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The past 15 years have seen significant advancements in generating large-scale omics data, such as genetic methylation and proteomic data, from diverse research studies. Efforts by organizations like the NIH have made this data readily available through expanding data repositories. However, analyses from public databases often suffer from issues like lack of generalizability and replication. Additionally, there is a lack of genetic data from non-White populations, leading to disparities in genetic risk prediction and limiting discovery opportunities. Here, Rasmussen-Torvik discuss study by Tahir et al which utilizes data from different cohorts and biobanks to analyze protein quantitative loci and perform phenomewide association studies. They identify important associations between genetic variants and clinical diagnoses, providing valuable insights. They have made their summary statistics available to the scientific community, emphasizing the importance of recognizing and valuing population science analyses in diverse populations.

In this issue of the JCI, Hazan et al report on whether time of day of COVID vaccination associates with subsequent breakthrough COVID-19 infection in 1,515,754 patients, using data from a large Israeli health maintenance organization. The authors found evidence to support that the timing of vaccine dosing associated with clinical outcomes. When considering the timing of the second dose in the original two-dose COVID vaccine series, individuals who were vaccinated in the morning (0800-1159 hours) had lower rates of breakthrough infections, emergency room visits for COVID, and hospitalizations for COVID when compared with individuals vaccinated in the evening (1600-1959 hours). However, only the association with breakthrough infections rose to the level of statistical significance.

The 2022 Lasker-Bloomberg Public Service award honors Dr. Lauren Gardner, who leads the team that built the Johns Hopkins COVID-19 global tracking map. Established in Jan 2020, the map evolved into the Johns Hopkins Coronavirus Resource Center by Mar 2020. The map and resource center website, which are supported by Bloomberg Philanthropies and the Stavros Niarchos Foundation, were critical resources for scientists, national and international policymakers, the press, and the public in the early months of the COVID pandemic, and they remain important and widely used resources to this day. In addition to providing comprehensive, reliable, and easily digestible data at a key time in the COVID pandemic, Dr. Gardner's work provides a model of how researchers and public health agencies should collect, process, and disseminate data during future infectious disease outbreaks.

To determine the changes in severe acute respiratory coronavirus virus 2 (SARS-CoV-2) serologic status and SARS-CoV-2 infection rates in healthcare workers (HCWs) over 6-months of follow-up. Prospective cohort study. HCWs in the Chicago area. Cohort participants were recruited in May and June 2020 for baseline serology testing (Abbott anti-nucleocapsid IgG) and were then invited for follow-up serology testing 6 months later. Participants completed monthly online surveys that assessed demographics, medical history, coronavirus disease 2019 (COVID-19), and exposures to SARS-CoV-2. The electronic medical record was used to identify SARS-CoV-2 polymerase chain reaction (PCR) positivity during follow-up. Serologic conversion and SARS-CoV-2 infection or possible reinfection rates (cases per 10,000 person days) by antibody status at baseline and follow-up were assessed. In total, 6,510 HCWs were followed for a total of 1,285,395 person days (median follow-up, 216 days). For participants who had baseline and follow-up serology checked, 285 (6.1%) of the 4,681 seronegative participants at baseline seroconverted to positive at follow-up; 138 (48%) of the 263 who were seropositive at baseline were seronegative at follow-up. When analyzed by baseline serostatus alone, 519 (8.4%) of 6,194 baseline seronegative participants had a positive PCR after baseline serology testing (4.25 per 10,000 person days). Of 316 participants who were seropositive at baseline, 8 (2.5%) met criteria for possible SARS-CoV-2 reinfection (ie, PCR positive >90 days after baseline serology) during follow-up, a rate of 1.27 per 10,000 days at risk. The adjusted rate ratio for possible reinfection in baseline seropositive compared to infection in baseline seronegative participants was 0.26 (95% confidence interval, 0.13-0.53). Seropositivity in HCWs is associated with moderate protection from future SARS-CoV-2 infection.

Heart failure is a leading cause of cardiovascular morbidity and mortality. However, the contribution of common genetic variation to heart failure risk has not been fully elucidated, particularly in comparison to other common cardiometabolic traits. We report a multi-ancestry genome-wide association study meta-analysis of all-cause heart failure including up to 115,150 cases and 1,550,331 controls of diverse genetic ancestry, identifying 47 risk loci. We also perform multivariate genome-wide association studies that integrate heart failure with related cardiac magnetic resonance imaging endophenotypes, identifying 61 risk loci. Gene-prioritization analyses including colocalization and transcriptome-wide association studies identify known and previously unreported candidate cardiomyopathy genes and cellular processes, which we validate in gene-expression profiling of failing and healthy human hearts. Colocalization, gene expression profiling, and Mendelian randomization provide convergent evidence for the roles of BCKDHA and circulating branch-chain amino acids in heart failure and cardiac structure. Finally, proteome-wide Mendelian randomization identifies 9 circulating proteins associated with heart failure or quantitative imaging traits. These analyses highlight similarities and differences among heart failure and associated cardiovascular imaging endophenotypes, implicate common genetic variation in the pathogenesis of heart failure, and identify circulating proteins that may represent cardiomyopathy treatment targets. Heart failure is a major cause of cardiovascular morbidity and mortality. Here, the authors report results of a genome-wide association study meta-analysis, characterizing the role of common genetic variants in heart failure, finding overlap with common cardiovascular risk factors and imaging measures of cardiac structure/function.

Background Type 2 diabetes (T2D) is a worldwide scourge caused by both genetic and environmental risk factors that disproportionately afflicts communities of color. Leveraging existing large-scale genome-wide association studies (GWAS), polygenic risk scores (PRS) have shown promise to complement established clinical risk factors and intervention paradigms, and improve early diagnosis and prevention of T2D. However, to date, T2D PRS have been most widely developed and validated in individuals of European descent. Comprehensive assessment of T2D PRS in non-European populations is critical for equitable deployment of PRS to clinical practice that benefits global populations. Methods We integrated T2D GWAS in European, African, and East Asian populations to construct a trans-ancestry T2D PRS using a newly developed Bayesian polygenic modeling method, and assessed the prediction accuracy of the PRS in the multi-ethnic Electronic Medical Records and Genomics (eMERGE) study (11,945 cases; 57,694 controls), four Black cohorts (5137 cases; 9657 controls), and the Taiwan Biobank (4570 cases; 84,996 controls). We additionally evaluated a post hoc ancestry adjustment method that can express the polygenic risk on the same scale across ancestrally diverse individuals and facilitate the clinical implementation of the PRS in prospective cohorts. Results The trans-ancestry PRS was significantly associated with T2D status across the ancestral groups examined. The top 2% of the PRS distribution can identify individuals with an approximately 2.5–4.5-fold of increase in T2D risk, which corresponds to the increased risk of T2D for first-degree relatives. The post hoc ancestry adjustment method eliminated major distributional differences in the PRS across ancestries without compromising its predictive performance. Conclusions By integrating T2D GWAS from multiple populations, we developed and validated a trans-ancestry PRS, and demonstrated its potential as a meaningful index of risk among diverse patients in clinical settings. Our efforts represent the first step towards the implementation of the T2D PRS into routine healthcare.

Lipid levels are important markers for the development of cardio-metabolic diseases. Although hundreds of associated loci have been identified through genetic association studies, the contribution of genetic factors to variation in lipids is not fully understood, particularly in U.S. minority groups. We performed genome-wide association analyses for four lipid traits in over 45,000 ancestrally diverse participants from the Population Architecture using Genomics and Epidemiology (PAGE) Study, followed by a meta-analysis with several European ancestry studies. We identified nine novel lipid loci, five of which showed evidence of replication in independent studies. Furthermore, we discovered one novel gene in a PrediXcan analysis, minority-specific independent signals at eight previously reported loci, and potential functional variants at two known loci through fine-mapping. Systematic examination of known lipid loci revealed smaller effect estimates in African American and Hispanic ancestry populations than those in Europeans, and better performance of polygenic risk scores based on minority-specific effect estimates. Our findings provide new insight into the genetic architecture of lipid traits and highlight the importance of conducting genetic studies in diverse populations in the era of precision medicine.

Purpose Discovering an incidental finding (IF) or secondary finding (SF) is a potential result of genomic testing, but few data exist describing types and frequencies of SFs likely to appear in broader clinical populations. Methods The Electronic Medical Records and Genomics Network Phase III (eMERGE III) developed a CLIA-compliant sequencing panel of 109 genes and 1551 variants of clinical relevance or research interest and deployed this panel at ten clinical sites. We evaluated medically actionable SFs across 67 genes and 14 single-nucleotide variants (SNVs) in a diverse cohort of 21,915 participants drawn from a variety of settings (e.g., primary care, biobanks, specialty clinics). Results Correcting for testing indication, we found a 3.02% overall frequency of SFs; 2.54% from 59 genes the American College of Medical Genetics and Genomics recommends for SF return, and 0.48% in other genes, primarily HFE and PALB2 . SFs associated with cancer susceptibility were most frequent (1.38%), followed by cardiovascular diseases (0.87%), and lipid disorders (0.50%). After removing HFE , the frequency of SFs and proportion of pathogenic versus likely pathogenic SFs did not differ in those self-identifying as White versus others. Conclusion Here we present frequencies and types of medically actionable secondary findings to support informed decision making by patients, participants, and practitioners engaged in genomic medicine.

Aims/hypothesisType 2 diabetes is a growing global public health challenge. Investigating quantitative traits, including fasting glucose, fasting insulin and HbA1c, that serve as early markers of type 2 diabetes progression may lead to a deeper understanding of the genetic aetiology of type 2 diabetes development. Previous genome-wide association studies (GWAS) have identified over 500 loci associated with type 2 diabetes, glycaemic traits and insulin-related traits. However, most of these findings were based only on populations of European ancestry. To address this research gap, we examined the genetic basis of fasting glucose, fasting insulin and HbA1c in participants of the diverse Population Architecture using Genomics and Epidemiology (PAGE) Study.MethodsWe conducted a GWAS of fasting glucose (n = 52,267), fasting insulin (n = 48,395) and HbA1c (n = 23,357) in participants without diabetes from the diverse PAGE Study (23% self-reported African American, 46% Hispanic/Latino, 40% European, 4% Asian, 3% Native Hawaiian, 0.8% Native American), performing transethnic and population-specific GWAS meta-analyses, followed by fine-mapping to identify and characterise novel loci and independent secondary signals in known loci.ResultsFour novel associations were identified (p < 5 × 10−9), including three loci associated with fasting insulin, and a novel, low-frequency African American-specific locus associated with fasting glucose. Additionally, seven secondary signals were identified, including novel independent secondary signals for fasting glucose at the known GCK locus and for fasting insulin at the known PPP1R3B locus in transethnic meta-analysis.Conclusions/interpretationOur findings provide new insights into the genetic architecture of glycaemic traits and highlight the continued importance of conducting genetic studies in diverse populations.Data availabilityFull summary statistics from each of the population-specific and transethnic results are available at NHGRI-EBI GWAS catalog (https://www.ebi.ac.uk/gwas/downloads/summary-statistics).

Diverticular disease (DD) is one of the most prevalent conditions encountered by gastroenterologists, affecting ~50% of Americans before the age of 60. Our aim was to identify genetic risk variants and clinical phenotypes associated with DD, leveraging multiple electronic health record (EHR) data sources of 91,166 multi-ancestry participants with a Natural Language Processing (NLP) technique. We developed a NLP-enriched phenotyping algorithm that incorporated colonoscopy or abdominal imaging reports to identify patients with diverticulosis and diverticulitis from multicenter EHRs. We performed genome-wide association studies (GWAS) of DD in European, African and multi-ancestry participants, followed by phenome-wide association studies (PheWAS) of the risk variants to identify their potential comorbid/pleiotropic effects in clinical phenotypes. Our developed algorithm showed a significant improvement in patient classification performance for DD analysis (algorithm PPVs ≥ 0.94), with up to a 3.5 fold increase in terms of the number of identified patients than the traditional method. Ancestry-stratified analyses of diverticulosis and diverticulitis of the identified subjects replicated the well-established associations between ARHGAP15 loci with DD, showing overall intensified GWAS signals in diverticulitis patients compared to diverticulosis patients. Our PheWAS analyses identified significant associations between the DD GWAS variants and circulatory system, genitourinary, and neoplastic EHR phenotypes. As the first multi-ancestry GWAS-PheWAS study, we showcased that heterogenous EHR data can be mapped through an integrative analytical pipeline and reveal significant genotype-phenotype associations with clinical interpretation. A systematic framework to process unstructured EHR data with NLP could advance a deep and scalable phenotyping for better patient identification and facilitate etiological investigation of a disease with multilayered data.