Original Investigation

Human Genetics

, Volume 128, Issue 1, pp 89-101

First online:

Open Access This content is freely available online to anyone, anywhere at any time.

The utility and predictive value of combinations of low penetrance genes for screening and risk prediction of colorectal cancer

  • Steven J. HawkenAffiliated withDepartment of Epidemiology and Community Medicine, University of OttawaOttawa Hospital Research Institute
  • , Celia M. T. GreenwoodAffiliated withDalla Lana School of Public Health, University of Toronto
  • , Thomas J. HudsonAffiliated withOntario Institute for Cancer ResearchDepartment of Medical Biophysics, University of TorontoDepartment of Molecular Genetics, University of Toronto
  • , Rafal KustraAffiliated withDalla Lana School of Public Health, University of Toronto
  • , John McLaughlinAffiliated withDalla Lana School of Public Health, University of TorontoSamuel Lunenfeld Research Institute, Mount Sinai HospitalCancer Care Ontario
  • , Quanhe YangAffiliated withCenters for Disease Control and Prevention, National Office of Public Health Genomics
  • , Brent W. ZankeAffiliated withOntario Institute for Cancer ResearchOttawa Hospital Research Institute
  • , Julian LittleAffiliated withDepartment of Epidemiology and Community Medicine, University of Ottawa Email author 


Despite the fact that colorectal cancer (CRC) is a highly treatable form of cancer if detected early, a very low proportion of the eligible population undergoes screening for this form of cancer. Integrating a genomic screening profile as a component of existing screening programs for CRC could potentially improve the effectiveness of population screening by allowing the assignment of individuals to different types and intensities of screening and also by potentially increasing the uptake of existing screening programs. We evaluated the utility and predictive value of genomic profiling as applied to CRC, and as a potential component of a population-based cancer screening program. We generated simulated data representing a typical North American population including a variety of genetic profiles, with a range of relative risks and prevalences for individual risk genes. We then used these data to estimate parameters characterizing the predictive value of a logistic regression model built on genetic markers for CRC. Meta-analyses of genetic associations with CRC were used in building science to inform the simulation work, and to select genetic variants to include in logistic regression model-building using data from the ARCTIC study in Ontario, which included 1,200 CRC cases and a similar number of cancer-free population-based controls. Our simulations demonstrate that for reasonable assumptions involving modest relative risks for individual genetic variants, that substantial predictive power can be achieved when risk variants are common (e.g., prevalence > 20%) and data for enough risk variants are available (e.g., ~140–160). Pilot work in population data shows modest, but statistically significant predictive utility for a small collection of risk variants, smaller in effect than age and gender alone in predicting an individual’s CRC risk. Further genotyping and many more samples will be required, and indeed the discovery of many more risk loci associated with CRC before the question of the potential utility of germline genomic profiling can be definitively answered.