackson Heart Study.

BACKGROUND: Black persons have an excess burden of cardiovascular disease (CVD) compared with white persons. This burden persists after adjustment for socioeconomic status and other known CVD risk factors. This study evaluated the CVD burden and the socioeconomic gradient of CVD among black participants in the JHS (Jackson Heart Study). METHODS AND RESULTS: CVD burden was evaluated by comparing the observed prevalence of myocardial infarction, stroke, and hypertension in the JHS at baseline (2000-2004) with the expected prevalence according to US national surveys during a similar time period. The socioeconomic gradient of CVD was evaluated using logistic regression models. Compared with the national data, the JHS age- and sex-standardized prevalence ratios for myocardial infarction, stroke, and hypertension were 1.07 (95% CI, 0.90-1.27), 1.46 (95% CI, 1.18-1.78), and 1.51 (95% CI, 1.42-1.60), respectively, in men and 1.50 (95% CI, 1.27-1.76), 1.33 (95% CI, 1.12-1.57), and 1.43 (95% CI, 1.37-1.50), respectively, in women. A significant and inverse relationship was observed between socioeconomic status and CVD within the JHS cohort. The strongest and most consistent socioeconomic correlate after adjusting for age and sex was income for myocardial infarction (odds ratio: 3.53; 95% CI, 2.31-5.40) and stroke (odds ratio: 3.73; 95% CI, 2.32-5.97), comparing the poor and affluent income categories. CONCLUSIONS: Except for myocardial infarction in men, CVD burden in the JHS cohort was higher than expected. A strong inverse socioeconomic gradient of CVD was also observed within the JHS cohort.