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Abstract:
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Using principles of Mendelian genetics, probability theory, and mutation-specific knowledge, Mendelian models identify those at high risk for carrying a heritable cancer-susceptibility mutation and assess future risk of cancer, based on family history. These quantitative risk measures can be used for research and to tailor personalized prevention programs. Our proposed PanelPRO is a generalizable, computationally efficient Mendelian risk prediction framework that incorporates an arbitrary number of gene-cancer associations. However, there are pragmatic challenges in the implementation of such a comprehensive model. There may be uncertainty in estimating the necessary population-level model parameters among rare genes and cancers. Obtaining accurate patient family history information for a large number of cancers may also be impractical. Motivated by the clinical context of pre-screening for tests of any cancer, we investigate simplifying assumptions that reduce the amount of patient information that needs to be collected and allow for more robust parameter estimation. The trade-offs for these aggregation approaches are evaluated through simulations.
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