RT Journal Article
SR Electronic
T1 A framework evaluating the utility of multi-gene, multi-disease population-based panel testing that accounts for uncertainty in penetrance estimates
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2022.08.10.503415
DO 10.1101/2022.08.10.503415
A1 Liang, Jane W.
A1 Christensen, Kurt D.
A1 Green, Robert C.
A1 Kraft, Peter
YR 2022
UL http://biorxiv.org/content/early/2022/08/13/2022.08.10.503415.abstract
AB Purpose Panel germline testing allows for the efficient detection of pathogenic variants for multiple conditions. However, because the benefits and harms of identifying these variants are not always well-understood, it may not be beneficial to make panels arbitrarily large.Methods We present a multi-gene, multi-disease aggregate utility formula that allows the user to consider the addition or removal of each gene based on its own merits. This formula takes as inputs variant frequency, penetrance estimates, and subjective disutilities for false positives (testing positive but not developing the disease) and false negatives (testing negative but developing the disease). We provide credible intervals for utility that reflect uncertainty in penetrance estimates.Results Rare, highly penetrant pathogenic variants tend to contribute positive net utilities for a wide variety of user-specified utility costs and even when accounting for uncertainty in parameter estimation. On the other hand, for pathogenic variants of moderate, uncertain penetrance, the clinical utility is more dependent on assumed disutilities.Conclusion The decision to include a gene on a panel depends on variant frequency, penetrance, and subjective utilities and should account for uncertainties around these factors. Our framework and accompanying webtool help quantify the utility of testing particular genes.Competing Interest StatementDr. Green has received compensation for advising the following companies: AIA, Allelica, Fabric, Genome Web, Genomic Life, Grail, OptumLabs, Verily, VinBigData; and is co-founder of Genome Medical and Nurture Genomics.