grant

Comprehensive Polygenic Risk Profiling Across Multiple Health Outcomes (CARDINAL)

Organization UNIVERSITY OF MARYLAND BALTIMORELocation BALTIMORE, UNITED STATESPosted 8 Jun 2021Deadline 31 Mar 2027
NIHUS FederalResearch GrantFY202521+ years oldAccelerationActive Follow-upAdmixtureAdultAdult HumanAdult-Onset Diabetes MellitusAfricanAfrican AmericanAfrican ancestryAfrican descentAfro AmericanAfroamericanAlgorithmsAllelesAllelomorphsBreast CancerCalibrationCancersCardiometabolic DiseaseCardiometabolic DisorderCervical CancerCervix CancerCessation of lifeChronic DiseaseChronic IllnessClassificationClinicalClinical ManagementCodeCoding SystemComplexDataData CollectionData SetDeathDevelopmentDiagnosisDiseaseDisorderEndocrine Gland SecretionEnrollmentEthnic OriginEthnicityEvaluationFamilyGene variantGene x Environment InteractionGeneticGenetic DiversityGenetic VariationGenomeGenotypeGuidelinesGxE interactionHPVHealthHealth CareHealth PolicyHereditaryHeterogeneityHistoryHormonesHuman Papilloma VirusHuman PapillomavirusHypertensionIndividualInfectionInfectious Human Wart VirusInheritedIntervention StrategiesJamaicanKetosis-Resistant Diabetes MellitusLaboratoriesLinkage DisequilibriumMalignant Breast NeoplasmMalignant Cervical NeoplasmMalignant Cervical TumorMalignant Neoplasm of the CervixMalignant NeoplasmsMalignant TumorMalignant Tumor of the CervixMalignant Tumor of the Cervix UteriMalignant Uterine Cervix NeoplasmMalignant Uterine Cervix TumorMalignant neoplasm of cervix uteriMapsMaturity-Onset Diabetes MellitusMeasuresMetabolicMethodsModelingMorbidityMorbidity - disease rateNIDDMNon-Insulin Dependent DiabetesNon-Insulin-Dependent Diabetes MellitusNoninsulin Dependent DiabetesNoninsulin Dependent Diabetes MellitusNorth AfricaNorth AmericaNorthern AfricaOutcomeParticipantPatientsPerformancePhenotypePopulationPredispositionPreventative strategyPrevention strategyPreventive strategyProcessProtocolProtocols documentationPublic HealthQuality ControlRecording of previous eventsReportingResearchResearch ResourcesResourcesRiskScientistScoring MethodSiteSlow-Onset Diabetes MellitusStable Diabetes MellitusSusceptibilitySystemSystematicsT2 DMT2DT2DMTarget PopulationsTherapeutic HormoneType 2 Diabetes MellitusType 2 diabetesType II Diabetes MellitusType II diabetesUnited StatesUterine Cervix CancerVariantVariationVascular Hypertensive DiseaseVascular Hypertensive Disorderactive followupadult onset diabetesadulthoodallelic variantcancer riskchronic disordercohortdashboarddata harmonizationdata integrationdata standardizationdata standardsdevelopmentaldiagnostic approachdiagnostic strategyenrollenvironment effect on genefollow upfollow-upfollowed upfollowupgene environment interactiongenetic risk assessmentgenetic variantgenomic datagenomic datasetgenomic variantharmonized datahealth care policyhealth recordhigh blood pressurehistorieshormone related cancerhyperpiesiahyperpiesishypertensive diseasehypertensive disorderimprovedinnovateinnovationinnovativeinteractive toolketosis resistant diabeteslife-style datalifestyle datamachine learning based methodmachine learning based pipelinemachine learning methodmachine learning methodologiesmachine learning pipelinemalignancymalignant breast tumormaturity onset diabetesmortalitymulti-ethnicmultiethnicneoplasm/cancernovelphenotypic datapolygenetic risk scorespolygenic predictorspolygenic risk scorepolygenic scorespopulation stratificationpreventpreventingstatistical learningtooltraittype 2 DMtype II DMtype two diabeteswart virus
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Full Description

Non-communicable conditions such as type 2 diabetes and hypertension; breast and cervical cancer; and infections like human papillomavirus that elevate malignancy risk comprise a significant share of illness and death in the United States. Traditional risk models rely on family history and clinical measures but often miss the full spectrum of inherited variation. Polygenic risk scores (PRSs) integrate thousands of genetic variants to quantify individual susceptibility, yet many scores lose precision when applied outside their original development cohort. The Comprehensive Polygenic Risk Profiling Across Multiple Health Outcomes (CARDINAL) Study Site will merge high-quality genomic data with health records, laboratory results, and longitudinal follow-up for more than 50,000 adults enrolled through US research initiatives. By harmonizing quality-controlled genotypes with richly annotated phenotype data, CARDINAL will enable a rigorous evaluation of existing PRSs for metabolic traits, infection-associated cancer risk, and hormone-driven malignancies. We will implement advanced statistical and machine-learning pipelines that adjust for genetic substructure, local ancestry, and gene- environment interactions, refining risk models to improve calibration and reclassification in real-world patient cohorts. Innovative tools will map individual and aggregate risk distributions, highlight PRS performance relative to conventional predictors, and facilitate rigorous comparisons of modeling strategies. Through collaborative partnerships with national research consortia and healthcare networks, CARDINAL will create an open system for sharing summary results, analysis code, and reporting guidelines. By developing, validating, and disseminating robust PRS tools within a U.S. healthcare context, CARDINAL aims to accelerate the incorporation of genetic risk assessment into routine clinical workflows, support risk-tailored intervention strategies, and ultimately reduce the burden of common chronic diseases, hormone-related cancers, and infection-associated
malignancies.

Grant Number: 5U01HG011717-05
NIH Institute/Center: NIH

Principal Investigator: Sally Adebamowo

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Comprehensive Polygenic Risk Profiling Across Multiple Health Outcomes (CARDINAL) — UNIVERSITY OF MARYLAND BALTIMORE | U | Dev Procure