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Updated: Jan 22, 2026

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
Published on: June 21, 2018
Exploring the effect of ascertainment bias on genetic studies that use clinical pedigrees
John Michael O Ranola1, Ginger J Tsai2, Brian H Shirts2
1Department of Laboratory Medicine, University of Washington, Seattle, WA, 98109, USA. ranolaj@uw.edu.
Abstract:
Recent studies have reported novel cancer risk associations with incidentally tested genes on cancer risk panels using clinically ascertained cohorts. Clinically ascertained pedigrees may have unknown ascertainment biases for both patients and relatives. We used a method to assess gene and variant risk and ascertainment bias based on comparing the number of observed disease instances in a pedigree given the sex and ages of individuals with those expected given established population incidence. We assessed the performance characteristics of the method by simulating families with varying genetic risk and proportion of individuals genotyped. We implemented this method using SEER cancer incidence data to assess clinical ascertainment bias in a set of 42 pedigrees with clinical testing ordered for either breast/ovarian cancer or colorectal/endometrial cancer at the University of Washington and negative sequencing results. In addition to expected biases consistent with the stated testing purpose, there were trends suggesting increased colorectal and endometrial cancer in pedigrees tested for breast cancer risk and trends suggesting increased breast cancer in families tested for colon cancer risk. There was no observed selection bias for prostate cancer in this set of families. This analysis illustrates that clinically ascertained data sets may have subtle biases. In the future, researchers seeking to explore risk associations with clinical data sets could assess potential ascertainment bias by comparing incidence of disease in families that test negative under given ordering criteria to expected population disease frequencies. Failure to assess for ascertainment bias increases the risk of false genetic associations.
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