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Integration of polygenic risk scores with modifiable risk factors improves risk prediction: results from a pan-cancer analysis

View ORCID ProfileLinda Kachuri, Rebecca E. Graff, Karl Smith-Byrne, Travis J. Meyers, View ORCID ProfileSara R. Rashkin, Elad Ziv, John S. Witte, Mattias Johansson
doi: https://doi.org/10.1101/2020.01.28.922088
Linda Kachuri
Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, USA
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  • ORCID record for Linda Kachuri
Rebecca E. Graff
Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, USA
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Karl Smith-Byrne
Genetic Epidemiology Group, Section of Genetics, International Agency for Research on Cancer, Lyon, France
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Travis J. Meyers
Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, USA
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Sara R. Rashkin
Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, USA
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  • ORCID record for Sara R. Rashkin
Elad Ziv
Department of Medicine, University of California, San Francisco, San Francisco, USA
Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, USA
Institute for Human Genetics, University of California, San Francisco, San Francisco, USA
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John S. Witte
Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, USA
Institute for Human Genetics, University of California, San Francisco, San Francisco, USA
Department of Urology, University of California, San Francisco, San Francisco, USA
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  • For correspondence: jwitte@ucsf.edu JohanssonM@iarc.fr
Mattias Johansson
Genetic Epidemiology Group, Section of Genetics, International Agency for Research on Cancer, Lyon, France
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  • For correspondence: jwitte@ucsf.edu JohanssonM@iarc.fr
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ABSTRACT

Cancer risk is determined by a complex interplay of genetic and modifiable risk factors. Combining individual germline risk variants into polygenic risk scores (PRS) creates a personalized genetic susceptibility profile that can be leveraged for disease prediction. Using data from the UK Biobank cohort (413,753 individuals; 22,755 incident cases), we systematically quantify the added predictive value of augmenting conventional cancer risk factors with PRS for 16 cancer types. Our results indicate that incorporating PRS in addition to family history of cancer and modifiable risk factors improves prediction accuracy, but the magnitude of incremental improvement varies substantially between cancers. We also demonstrate the utility of PRS for risk stratification. Individuals with high genetic risk (PRS≥80th percentile) have significantly divergent 5-year absolute risk trajectories across strata based on family history and modifiable risk factors. Finally, we estimate that high genetic risk accounts for 4.0% to 30.3% of new cancer cases, which exceeds the impact of many lifestyle-related risk factors. In summary, we provide novel quantitative data illustrating the importance of integrating PRS into personalized cancer risk assessment.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC 4.0 International license.
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Posted January 29, 2020.
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Integration of polygenic risk scores with modifiable risk factors improves risk prediction: results from a pan-cancer analysis
Linda Kachuri, Rebecca E. Graff, Karl Smith-Byrne, Travis J. Meyers, Sara R. Rashkin, Elad Ziv, John S. Witte, Mattias Johansson
bioRxiv 2020.01.28.922088; doi: https://doi.org/10.1101/2020.01.28.922088
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Integration of polygenic risk scores with modifiable risk factors improves risk prediction: results from a pan-cancer analysis
Linda Kachuri, Rebecca E. Graff, Karl Smith-Byrne, Travis J. Meyers, Sara R. Rashkin, Elad Ziv, John S. Witte, Mattias Johansson
bioRxiv 2020.01.28.922088; doi: https://doi.org/10.1101/2020.01.28.922088

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