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NIH grant 3U01CA261339-05S1: Leveraging Prospective Cancer Epidemiology Cohorts and Novel Methods to Improve Polygenic Risk Scores
Institute: {'code': 'CA', 'abbreviation': 'NCI', 'name': 'National Cancer Institute', 'admin_org_id': '12500008', 'admin_funding_url': ''}. PI: Fei Chen. Award: $300000.
Start: 2026-08-28T00:00:00.
Abstract
There are stark differences in the burden of certain cancers across populations. For example, in
comparison to individuals of European ancestry, African American men have a ~67% higher incidence rate of
prostate cancer and Asian/Pacific Islander men and women have a 70% and 95% higher incidence rate of liver
cancer, respectively. These differences in the burden of cancer across groups have been attributed to an
interplay of genetic, environmental, and social factors. The inadequate representation of individuals from all
ancestries limits the translational potential of GWAS findings to the world’s populations. Applying PRS developed
in European ancestry individuals to other populations may result in biased risk prediction, and further exacerbate
disease differences due to inaccurate assessment of individuals at high risk of disease. Here, we propose to
address the drastic need for appropriate PRS construction and evaluation across multiple populations by
applying new PRS approaches to the following six large-scale, longstanding cohorts: the Multiethnic Cohort
(MEC); the Kaiser Resource for Genetic Epidemiology Research on Aging (GERA) cohort; the Women’s Health
Initiative (WHI); the Harvard Nurses Health Studies (NHS); the Harvard Health Professionals Follow-Up Study
(HPFS); and the Prostate, Lung, Colorectal and Ovarian Cancer Screening Trial (PLCO). Together, these
cohorts include over 300,000 individuals (100,000 non-Europeans) and 91,000 incident cancer cases (24,000
non-Europeans). The individuals in these cohorts are from different ancestries. While focusing on cancer
outcomes, we will utilize these unique and extensive resources to develop methods to construct and evaluate
PRS, and importantly for translation, estimate absolute and excess relative risk of cancer jointly for PRS and
established risk factors in multiple populations. To facilitate access to developed pipelines and data resources,
we will follow F.A.I.R. analytic principles while participating with the Coordinating Center and other study sites.
Ultimately, constructing and evaluating risk models in multiple populations is essential to broaden the impact of
genomic medicine on human health.document fetched 2026-09-06 02:47