Exploring the effect of ascertainment bias on genetic studies that use clinical pedigrees

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.

Fig. 1

Number of families required for classification of variants with varying relative risks including colon cancer caused by a variant which affects function in MLH1. A horizontal dashed line is drawn at P = 0.05 and a solid line at P = 0.01 for convenience. Here, each set of families is simulated 1000 times to obtain a distribution for the boxplot. The underlying simulation used colon cancer risk from the SEER population as baseline and assumes that 30% of individuals are genotyped

Fig. 2

Effect of genotyping on determining the class of a variant for varying relative risks. Here, each set of 5 families is simulated 1000 times to obtain a distribution. The underlying simulation used colon cancer risk from the SEER population

Fig. 3

This figure shows boxplots of the probability of correct “pathogenic” classification in unbiased and biased pedigrees, ones which were selected to have more than two affecteds with only one accounted for, for varying relative risks. Here, probabilities were obtained from a sample of five families and repeated 1000 times to obtain distributions. The underlying simulation used colon cancer risk from the SEER population as baseline and assumes that 30% of individuals are genotyped

Fig. 4

Expected distributions of the number of affected individuals for various cancer types for 21 clinical families with testing ordered for breast or ovarian cancer risk a, and colorectal or endometrial cancer risk b. Also included are the number of observed cancer cases for each type represented by a vertical black line and the corresponding p-value. Note that these histograms of expected cancer cases in the group of families were made by sampling the distribution 100,000 times