||2R01CA228198-05A1 Interpret this number
||University Of Chicago
||Polygenic Risk Prediction of Breast Cancer for Women of African Descent
African Americans have the highest breast cancer mortality rate, highest incidence rate of early-onset
breast cancer, and highest incidence rate of triple-negative breast cancer in the U.S. More than 220
susceptibility loci for breast cancer have been identified by genome-wide association studies (GWAS), mainly
in population of European ancestry. Polygenic risk scores (PRS), which aggregate common genetic variants
identified by GWAS, have been developed to predict genetic risk of breast cancer for European ancestry
women and used in clinical practice, but the PRS for African American women has suboptimal accuracy.
Therefore, we propose a comprehensive analytical study that leverages several types of existing genetic
datasets for breast cancer available to us and in public domains to address three specific aims. First, we aim
to conduct cross-ancestry fine-mapping analysis to identify a credible set of causal variants in 237 breast
cancer susceptibility loci. Then we will develop parsimonious and robust PRS from the credible set of variants.
We have compiled and harmonized genetic data from breast cancer GWASs in women of African ancestry,
including 18,034 cases and 22,104 controls, and leverage the association results from European ancestry
(>133,000 cases and >291,000 controls) and Asian (>22,000 cases and >22,000 controls) populations.
Second, we aim to develop precise PRS using genome-wide data with several novel cross-ancestry statistical
methods. We will develop PRS models for overall breast cancer and its subtypes (estrogen receptor positive
and negative, and triple-negative breast cancer). Then we will validate the models generated in aims 1-2 in
independent datasets (>14,000 cases and >82,000 controls) from case-control studies, ongoing cohort studies,
and an ongoing risk-adaptive breast cancer screening trial. Third, we will integrate the best performed PRS
with existing risk prediction models that are based on non-genetic risk factors. The resulting absolute risk
models have a good potential to translate knowledge from GWAS to inform the practice of genetic counseling,
breast cancer screening and prevention for African Americans. It has good potential to advance racial equity in