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The increasing volume of whole-genome sequence (WGS) and multi-omics data requires new approaches for analysis. As one solution, we have created the cloud-based Analysis Commons, which brings together genotype and phenotype data from multiple studies in a setting that is accessible by multiple investigators. This framework addresses many of the challenges of multicenter WGS analyses, including data-sharing mechanisms, phenotype harmonization, integrated multi-omics analyses, annotation and computational flexibility. In this setting, the computational pipeline facilitates a sequence-to-discovery analysis workflow illustrated here by an analysis of plasma fibrinogen levels in 3,996 individuals from the National Heart, Lung, and Blood Institute (NHLBI) Trans-Omics for Precision Medicine (TOPMed) WGS program. The Analysis Commons represents a novel model for translating WGS resources from a massive quantity of phenotypic and genomic data into knowledge of the determinants of health and disease risk in diverse human populations.

INTRODUCTION Alzheimer's disease (AD) is a complex disease influenced by genetics and environment. More than 75 susceptibility loci have been linked to late‐onset AD, but most of these loci were discovered in genome‐wide association studies (GWAS) exclusive to non‐Hispanic White individuals. There are wide disparities in AD risk across racially stratified groups, and while these disparities are not due to genetic differences, underrepresentation in genetic research can further exacerbate and contribute to their persistence. We investigated the racial/ethnic representation of participants in United States (US)‐based AD genetics and the statistical implications of current representation. METHODS We compared racial/ethnic data of participants from array and sequencing studies in US AD genetics databases, including National Institute on Aging Genetics of Alzheimer's Disease Data Storage Site (NIAGADS) and NIAGADS Data Sharing Service (dssNIAGADS), to AD and related dementia (ADRD) prevalence and mortality. We then simulated the statistical power of these datasets to identify risk variants from non‐White populations. RESULTS There is insufficient statistical power (probability <80%) to detect single nucleotide polymorphisms (SNPs) with low to moderate effect sizes (odds ratio [OR]<1.5) using array data from Black and Hispanic participants; studies of Asian participants are not powered to detect variants OR <= 2. Using available and projected sequencing data from Black and Hispanic participants, risk variants with OR = 1.2 are detectable at high allele frequencies. Sample sizes remain insufficiently powered to detect these variants in Asian populations. DISCUSSION AD genetics datasets are largely representative of US ADRD burden. However, there is a wide discrepancy between proportional representation and statistically meaningful representation. Most variation identified in GWAS of non‐Hispanic White individuals have low to moderate effects. Comparable risk variants in non‐White populations are not detectable given current sample sizes, which could lead to disparities in future studies and drug development. We urge AD genetics researchers and institutions to continue investing in recruiting diverse participants and use community‐based participatory research practices.

Polygenic risk scores (PRSs) summarize the genetic predisposition of a complex human trait or disease and may become a valuable tool for advancing precision medicine. However, PRSs that are developed in populations of predominantly European genetic ancestries can increase health disparities due to poor predictive performance in individuals of diverse and complex genetic ancestries. We describe genetic and modifiable risk factors that limit the transferability of PRSs across populations and review the strengths and weaknesses of existing PRS construction methods for diverse ancestries. Developing PRSs that benefit global populations in research and clinical settings provides an opportunity for innovation and is essential for health equity.This Review summarizes the genetic and non-genetic factors that impact the transferability of polygenic risk scores (PRSs) across populations, highlighting the technical challenges of existing PRS construction methods for diverse ancestries and the emerging resources for more widespread use of PRSs.

South Asians experience disproportionately elevated cardiometabolic disease risk yet remain underrepresented in genomic research. The OurHealth Study builds a digital biobank of US South Asian adults, integrating remote surveys, mailed biospecimens for sequencing, and electronic health record sharing to identify genetic and non-genetic drivers of cardiometabolic disease. By pairing remote participation with culturally tailored outreach, OurHealth enhances accessibility, supports granular phenotyping, and addresses logistical barriers to genomic research inclusion.

A primordial gut-epithelial innate defense response is the release of hydrogen peroxide by dual NADPH oxidase (DUOX). In inflammatory bowel disease (IBD), a condition characterized by an imbalanced gut microbiota-immune homeostasis, DUOX2 isoenzyme is the highest induced gene. Performing multiomic analyses using 2872 human participants of a wellness program, we detected a substantial burden of rare protein-altering DUOX2 gene variants of unknown physiologic significance. We identified a significant association between these rare loss-of-function variants and increased plasma levels of interleukin-17C, which is induced also in mucosal biopsies of patients with IBD. DUOX2-deficient mice replicated increased IL-17C induction in the intestine, with outlier high Il17c expression linked to the mucosal expansion of specific Proteobacteria pathobionts. Integrated microbiota/host gene expression analyses in patients with IBD corroborated IL-17C as a marker for epithelial activation by gram-negative bacteria. Finally, the impact of DUOX2 variants on IL-17C induction provided a rationale for variant stratification in case control studies that substantiated DUOX2 as an IBD risk gene. Thus, our study identifies an association of deleterious DUOX2 variants with a preclinical hallmark of disturbed microbiota-immune homeostasis that appears to precede the manifestation of IBD.

Deficiency of the immune checkpoint lymphocyte activation gene-3 (LAG3) protein is significantly associated with both elevated HDL-cholesterol (HDL-C) and myocardial infarction risk. We determined the association of genetic variants within ±500 kb of LAG3 with plasma LAG3 and defined LAG3-associated plasma proteins with HDL-C and clinical outcomes. Whole genome sequencing and plasma proteomics were obtained from the Multi-Ethnic Study of Atherosclerosis (MESA) and the Framingham Heart Study (FHS) cohorts as part of the Trans-Omics for Precision Medicine program. In situ Hi-C chromatin capture was performed in EBV-transformed cell lines isolated from four MESA participants. Genetic association analyses were performed in MESA using multivariate regression models, with validation in FHS. A LAG3-associated protein network was tested for association with HDL-C, coronary heart disease, and all-cause mortality. We identify an association between the LAG3 rs3782735 variant and plasma LAG3 protein. Proteomics analysis reveals 183 proteins significantly associated with LAG3 with four proteins associated with HDL-C. Four proteins discovered for association with all-cause mortality in FHS shows nominal associations in MESA. Chromatin capture analysis reveals significant cis interactions between LAG3 and C1S, LRIG3, TNFRSF1A , and trans interactions between LAG3 and B2M . A LAG3-associated protein network has significant associations with HDL-C and mortality. Rodriguez et al. use whole genome sequencing and plasma proteomics from the Multi-Ethnic Study of Atherosclerosis (MESA) and the Framingham Heart Study (FHS) cohorts of the Trans-Omics for Precision Medicine program and perform in situ Hi-C chromatin capture in cell lines isolated from four MESA participants. They demonstrate that lymphocyte activation gene-3 protein networks are associated with HDL-cholesterol and mortality, which could guide the development of precision medicine.

Subtle behavioral and cognitive symptoms precede schizophrenia (SCZ) and appear in individuals with elevated risk based on polygenic risk scores (SCZ-PRS) and family history of psychosis (SCZ-FH). However, most SCZ-PRS studies focus on European ancestry youth, limiting generalizability. Furthermore, it remains unclear whether SCZ-FH reflects common-variant polygenic risk or broader SCZ liability. Using baseline data from the Adolescent Brain Cognitive Development (ABCD) study, we investigated associations of SCZ-FH and SCZ-PRS with cognitive, behavioral, and emotional measures from NIH-Toolbox, Child Behavior Checklist (CBCL), and Kiddie Schedule for Affective Disorders and Schizophrenia (KSADS) for 9,636 children (mean age = 9.92 yrs, 47.4% female), specifically, 5,636 European, 2,093 African, and 1,477 Admixed American ancestry individuals. SCZ-FH was associated with SCZ-PRS (  = 0.05, FDR-  = 0.02) and subthreshold psychotic symptoms (  = 0.46, FDR-  = 0.01) in European youth, higher CBCL scores ( range = 0.36-0.6, FDR-  < 0.001), and higher odds of multiple internalizing and externalizing disorders (OR = 1.10-1.22, FDR-  < 0.001) across ancestries. SCZ-PRS was associated with lower cognition across ancestries (  = -0.43, FDR-  = 0.02), higher CBCL total problems, anxious/depressed, rule-breaking and aggressive behaviors in European youth ( range = 0.16-0.33, FDR-  < 0.04), and depressive disorders in Admixed American youth (OR = 1.37, FDR-  = 0.02). Results remained consistent when SCZ-PRS and SCZ-FH were jointly modeled. Some SCZ-FH associations weakened when income-to-needs was accounted for, suggesting that SCZ-FH may capture both genetic and environmental influences. SCZ-FH showed associations with broad psychopathology, while SCZ-PRS was associated with cognition and specific symptoms in European youth. Findings highlight their complementary role in SCZ risk assessment and the need to improve PRS utility across ancestries.

Both genetic and lifestyle factors contribute to an individual’s disease risk, suggesting a multi-omic approach is essential for personalized prevention. Studies have examined the effectiveness of lifestyle coaching on clinical outcomes, however, little is known about the impact of genetic predisposition on the response to lifestyle coaching. Here we report on the results of a real-world observational study in 2531 participants enrolled in a commercial “Scientific Wellness” program, which combines multi-omic data with personalized, telephonic lifestyle coaching. Specifically, we examined: 1) the impact of this program on 55 clinical markers and 2) the effect of genetic predisposition on these clinical changes. We identified sustained improvements in clinical markers related to cardiometabolic risk, inflammation, nutrition, and anthropometrics. Notably, improvements in HbA1c were akin to those observed in landmark trials. Furthermore, genetic markers were associated with longitudinal changes in clinical markers. For example, individuals with genetic predisposition for higher LDL-C had a lesser decrease in LDL-C on average than those with genetic predisposition for average LDL-C. Overall, these results suggest that a program combining multi-omic data with lifestyle coaching produces clinically meaningful improvements, and that genetic predisposition impacts clinical responses to lifestyle change.

Obstructive sleep apnea is a common disorder associated with increased risk for cardiovascular disease, diabetes, and premature mortality. Although there is strong clinical and epidemiologic evidence supporting the importance of genetic factors in influencing obstructive sleep apnea, its genetic basis is still largely unknown. Prior genetic studies focused on traits defined using the apnea-hypopnea index, which contains limited information on potentially important genetically determined physiologic factors, such as propensity for hypoxemia and respiratory arousability. To define novel obstructive sleep apnea genetic risk loci for obstructive sleep apnea, we conducted genome-wide association studies of quantitative traits in Hispanic/Latino Americans from three cohorts. Genome-wide data from as many as 12,558 participants in the Hispanic Community Health Study/Study of Latinos, Multi-Ethnic Study of Atherosclerosis, and Starr County Health Studies population-based cohorts were metaanalyzed for association with the apnea-hypopnea index, average oxygen saturation during sleep, and average respiratory event duration. Two novel loci were identified at genome-level significance (rs11691765, GPR83, P = 1.90 × 10 for the apnea-hypopnea index, and rs35424364; C6ORF183/CCDC162P, P = 4.88 × 10 for respiratory event duration) and seven additional loci were identified with suggestive significance (P < 5 × 10 ). Secondary sex-stratified analyses also identified one significant and several suggestive associations. Multiple loci overlapped genes with biologic plausibility. These are the first genome-level significant findings reported for obstructive sleep apnea-related physiologic traits in any population. These findings identify novel associations in inflammatory, hypoxia signaling, and sleep pathways.

We analyzed 1196 proteins in longitudinal plasma samples from participants in a commercial wellness program, including samples collected pre-diagnosis from ten cancer patients and 69 controls. For three individuals ultimately diagnosed with metastatic breast, lung, or pancreatic cancer, CEACAM5 was a persistent longitudinal outlier as early as 26.5 months pre-diagnosis. CALCA, a biomarker for medullary thyroid cancer, was hypersecreted in metastatic pancreatic cancer at least 16.5 months pre-diagnosis. ERBB2 levels spiked in metastatic breast cancer between 10.0 and 4.0 months pre-diagnosis. Our results support the value of deep phenotyping seemingly healthy individuals in prospectively inferring disease transitions.