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A framework evaluating the utility of multi-gene, multi-disease population-based panel testing that accounts for uncertainty in penetrance estimates
View ORCID ProfileJane W. Liang, Kurt D. Christensen, Robert C. Green, View ORCID ProfilePeter Kraft
doi: https://doi.org/10.1101/2022.08.10.503415
Jane W. Liang
1Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
2Department of Data Science, Dana-Farber Cancer Institute, Boston, MA, USA
Kurt D. Christensen
3Center for Healthcare Research in Pediatrics, Department of Population Medicine, Harvard Pilgrim Health Care Institute, Boston, Massachusetts
4Department of Population Medicine, Harvard Medical School, Boston, Massachusetts
5Broad Institute of MIT and Harvard, Cambridge, Massachusetts
Robert C. Green
6Mass General Brigham, Boston, MA, USA
5Broad Institute of MIT and Harvard, Cambridge, Massachusetts
7Ariadne Labs, Boston, MA, USA
8Harvard Medical School, Boston, MA, USA
Peter Kraft
9Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
10Program in Genetic Epidemiology and Statistical Genetics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
1Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
5Broad Institute of MIT and Harvard, Cambridge, Massachusetts
Posted August 13, 2022.
A framework evaluating the utility of multi-gene, multi-disease population-based panel testing that accounts for uncertainty in penetrance estimates
Jane W. Liang, Kurt D. Christensen, Robert C. Green, Peter Kraft
bioRxiv 2022.08.10.503415; doi: https://doi.org/10.1101/2022.08.10.503415
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