  Multi-gene panel testing allows efficient detection of pathogenic variants in
cancer susceptibility genes including moderate-risk genes such as ATM and
PALB2. A growing number of studies examine the risk of breast cancer (BC)
conferred by pathogenic variants of such genes. A meta-analysis combining the
reported risk estimates can provide an overall age-specific risk of developing
BC, i.e., penetrance for a gene. However, estimates reported by case-control
studies often suffer from ascertainment bias. Currently there are no methods
available to adjust for such ascertainment bias in this setting. We consider a
Bayesian random-effects meta-analysis method that can synthesize different
types of risk measures and extend it to incorporate studies with ascertainment
bias. This is achieved by introducing a bias term in the model and assigning
appropriate priors. We validate the method through a simulation study and apply
it to estimate BC penetrance for carriers of pathogenic variants of ATM and
PALB2 genes. Our simulations show that the proposed method results in more
accurate and precise penetrance estimates compared to when no adjustment is
made for ascertainment bias or when such biased studies are discarded from the
analysis. The estimated overall BC risk for individuals with pathogenic
variants in (1) ATM is 5.77% (3.22%-9.67%) by age 50 and 26.13% (20.31%-32.94%)
by age 80; (2) PALB2 is 12.99% (6.48%-22.23%) by age 50 and 44.69%
(34.40%-55.80%) by age 80. The proposed method allows for meta-analyses to
include studies with ascertainment bias resulting in a larger number of studies
included and thereby more robust estimates.
