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Study Sites


Center for Admixed Populations and Health Equity

Population admixture has brought together genomes from continental populations that have diverged primarily because of genetic drift, but also because of different selective pressures.  Although disentangling interplay of genetic and social determinants of health in admixed populations is critical for equitable medicine, admixed individuals represent only ~4% of participants in current genomic research databases. CAPE aims to develop and deploy methods for admixture-PRS that provide equitable utility for every admixed individual irrespective of their genetic ancestry, by leveraging one of the largest dataset of admixed individuals of >230,000 individuals across EHR- and cohort-based studies to build, test and calibrate admixture-PRS. This project is led by Bogdan Pasaniuc (UCLA, contact PI), Eimear Kenny (Mt. Sinai, MPI), and Leslie Lange (U of Colorado, MPI).


CARdiometabolic Disorders IN African-ancestry popuLations

Sally Adebamowo (University of Maryland), contact PI, is joined by multiple PI Bamidele Tayo (Loyola University of Chicago) for the CARDINAL (CARdiometabolic Disorders IN African-ancestry popuLations) study. They will leverage core cohorts of African-ancestry participants to understand polygenic risk for cardiometabolic phenotypes (traits, endpoints, and potential mediators and confounders).


Diabetes Polygenic RIsk Scores in Multiple ancestries

Diabetes Polygenic RIsk Scores in Multiple ancestries (D-PRISM) is led by Josep Mercader (contact PI) and Alisa Manning (MPI) from the Broad and Maggie Ng (MPI) from VUMC, along with 32 co-investigators across 5 countries. D-PRISM work will focus on improving polygenic risk score prediction of diabetes and progression across the lifespan in diverse ancestries using the PRS-CS method and its extensions. Their study includes more than 1.5M individuals with type 1 and type 2 diabetes, gestational diabetes and/or related complications from diverse ancestries. Results from this study will demonstrate how genomic data can inform more efficient and targeted preventive strategies within healthcare systems and across ethnically diverse populations.


EndoPhenotype InCorporated PRS

Yun Li (UNC, contact PI), Nancy Cox (VUMC, MPI), and Alex Reiner (FHCRC/UW, MPI) are leading a study site that will focus on creating PRS-adjusted laboratory values, which are used routinely for diagnosing and monitoring the progression of disease but manifest substantial differences across racial/ethnic groups. They anticipate these PRS-adjusted laboratory values will reduce structural health disparities and improve prediction of relevant clinical endpoints. They additionally leverage important aspects of population genetics, including local ancestry information and population-specific selection signatures, to construct better predictive PRS in admixed populations. 


Functional and Fine-Mapping Approach to Improve Responsible Risk-modeling of Polygenic Risk Scores

Pradeep Natarajan (Broad Institute) and colleagues lead a study site aimed at addressing key gaps in the current ability to deliver polygenic scores accurately to diverse populations. First, they propose to harmonize and aggregate individual-level data from >50,000 individuals of South Asian ancestry – prioritized on the basis of marked under-representation in studies to date – developing and sharing analytic workflows and new statistical genetics approaches. Second, using a ‘diversity-first’ approach, they plan to validate the FFAIR-PRS approach for enhanced polygenic score development, and build integrated and calibrated absolute risk estimators to facilitate ultimate clinical adoption via other ongoing research efforts.


Polygenic Risk Estimation and Validation to ENhance Treatment - Coronary Heart Disease

As part of the PRIMED consortium, the Mayo team (Iftikhar Kullo, contact PI and Daniel Schaid, multiple PI) will generate PRSs for populations of diverse ancestry using existing and new datasets with genotype and phenotype data for CHD and its risk factors. They will develop statistical methods to account for admixture and ‘local’ ancestry when estimating PRSs in admixed individuals and investigate whether weighting variants by function improves risk prediction. They will integrate PRSs with clinical information to compute absolute risk estimates for individuals from diverse ancestry groups. 


Leveraging Diversity in Cancer Epidemiology Cohorts and Novel Methods to Improve Polygenic Risk Scores

This project will construct and evaluate polygenic risk scores (PRS), and estimate absolute and excess relative risk for several cancers jointly for PRS and established risk factors in multiethnic populations from six large-scale, longstanding cohorts. This research will inform the development and implementation of risk models across populations to better guide public health screening and potentially inform clinicians and patients in treatment decisions regarding cancer.

This project is led by David Conti (USC, contact PI) and John Witte (Stanford, MPI). The Project Scientist is Leah Mechanic (NCI).