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IntroductionNational obesity prevention strategies may benefit from precision health approaches involving diverse participants in population health studies. We used cohort data from the National Institutes of Health All of Us Research Program (All of Us) Researcher Workbench to estimate population-level obesity prevalence.MethodsTo estimate state-level obesity prevalence we used data from physical measurements made during All of Us enrollment visits and data from participant electronic health records (EHRs) where available. Prevalence estimates were calculated and mapped by state for 2 categories of body mass index (BMI) (kg/m2): obesity (BMI >30) and severe obesity (BMI >35). We calculated and mapped prevalence by state, excluding states with fewer than 100 All of Us participants.ResultsData on height and weight were available for 244,504 All of Us participants from 33 states, and corresponding EHR data were available for 88,840 of these participants. The median and IQR of BMI taken from physical measurements data was 28.4 (24.4- 33.7) and 28.5 (24.5-33.6) from EHR data, where available. Overall obesity prevalence based on physical measurements data was 41.5% (95% CI, 41.3%-41.7%); prevalence of severe obesity was 20.7% (95% CI, 20.6-20.9), with large geographic variations observed across states. Prevalence estimates from states with greater numbers of All of Us participants were more similar to national population-based estimates than states with fewer participants.ConclusionAll of Us participants had a high prevalence of obesity, with state-level geographic variation mirroring national trends. The diversity among All of Us participants may support future investigations on obesity prevention and treatment in diverse populations.

The new Pharmacogenomics Global Research Network (PGRN) is an independent society that builds on the National Institutes of Health (NIH)–funded Pharmacogenomics Research Network that was established in 2000. Leveraging the original PGRN’s previous success, the new network continues to be a leader in the field of personalized medicine focusing on research, discovery, and translation of genomic variation influencing drug efficacy and adverse events, while simultaneously increasing inclusion in the field and expanding globally.

Rationale: Limited information is available on racial/ethnic differences in pulmonary arterial hypertension (PAH).Objectives: Determine effects of race/ethnicity and ancestry on mortality and disease outcomes in diverse patients with PAH.Methods: Patients with Group 1 PAH were included from two national registries with genome-wide data and two local cohorts, and further incorporated in a global meta-analysis. Hazard ratios (HRs) and 95% confidence intervals (CIs) were calculated for transplant-free, all-cause mortality in Hispanic patients with non-Hispanic white (NHW) patients as the reference group. Odds ratios (ORs) for inpatient-specific mortality in patients with PAH were also calculated for race/ethnic groups from an additional National Inpatient Sample dataset not included in the meta-analysis.Measurements and Main Results: After covariate adjustment, self-reported Hispanic patients (n = 290) exhibited significantly reduced mortality versus NHW patients (n = 1,970) after global meta-analysis (HR, 0.60 [95% CI, 0.41-0.87]; P = 0.008). Although not significant, increasing Native American genetic ancestry appeared to account for part of the observed mortality benefit (HR, 0.48 [95% CI, 0.23-1.01]; P = 0.053) in the two national registries. Finally, in the National Inpatient Sample, an inpatient mortality benefit was also observed for Hispanic patients (n = 1,524) versus NHW patients (n = 8,829; OR, 0.65 [95% CI, 0.50-0.84]; P = 0.001). An inpatient mortality benefit was observed for Native American patients (n = 185; OR, 0.38 [95% CI, 0.15-0.93]; P = 0.034).Conclusions: This study demonstrates a reproducible survival benefit for Hispanic patients with Group 1 PAH in multiple clinical settings. Our results implicate contributions of genetic ancestry to differential survival in PAH.

BackgroundRare genetic variants cause pulmonary arterial hypertension, but the contribution of common genetic variation to disease risk and natural history is poorly characterised. We tested for genome-wide association for pulmonary arterial hypertension in large international cohorts and assessed the contribution of associated regions to outcomes.MethodsWe did two separate genome-wide association studies (GWAS) and a meta-analysis of pulmonary arterial hypertension. These GWAS used data from four international case-control studies across 11 744 individuals with European ancestry (including 2085 patients). One GWAS used genotypes from 5895 whole-genome sequences and the other GWAS used genotyping array data from an additional 5849 individuals. Cross-validation of loci reaching genome-wide significance was sought by meta-analysis. Conditional analysis corrected for the most significant variants at each locus was used to resolve signals for multiple associations. We functionally annotated associated variants and tested associations with duration of survival. All-cause mortality was the primary endpoint in survival analyses.FindingsA locus near SOX17 (rs10103692, odds ratio 1·80 [95% CI 1·55-2·08], p=5·13 × 10-15) and a second locus in HLA-DPA1 and HLA-DPB1 (collectively referred to as HLA-DPA1/DPB1 here; rs2856830, 1·56 [1·42-1·71], p=7·65 × 10-20) within the class II MHC region were associated with pulmonary arterial hypertension. The SOX17 locus had two independent signals associated with pulmonary arterial hypertension (rs13266183, 1·36 [1·25-1·48], p=1·69 × 10-12; and rs10103692). Functional and epigenomic data indicate that the risk variants near SOX17 alter gene regulation via an enhancer active in endothelial cells. Pulmonary arterial hypertension risk variants determined haplotype-specific enhancer activity, and CRISPR-mediated inhibition of the enhancer reduced SOX17 expression. The HLA-DPA1/DPB1 rs2856830 genotype was strongly associated with survival. Median survival from diagnosis in patients with pulmonary arterial hypertension with the C/C homozygous genotype was double (13·50 years [95% CI 12·07 to >13·50]) that of those with the T/T genotype (6·97 years [6·02-8·05]), despite similar baseline disease severity.InterpretationThis is the first study to report that common genetic variation at loci in an enhancer near SOX17 and in HLA-DPA1/DPB1 is associated with pulmonary arterial hypertension. Impairment of SOX17 function might be more common in pulmonary arterial hypertension than suggested by rare mutations in SOX17. Further studies are needed to confirm the association between HLA typing or rs2856830 genotyping and survival, and to determine whether HLA typing or rs2856830 genotyping improves risk stratification in clinical practice or trials.FundingUK NIHR, BHF, UK MRC, Dinosaur Trust, NIH/NHLBI, ERS, EMBO, Wellcome Trust, EU, AHA, ACClinPharm, Netherlands CVRI, Dutch Heart Foundation, Dutch Federation of UMC, Netherlands OHRD and RNAS, German DFG, German BMBF, APH Paris, INSERM, Université Paris-Sud, and French ANR.

Objectives ABO blood types have widespread clinical use and robust associations with disease. The purpose of this study is to evaluate the portability and suitability of tag single-nucleotide polymorphisms (tSNPs) used to determine ABO alleles and blood types across diverse populations in published literature. Materials and Methods Bibliographic databases were searched for studies using tSNPs to determine ABO alleles. We calculated linkage between tSNPs and functional variants across inferred continental ancestry groups from 1000 Genomes. We compared r2 across ancestry and assessed real-world consequences by comparing tSNP-derived blood types to serology in a diverse population from the All of Us Research Program. Results Linkage between functional variants and O allele tSNPs was significantly lower in African (median r2 = 0.443) compared to East Asian (r2 = 0.946, P  = 1.1 × 10−5) and European (r2 = 0.869, P  = .023) populations. In All of Us , discordance between tSNP-derived blood types and serology was high across all SNPs in African ancestry individuals and linkage was strongly correlated with discordance across all ancestries (ρ = −0.90, P  = 3.08 × 10−23). Discussion Many studies determine ABO blood types using tSNPs. However, tSNPs with low linkage disequilibrium promote misinference of ABO blood types, particularly in diverse populations. We observe common use of inappropriate tSNPs to determine ABO blood type, particularly for O alleles and with some tSNPs mistyping up to 58% of individuals. Conclusion Our results highlight the lack of transferability of tSNPs across ancestries and potential exacerbation of disparities in genomic research for underrepresented populations. This is especially relevant as more diverse cohorts are made publicly available. [ABSTRACT FROM AUTHOR]

Pharmacogenomic Polygenic Risk Scores (PRS) have emerged as a tool to address the polygenic nature of pharmacogenetic phenotypes, increasing the potential to predict drug response. Most pharmacogenomic PRS have been extrapolated from disease‐associated variants identified by genome wide association studies (GWAS), although some have begun to utilize genetic variants from pharmacogenomic GWAS. As pharmacogenomic PRS hold the promise of enabling precision medicine, including stratified treatment approaches, it is important to assess the opportunities and challenges presented by the current data. This assessment will help determine how pharmacogenomic PRS can be advanced and transitioned into clinical use. In this review, we present a summary of recent evidence, evaluate the current status, and identify several challenges that have impeded the progress of pharmacogenomic PRS. These challenges include the reliance on extrapolations from disease genetics and limitations inherent to pharmacogenomics research such as low sample sizes, phenotyping inconsistencies, among others. We finally propose recommendations to overcome the challenges and facilitate the clinical implementation. These recommendations include standardizing methodologies for phenotyping, enhancing collaborative efforts, developing new statistical methods to capitalize on drug‐specific genetic associations for PRS construction. Additional recommendations include enhancing the infrastructure that can integrate genomic data with clinical predictors, along with implementing user‐friendly clinical decision tools, and patient education. Ethical and regulatory considerations should address issues related to patient privacy, informed consent and safe use of PRS. Despite these challenges, ongoing research and large‐scale collaboration is likely to advance the field and realize the potential of pharmacogenomic PRS. [ABSTRACT FROM AUTHOR]

Electronic health records (EHRs) contain a vast array of phenotypic data on large numbers of individuals, often collected over decades. Due to the wealth of information, EHR data have emerged as a powerful resource to make first discoveries and identify disparities in our healthcare system. While the number of EHR‐based studies has exploded in recent years, most of these studies are directed at associations with disease rather than pharmacotherapeutic outcomes, such as drug response or adverse drug reactions. This is largely due to challenges specific to deriving drug‐related phenotypes from the EHR. There is great potential for EHR‐based discovery in clinical pharmacology research, and there is a critical need to address specific challenges related to accurate and reproducible derivation of drug‐related phenotypes from the EHR. This review provides a detailed evaluation of challenges and considerations for deriving drug‐related data from EHRs. We provide an examination of EHR‐based computable phenotypes and discuss cutting‐edge approaches to map medication information for clinical pharmacology research, including medication‐based computable phenotypes and natural language processing. We also discuss additional considerations such as data structure, heterogeneity and missing data, rare phenotypes, and diversity within the EHR. By further understanding the complexities associated with conducting clinical pharmacology research using EHR‐based data, investigators will be better equipped to design thoughtful studies with more reproducible results. Progress in utilizing EHRs for clinical pharmacology research should lead to significant advances in our ability to understand differential drug response and predict adverse drug reactions. [ABSTRACT FROM AUTHOR]

We hypothesized that generalized linear mixed models (GLMMs), which estimate the additive genetic variance underlying phenotype variability, would facilitate rapid characterization of clinical phenotypes from an electronic health record. We evaluated 1,288 phenotypes in 29,349 subjects of European ancestry with single-nucleotide polymorphism (SNP) genotyping on the Illumina Exome Beadchip. We show that genetic liability estimates are primarily driven by SNPs identified by prior genome-wide association studies and SNPs within the human leukocyte antigen (HLA) region. We identify 44 (false discovery rate q

Accuracy of warfarin dose prediction algorithms may be improved by including data from diverse populations in genetic studies of dose variability. Here, we surveyed single nucleotide polymorphisms in vitamin K‐related genetic pathways for association with warfarin dose requirements in two admixed Latino populations in standard‐principal component adjusted and contemporary‐local ancestry adjusted regression models. A total of five variants from vitamin K‐related genes/pathways were associated with warfarin dose in both cohorts (P < 0.0125) in standard models. Local ancestry‐adjusted analysis unveiled 35 associated variants with absolute effects ranging from β = 9.04 (±2.23) to 39.18 (±10.89) per ancestral allele in the discovery cohort and β = 6.47 (± 2.02) to 17.82 (± 6.83) in the replication cohort. Importantly, we demonstrate the technical validity of the Tractor model in cohorts with admixed ancestry from three founder populations and bring attention to the technical hurdles obstructing the inclusion of diverse, especially admixed, populations in pharmacogenomic research. [ABSTRACT FROM AUTHOR]

Epidemiologic evidence for the association of cholesterol and breast cancer is inconsistent. Several factors may contribute to this inconsistency, including limited sample sizes, confounding effects of antihyperlipidemic treatment, age, and body mass index, and the assumption that the association follows a simple linear function. Here, we aimed to address these factors by combining visualization and quantification a large-scale contemporary electronic health record database (the All of Us Research Program). We find clear visual and quantitative evidence that breast cancer is strongly, positively, and near-linearly associated with total cholesterol and low-density lipoprotein cholesterol, but not associated with triglycerides. The association of breast cancer with high-density lipoprotein cholesterol was non-linear and age dependent. Standardized odds ratios were 2.12 (95% confidence interval 1.9-2.48), P = 5.6 c 10-31 for total cholesterol; 1.99 (1.75-2.26), P = 2.6 x 10-26 for low-density lipoprotein cholesterol; 1.69 (1.3-2.2), P = 9.0 x 10-5 for high-density lipoprotein cholesterol at age < 56; and 0.65 (0.55-0.78), P = 1.2 x 10-6 for high-density lipoprotein cholesterol at age ≥ 56. The inclusion of the lipid levels measured after antihyperlipidemic treatment in the analysis results in erroneous associations. We demonstrate that the use of the logistic regression without inspecting risk variable linearity and accounting for confounding effects may lead to inconsistent results. [ABSTRACT FROM AUTHOR]