Bayesin analysis for cystic fibrosis risks in prenatal and carrier screening

Abstract

Purpose: Risk assessment is an essential component of genetic counseling and testing, and Bayesian analysis plays a central role in complex risk calculations. We previously developed generalizable Bayesian methods to calculate the autosomal recessive disease risk of a fetus when one or no mutation is detected, and another, independent risk factor is present. Our methods are particularly useful for calculating the CF disease risk for a fetus with echogenic bowel. In genetics practice, however, there are other scenarios for which our previous methods are inadequate. Methods and Results: We provide herein methods for calculating genetic risks in a variety of common clinical scenarios. These scenarios include the following: (1) different mutation panels that have been used for the parents and for a fetus; (2) genetic testing results available on the proband or other relatives, in addition to the consultand; (3) fetal ultrasound negative for echogenic bowel with a positive family history; and (4) a consultand with a mixed ethnic background. Conclusion: Our Bayesian methods have proven their versatility through application to many different common genetic counseling scenarios. These methods permit autosomal recessive disease and carrier probabilities to be calculated accurately, taking into account all relevant information. Our methods allow accurate genetic risk estimates for patients and their family members for CF or other autosomal recessive disorders.