Protocol for a type 3 hybrid implementation cluster randomized clinical trial to evaluate the effect of patient and clinician nudges to advance the use of genomic medicine across a diverse health system.

BACKGROUND: Germline genetic testing is recommended for an increasing number of conditions with underlying genetic etiologies, the results of which impact medical management. However, genetic testing is underutilized in clinics due to system, clinician, and patient level barriers. Behavioral economics provides a framework to create implementation strategies, such as nudges, to address these multi-level barriers and increase the uptake of genetic testing for conditions where the results impact medical management. METHODS: Patients meeting eligibility for germline genetic testing for a group of conditions will be identified using electronic phenotyping algorithms. A pragmatic, type 3 hybrid cluster randomization study will test nudges to patients and/or clinicians, or neither. Clinicians who receive nudges will be prompted to either refer their patient to genetics or order genetic testing themselves. We will use rapid cycle approaches informed by clinician and patient experiences, health equity, and behavioral economics to optimize these nudges before trial initiation. The primary implementation outcome is uptake of germline genetic testing for the pre-selected health conditions. Patient data collected through the electronic health record (e.g. demographics, geocoded address) will be examined as moderators of the effect of nudges. DISCUSSION: This study will be one of the first randomized trials to examine the effects of patient- and clinician-directed nudges informed by behavioral economics on uptake of genetic testing. The pragmatic design will facilitate a large and diverse patient sample, allow for the assessment of genetic testing uptake, and provide comparison of the effect of different nudge combinations. This trial also involves optimization of patient identification, test selection, ordering, and result reporting in an electronic health record-based infrastructure to further address clinician-level barriers to utilizing genomic medicine. The findings may help determine the impact of low-cost, sustainable implementation strategies that can be integrated into health care systems to improve the use of genomic medicine. TRIAL REGISTRATION: ClinicalTrials.gov. NCT06377033. Registered on March 31, 2024. https://clinicaltrials.gov/study/NCT06377033?term=NCT06377033&rank=1.

Genetic risk factors for COVID-19 and influenza are largely distinct.

Coronavirus disease 2019 (COVID-19) and influenza are respiratory illnesses caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and influenza viruses, respectively. Both diseases share symptoms and clinical risk factors1, but the extent to which these conditions have a common genetic etiology is unknown. This is partly because host genetic risk factors are well characterized for COVID-19 but not for influenza, with the largest published genome-wide association studies for these conditions including >2 million individuals2 and about 1,000 individuals3-6, respectively. Shared genetic risk factors could point to targets to prevent or treat both infections. Through a genetic study of 18,334 cases with a positive test for influenza and 276,295 controls, we show that published COVID-19 risk variants are not associated with influenza. Furthermore, we discovered and replicated an association between influenza infection and noncoding variants in B3GALT5 and ST6GAL1, neither of which was associated with COVID-19. In vitro small interfering RNA knockdown of ST6GAL1-an enzyme that adds sialic acid to the cell surface, which is used for viral entry-reduced influenza infectivity by 57%. These results mirror the observation that variants that downregulate ACE2, the SARS-CoV-2 receptor, protect against COVID-19 (ref. 7). Collectively, these findings highlight downregulation of key cell surface receptors used for viral entry as treatment opportunities to prevent COVID-19 and influenza.

p-TsOH-Mediated Intramolecular C2-Arylation on NH-Indoles: Access of 5,10-Dihydroindeno[1,2-b]indoles.

5,10-Dihydroindeno[1,2-b]indole has served as an important starting precursor for BARAC-fluor reagent in medicinal chemistry. Herein, an unprecedented p-TsOH assisted intramolecular C2-arylation of NH-indoles via C(sp2)-CN/C(sp2)-H coupling, offering a series of 5,10-dihydroindeno[1,2-b]indoles with moderate to good yields, has been showcased under redox-neutral conditions. Furthermore, successful scalability and synthetic applications highlight the practical nature of the method.

Unlocking the role of herbal cosmeceutical in anti-ageing and skin ageing associated diseases.

The process of skin ageing is a natural biological phenomenon characterised by the emergence of wrinkles, age spots, sagging skin, and dryness over time. The increasing significance of skin in physical attractiveness has heightened skincare concerns. Anti-ageing cosmetics play a pivotal role in nurturing the skin, enhancing its quality, and promoting overall health. Today, cosmetics have evolved beyond mere aesthetics and are now integral to individual wellness. The contemporary quest for perpetual youth has intensified, prompting a deeper exploration into the skin ageing process. This comprehensive exploration delves into various elements involved in skin ageing, encompassing cells such as stem and endothelial cells, blood vessels, soft tissues, and signalling pathways. The molecular basis of skin ageing, including biochemical factors like reactive oxygen species, damaged DNA, free radicals, ions, and proteins (mRNA), is scrutinised alongside relevant animal models. The article critically analyzes the outcomes of utilising herbal components, emphasising their advantageous anti-ageing properties. The factors contributing to skin ageing, mechanistic perspectives, management approaches involving herbal cosmeceutical, and associated complications (especially cardiovascular diseases, Parkinson's, Alzheimer's, etc.) are succinctly addressed. In addition, the manuscript further summarises the recent patented innovations and toxicity of the herbal cosmeceuticals for anti-ageing and ageing associated disorders. Despite progress, further research is imperative to unlock the full potential of herbal components as anti-ageing agents.

Diversity and scale: Genetic architecture of 2068 traits in the VA Million Veteran Program.

One of the justifiable criticisms of human genetic studies is the underrepresentation of participants from diverse populations. Lack of inclusion must be addressed at-scale to identify causal disease factors and understand the genetic causes of health disparities. We present genome-wide associations for 2068 traits from 635,969 participants in the Department of Veterans Affairs Million Veteran Program, a longitudinal study of diverse United States Veterans. Systematic analysis revealed 13,672 genomic risk loci; 1608 were only significant after including non-European populations. Fine-mapping identified causal variants at 6318 signals across 613 traits. One-third (n = 2069) were identified in participants from non-European populations. This reveals a broadly similar genetic architecture across populations, highlights genetic insights gained from underrepresented groups, and presents an extensive atlas of genetic associations.

Accelerating Genome- and Phenome-Wide Association Studies using GPUs - A case study using data from the Million Veteran Program.

The expansion of biobanks has significantly propelled genomic discoveries yet the sheer scale of data within these repositories poses formidable computational hurdles, particularly in handling extensive matrix operations required by prevailing statistical frameworks. In this work, we introduce computational optimizations to the SAIGE (Scalable and Accurate Implementation of Generalized Mixed Model) algorithm, notably employing a GPU-based distributed computing approach to tackle these challenges. We applied these optimizations to conduct a large-scale genome-wide association study (GWAS) across 2,068 phenotypes derived from electronic health records of 635,969 diverse participants from the Veterans Affairs (VA) Million Veteran Program (MVP). Our strategies enabled scaling up the analysis to over 6,000 nodes on the Department of Energy (DOE) Oak Ridge Leadership Computing Facility (OLCF) Summit High-Performance Computer (HPC), resulting in a 20-fold acceleration compared to the baseline model. We also provide a Docker container with our optimizations that was successfully used on multiple cloud infrastructures on UK Biobank and All of Us datasets where we showed significant time and cost benefits over the baseline SAIGE model.

CT imaging-derived phenotypes for abdominal muscle and their association with age and sex in a medical biobank.

The study of muscle mass as an imaging-derived phenotype (IDP) may yield new insights into determining the normal and pathologic variations in muscle mass in the population. This can be done by determining 3D abdominal muscle mass from 12 distinct abdominal muscle regions and groups using computed tomography (CT) in a racially diverse medical biobank. To develop a fully automatic technique for assessment of CT abdominal muscle IDPs and preliminarily determine abdominal muscle IDP variations with age and sex in a clinically and racially diverse medical biobank. This retrospective study was conducted using the Penn Medicine BioBank (PMBB), a research protocol that recruits adult participants during outpatient visits at hospitals in the Penn Medicine network. We developed a deep residual U-Net (ResUNet) to segment 12 abdominal muscle groups including the left and right psoas, quadratus lumborum, erector spinae, gluteus medius, rectus abdominis, and lateral abdominals. 110 CT studies were randomly selected for training, validation, and testing. 44 of the 110 CT studies were selected to enrich the dataset with representative cases of intra-abdominal and abdominal wall pathology. The studies were divided into non-overlapping training, validation and testing sets. Model performance was evaluated using the Sørensen-Dice coefficient. Volumes of individual muscle groups were plotted to distribution curves. To investigate associations between muscle IDPs, age, and sex, deep learning model segmentations were performed on a larger abdominal CT dataset from PMBB consisting of 295 studies. Multivariable models were used to determine relationships between muscle mass, age and sex. The model's performance (Dice scores) on the test data was the following: psoas: 0.85 ± 0.12, quadratus lumborum: 0.72 ± 0.14, erector spinae: 0.92 ± 0.07, gluteus medius: 0.90 ± 0.08, rectus abdominis: 0.85 ± 0.08, lateral abdominals: 0.85 ± 0.09. The average Dice score across all muscle groups was 0.86 ± 0.11. Average total muscle mass for females was 2041 ± 560.7 g with a high of 2256 ± 560.1 g (41-50 year old cohort) and a change of - 0.96 g/year, declining to an average mass of 1579 ± 408.8 g (81-100 year old cohort). Average total muscle mass for males was 3086 ± 769.1 g with a high of 3385 ± 819.3 g (51-60 year old cohort) and a change of - 1.73 g/year, declining to an average mass of 2629 ± 536.7 g (81-100 year old cohort). Quadratus lumborum was most highly correlated with age for both sexes (correlation coefficient of - 0.5). Gluteus medius mass in females was positively correlated with age with a coefficient of 0.22. These preliminary findings show that our CNN can automate detailed abdominal muscle volume measurement. Unlike prior efforts, this technique provides 3D muscle segmentations of individual muscles. This technique will dramatically impact sarcopenia diagnosis and research, elucidating its clinical and public health implications. Our results suggest a peak age range for muscle mass and an expected rate of decline, both of which vary between genders. Future goals are to investigate genetic variants for sarcopenia and malnutrition, while describing genotype-phenotype associations of muscle mass in healthy humans using imaging-derived phenotypes. It is feasible to obtain 3D abdominal muscle IDPs with high accuracy from patients in a medical biobank using fully automated machine learning methods. Abdominal muscle IDPs showed significant variations in lean mass by age and sex. In the future, this tool can be leveraged to perform a genome-wide association study across the medical biobank and determine genetic variants associated with early or accelerated muscle wasting.

Diagnosis of the Initial Stage of Hepatocellular Carcinoma: A Review.

Hepatocellular carcinoma (HCC) is the third most common cause of cancer-related deaths worldwide. There may be more than a million instances of hepatocellular carcinoma by 2025, making it a persistent concern for global health. The most common form of hepatocellular carcinoma accounts for more than 90% of cases. There is no known cure for hepatocellular carcinoma, which is usually detected late in life. Unlike most other common malignancies, such as lung, prostate, and breast cancers, where mortality rates are declining, rates of death are rising by around 2-3% every year. It is extremely difficult to diagnose hepatocellular carcinoma in its early stages. Alpha-fetoprotein serology studies and ultrasonography (US) monitoring were historically the primary methods for early detection of hepatocellular cancer. However, the sensitivity or specificity of ultrasonography/alpha-fetoprotein (US/AFP) is not high enough to detect hepatocellular carcinoma in its early stages. Alpha-fetoprotein, or AFP, is an amino acid that is normally produced by the liver or yolk sac of an embryonic baby. In adults, AFP levels are typically modest. Adults with high levels of AFP have been associated with several illnesses, the most well-known of which are certain types of cancer. It is still possible to diagnose hepatocellular carcinoma early because of current technological advancements. We address the advancements in the diagnosis of hepatocellular carcinoma in this article, with a focus on new imaging techniques and diagnostic markers for early-stage tumor identification.

Transdermal Delivery of Niacin Through Polysaccharide Films Ameliorates Cutaneous Flushing in Experimental Wistar Rats.

BACKGROUND: Niacin, an established therapeutic for dyslipidemia, is hindered by its propensity to induce significant cutaneous flushing when administered orally in its unmodified state, thereby constraining its clinical utility. OBJECTIVE: This study aimed to fabricate, characterize, and assess the in-vitro and in-vivo effectiveness of niacin-loaded polymeric films (NLPFs) comprised of carboxymethyl tamarind seed polysaccharide. The primary objective was to mitigate the flushing-related side effects associated with oral niacin administration. METHODS: NLPFs were synthesized using the solvent casting method and subsequently subjected to characterization, including assessments of tensile strength, moisture uptake, thickness, and folding endurance. Surface characteristics were analyzed using a surface profiler and scanning electron microscopy (SEM). Potential interactions between niacin and the polysaccharide core were investigated through X-ray diffraction experiments (XRD) and Fourier transform infrared spectroscopy (FTIR). The viscoelastic properties of the films were explored using a Rheometer. In-vitro assessments included drug release studies, swelling behavior assays, and antioxidant assays. In-vivo efficacy was evaluated through skin permeation assays, skin irritation assays, and histopathological analyses. RESULTS: NLPFs exhibited a smooth texture with favorable tensile strength and moisture absorption capabilities. Niacin demonstrated interaction with the polysaccharide core, rendering the films amorphous. The films displayed slow and sustained drug release, exceptional antioxidant properties, optimal swelling behavior, and viscoelastic characteristics. Furthermore, the films exhibited biocompatibility and non-toxicity towards skin cells. CONCLUSION: NLPFs emerged as promising carrier systems for the therapeutic transdermal delivery of niacin, effectively mitigating its flushing-associated adverse effects.

Tunable Q-factor wavelet transform based identification of diabetic patients using ECG signals.

Diabetes is a chronic health condition that is characterized by increased levels of glucose (sugar) in the blood. It can have harmful effects on different parts of the body, such as the retina of the eyes, skin, nervous system, kidneys, and heart. Diabetes affects the structure of electrocardiogram (ECG) impulses by causing cardiovascular autonomic dysfunction. Multi-resolution analysis of the input ECG signal is utilized in this paper to develop a machine learning-based system for the automated detection of diabetic patients. In the first step, the input ECG signal is decomposed into sub-bands utilizing the tunable Q-factor wavelet transform (TQWT) technique. In the second step, four entropy-based characteristics are evaluated from each SB and elected using the K-W test method. To develop an automatic diabetes detection system, selected features are given as input with 10-fold validation to a SVM classifier using various kernel functions. The 3rd sub-band of TQWT with the Coarse Gaussian kernel function kernel of the SVM classifier yields a classification accuracy of 91.5%. In the same dataset, the comparative analysis demonstrates that the proposed method outperforms other existing methods.

A Review on Lipid-Based Nanoformulations for Targeting Brain through Non-invasive Nasal Route.

The nasal method for administering nanoformulations to the brain has been examined and proven successful by prior investigators. For the treatment of central nervous system (CNS) disorders such as neuropsychiatric, depression, Alzheimer and anxiety, intranasal administration has become more popular for delivering drugs to the brain. This method offers direct transport through neuronal pathways. The lipid-based nanocarriers like nanostructured lipid carriers (NLC) appear more favorable than other nanosystems for brain administration. The nanostructured lipid carriers (NLC) system can quickly transform into a gelling system to facilitate easy administration into the nasal passages. The various compatibility studies showed that the other lipid structured-based formulations may not work well for various reasons, including a low drug filing capacity; during storage, the formulation showed changes in the solid lipid structures, which gives a chance of medication ejection. Formulations containing NLC can minimize these problems by improving drug solubility and permeation rate by incorporating a ratio of liquid lipids with solid lipids, resulting in improved stability during storage and drug bioavailability because of the higher drug loading capacity. This review aimed to find and emphasize research on lipid-based nanocarrier formulations that have advanced the treatment of central nervous system illnesses using nasal passages to reach the targeted area's drug molecules.

Uncovering genetic associations in the human diseasome using an endophenotype-augmented disease network.

MOTIVATION: Many diseases, particularly cardiometabolic disorders, exhibit complex multimorbidities with one another. An intuitive way to model the connections between phenotypes is with a disease-disease network (DDN), where nodes represent diseases and edges represent associations, such as shared single-nucleotide polymorphisms (SNPs), between pairs of diseases. To gain further genetic understanding of molecular contributors to disease associations, we propose a novel version of the shared-SNP DDN (ssDDN), denoted as ssDDN+, which includes connections between diseases derived from genetic correlations with intermediate endophenotypes. We hypothesize that a ssDDN+ can provide complementary information to the disease connections in a ssDDN, yielding insight into the role of clinical laboratory measurements in disease interactions. RESULTS: Using PheWAS summary statistics from the UK Biobank, we constructed a ssDDN+ revealing hundreds of genetic correlations between diseases and quantitative traits. Our augmented network uncovers genetic associations across different disease categories, connects relevant cardiometabolic diseases, and highlights specific biomarkers that are associated with cross-phenotype associations. Out of the 31 clinical measurements under consideration, HDL-C connects the greatest number of diseases and is strongly associated with both type 2 diabetes and heart failure. Triglycerides, another blood lipid with known genetic causes in non-mendelian diseases, also adds a substantial number of edges to the ssDDN. This work demonstrates how association with clinical biomarkers can better explain the shared genetics between cardiometabolic disorders. Our study can facilitate future network-based investigations of cross-phenotype associations involving pleiotropy and genetic heterogeneity, potentially uncovering sources of missing heritability in multimorbidities. AVAILABILITY AND IMPLEMENTATION: The generated ssDDN+ can be explored at https://hdpm.biomedinfolab.com/ddn/biomarkerDDN.

Emulgel: A Cutting Edge Approach for Topical Drug Delivery System.

Emulgel is considered an advanced leading form of topical drug delivery system. It possesses the quality of a dual control drug mechanism for drug release as it holds the properties of both gel as well as emulsion. Emulgel is capable of overcoming the problems of the conventional route of topical drug delivery, like low spreadability and stickiness with the delivery of hydrophobic drugs, enhanced bioavailability at the local site of action, no greasy texture, and ensuring patient compliance. An emulsion is used either w/o or o/w, and the drug can be incorporated into the suitable phase of the emulsion. After that, the emulsion is incorporated into the gel phase. Several factors like oil phase, gelling agent, and emulsifier can affect the efficacy and stability. This advancement is beneficial not only for dermatology but also for cosmetology as well. Currently, emulgel-based formulations are used for the delivery of anti-inflammatory, analgesic, anti-acne, and antifungal drugs with a wide array of exploration.

Novel ancestry-specific primary open-angle glaucoma loci and shared biology with vascular mechanisms and cell proliferation.

Primary open-angle glaucoma (POAG), a leading cause of irreversible blindness globally, shows disparity in prevalence and manifestations across ancestries. We perform meta-analysis across 15 biobanks (of the Global Biobank Meta-analysis Initiative) (n = 1,487,441: cases = 26,848) and merge with previous multi-ancestry studies, with the combined dataset representing the largest and most diverse POAG study to date (n = 1,478,037: cases = 46,325) and identify 17 novel significant loci, 5 of which were ancestry specific. Gene-enrichment and transcriptome-wide association analyses implicate vascular and cancer genes, a fifth of which are primary ciliary related. We perform an extensive statistical analysis of SIX6 and CDKN2B-AS1 loci in human GTEx data and across large electronic health records showing interaction between SIX6 gene and causal variants in the chr9p21.3 locus, with expression effect on CDKN2A/B. Our results suggest that some POAG risk variants may be ancestry specific, sex specific, or both, and support the contribution of genes involved in programmed cell death in POAG pathogenesis.

Clinical correlates of CT imaging-derived phenotypes among lean and overweight patients with hepatic steatosis.

The objective of this study is to define CT imaging derived phenotypes for patients with hepatic steatosis, a common metabolic liver condition, and determine its association with patient data from a medical biobank. There is a need to further characterize hepatic steatosis in lean patients, as its epidemiology may differ from that in overweight patients. A deep learning method determined the spleen-hepatic attenuation difference (SHAD) in Hounsfield Units (HU) on abdominal CT scans as a quantitative measure of hepatic steatosis. The patient cohort was stratified by BMI with a threshold of 25 kg/m2 and hepatic steatosis with threshold SHAD ≥ - 1 HU or liver mean attenuation ≤ 40 HU. Patient characteristics, diagnoses, and laboratory results representing metabolism and liver function were investigated. A phenome-wide association study (PheWAS) was performed for the statistical interaction between SHAD and the binary characteristic LEAN. The cohort contained 8914 patients-lean patients with (N = 278, 3.1%) and without (N = 1867, 20.9%) steatosis, and overweight patients with (N = 1863, 20.9%) and without (N = 4906, 55.0%) steatosis. Among all lean patients, those with steatosis had increased rates of cardiovascular disease (41.7 vs 27.8%), hypertension (86.7 vs 49.8%), and type 2 diabetes mellitus (29.1 vs 15.7%) (all p < 0.0001). Ten phenotypes were significant in the PheWAS, including chronic kidney disease, renal failure, and cardiovascular disease. Hepatic steatosis was found to be associated with cardiovascular, kidney, and metabolic conditions, separate from overweight BMI.

Polygenic risk scores for cardiometabolic traits demonstrate importance of ancestry for predictive precision medicine.

Polygenic risk scores (PRS) have predominantly been derived from genome-wide association studies (GWAS) conducted in European ancestry (EUR) individuals. In this study, we present an in-depth evaluation of PRS based on multi-ancestry GWAS for five cardiometabolic phenotypes in the Penn Medicine BioBank (PMBB) followed by a phenome-wide association study (PheWAS). We examine the PRS performance across all individuals and separately in African ancestry (AFR) and EUR ancestry groups. For AFR individuals, PRS derived using the multi-ancestry LD panel showed a higher effect size for four out of five PRSs (DBP, SBP, T2D, and BMI) than those derived from the AFR LD panel. In contrast, for EUR individuals, the multi-ancestry LD panel PRS demonstrated a higher effect size for two out of five PRSs (SBP and T2D) compared to the EUR LD panel. These findings underscore the potential benefits of utilizing a multi-ancestry LD panel for PRS derivation in diverse genetic backgrounds and demonstrate overall robustness in all individuals. Our results also revealed significant associations between PRS and various phenotypic categories. For instance, CAD PRS was linked with 18 phenotypes in AFR and 82 in EUR, while T2D PRS correlated with 84 phenotypes in AFR and 78 in EUR. Notably, associations like hyperlipidemia, renal failure, atrial fibrillation, coronary atherosclerosis, obesity, and hypertension were observed across different PRSs in both AFR and EUR groups, with varying effect sizes and significance levels. However, in AFR individuals, the strength and number of PRS associations with other phenotypes were generally reduced compared to EUR individuals. Our study underscores the need for future research to prioritize 1) conducting GWAS in diverse ancestry groups and 2) creating a cosmopolitan PRS methodology that is universally applicable across all genetic backgrounds. Such advances will foster a more equitable and personalized approach to precision medicine.

Neuropathy, its Profile and Experimental Nerve Injury Neuropathic Pain Models: A Review.

Neuropathy is a terrible disorder that has a wide range of etiologies. Drug-induced neuropathy, which happens whenever a chemical agent damages the peripheral nerve system, has been linked here to the iatrogenic creation of some drugs. It is potentially permanent and causes sensory impairments and paresthesia that typically affects the hands, feet, and stockings; motor participation is uncommon. It might appear suddenly or over time, and the long-term outlook varies. The wide range of chronic pain conditions experienced by people has been one of the main obstacles to developing new, more effective medications for the treatment of neuropathic pain. Animal models can be used to examine various neuropathic pain etiologies and symptoms. Several models investigate the peripheral processes of neuropathic pain, whereas some even investigate the central mechanisms, such as drug induce models like vincristine, cisplatin, bortezomib, or thalidomide, etc., and surgical models like sciatic nerve chronic constriction injury (CCI), sciatic nerve ligation through spinal nerve ligation (SNL), sciatic nerve damage caused by a laser, SNI (spared nerve injury), etc. The more popular animal models relying on peripheral nerve ligatures are explained. In contrast to chronic sciatic nerve contraction, which results in behavioral symptoms of less reliable stressful neuropathies, (SNI) spared nerve injury generates behavioral irregularities that are more feasible over a longer period. This review summarizes the latest methods models as well as clinical ideas concerning this mechanism. Every strongest current information on neuropathy is discussed, along with several popular laboratory models for causing neuropathy.

Diversity and Scale: Genetic Architecture of 2,068 Traits in the VA Million Veteran Program.

Genome-wide association studies (GWAS) have underrepresented individuals from non-European populations, impeding progress in characterizing the genetic architecture and consequences of health and disease traits. To address this, we present a population-stratified phenome-wide GWAS followed by a multi-population meta-analysis for 2,068 traits derived from electronic health records of 635,969 participants in the Million Veteran Program (MVP), a longitudinal cohort study of diverse U.S. Veterans genetically similar to the respective African (121,177), Admixed American (59,048), East Asian (6,702), and European (449,042) superpopulations defined by the 1000 Genomes Project. We identified 38,270 independent variants associating with one or more traits at experiment-wide P<4.6×10-11 significance; fine-mapping 6,318 signals identified from 613 traits to single-variant resolution. Among these, a third (2,069) of the associations were found only among participants genetically similar to non-European reference populations, demonstrating the importance of expanding diversity in genetic studies. Our work provides a comprehensive atlas of phenome-wide genetic associations for future studies dissecting the architecture of complex traits in diverse populations.