Integrated modeLs for Early Risk-prediction in Africa (ILERA) study
非洲早期风险预测综合模型 (ILERA) 研究
基本信息
- 批准号:10712951
- 负责人:
- 金额:$ 25万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 财政年份:2023
- 资助国家:美国
- 起止时间:2023-09-20 至 2026-07-31
- 项目状态:未结题
- 来源:
- 关键词:AddressAdipose tissueAfricaAfricanAgeBloodBlood PressureBody mass indexBurkina FasoCardiometabolic DiseaseCategoriesCessation of lifeCholesterolCollaborationsCommunicable DiseasesComplexCountryDataData CollectionData ScienceData SetDiastolic blood pressureDietDiseaseDisease OutcomeDrug TargetingDrug usageEnvironmentEuropeanEvaluationExerciseGene ExpressionGenerationsGenetic RiskGenomicsGenotypeGenotype-Tissue Expression ProjectGhanaGrantHealthHealth PrioritiesHealthcareHigh Density LipoproteinsHypertensionIndividualIndustrializationKenyaKidney DiseasesLife StyleLipidsLongevityLow-Density LipoproteinsMedical centerMethodsModelingObesityParticipantPopulationPulse RatesQuality of lifeRenal functionResearchResource-limited settingRiskScienceSmokingSocioeconomic StatusSouth AfricaSouth AfricanSystemTechnologyTestingTimeTriglyceridesUniversitiesVariantVisceralWaist-Hip RatioWhole BloodWitZimbabweage relatedbiomarker identificationcohortdeep learningdisorder riskexperiencefunctional genomicsgenome resourcegenome wide association studygenome-widegenomic datahigh riskimprovedindustry partnerinsightmultiple omicsnon-geneticnovelpolygenic risk scorepopulation basedpopulation stratificationpredictive modelingprematurepublic health interventionrisk predictionrisk prediction modelstatisticssubcutaneoustraittranscriptometranscriptome sequencingtranscriptomicstranslational potentialwhole genome
项目摘要
Project Summary
Cardiometabolic diseases (CMDs) claim millions of lives in Africa every year and a sizable portion of these
deaths are premature. Despite the availability of simple and affordable approaches such as lifestyle adjustment
and the use of drugs (e.g. lipid lowering statins) that could increase lifespan and improve the quality of life, this
is becoming a more serious health burden in Africa with time. The ability to prioritize healthcare to the populations
that are at highest risk could be especially relevant in resource constrained environments. One of the major
challenges to accurately stratifying a population by risk is the low predictivity of current polygenic risk scoring
models (PRSs) in African populations.
The Integrated modeLs for Early Risk-prediction in Africa (ILERA) study (Ilera in Yourba means health) aims
to investigate the potential for improving the prediction of 13 cardiometabolic disease indicator levels (and
thereby of CMDs) by integrating diverse types of data (genomic, transcriptomic, lifestyle-related data) into risk
prediction models. Starting with currently best performing PRSs, we plan to progressively add layers of data
such as predicted transcriptomes, environment and lifestyle information to assess whether this additional data,
either independently or in combination with others, could improve prediction. To allow for complex and non-linear
interactions between these factors, data-driven approaches will be employed to integrate these variables with
the genomic data. In-depth evaluation of the predictivity of these models will be performed in independent cohorts
from South, East and West Africa and also in longitudinal data from the same cohort. The potential for an early
warning system aimed at public health intervention will be investigated using a combination of the best predictive
models and traits.
The project will be led from the University of the Witwatersrand (Wits), collaborating with the Wits Donald Gordon
Medical Center, the African Institute of Biomedical Science and Technology (ABiST) Zimbabwe and an US
based industry partner, Variant Bio. The predicted transcriptome will be based on 750 South African participants
with whole genome sequence and blood transcriptome RNA-Seq. The primary target dataset of ~5000
participants was generated through the H3Africa AWI-Gen study and the models will be tested in two Southern
African datasets (~1200 participants from South Africa and Zimbabwe) as well as ~6000 participants from
Ghana, Burkina Faso and Kenya. Longitudinal data, captured 5 years after baseline data collection, will be used
to understand the impact of age on the predictive models. The study will build on years of existing successful
collaboration and will tap into the Wits experience in genomics research, Variant Bio’s expertise in multi-omics
research and leverage partnership with other projects in the DSI-Africa consortium for data science capacity.
项目总结
项目成果
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