||5R01CA228198-02 Interpret this number
||University Of Chicago
||Genetic Risk Prediction of Breast Cancer for African Americans
More than 180 susceptibility loci for breast cancer have been identified by genome-wide association
studies (GWAS), mainly in Caucasian populations. However, many of these risk variants cannot be directly
replicated in women of African ancestry, suggesting that causal variants are yet to be identified. Polygenic risk
scores (PRS), which aggregate common genetic variants identified by GWAS, have been developed to predict
genetic risk of breast cancer for Caucasian women, but there is no validated PRS for African American women.
The linkage equilibrium in African ancestry populations is much less extensive than in Caucasian and Asian
populations, which makes African ancestry population the ideal population to find causative variants after
localizing a breast cancer susceptibility locus. 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-ethnic fine-mapping analysis for narrowing
down casual variant candidate lists in 180+ loci of breast cancer. We will compile and harmonize genetic
data from studies of breast cancer in women of African ancestry, including 7,525 cases and 6,207 controls,
and leverage the association results from Caucasians (>122,000 cases and >105,000 controls), East Asians
(>14,000 cases and >13,000 controls), and Latinos (4,400 cases and 7,500 controls). We will use a Bayesian
statistical method to directly incorporate multiple functional annotations for the top variants in each locus.
Second, we aim to develop breast cancer polygenic risk score models in African Americans by
leveraging functional annotations, linkage disequilibrium, and gene expression data. Several PRSs will
be developed for overall breast cancer risk and by estrogen receptor, cross-validated internally, and validated
with external studies. Third, we aim to develop breast cancer risk prediction model by combining both
genetic and non-genetic factors. The proposed study will efficiently utilize several types of existing data
using innovative integrative approaches and has the potential to advance the field by narrowing down the
genetic regions containing causal variants. More importantly, the risk prediction model has a good potential to
translate knowledge from GWAS to the practice of breast cancer screening.
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