Evaluation and classification of severity for 176 genes on an expanded carrier screening panel. (16th June 2020)

• Record Type: Journal Article
• Authors: Arjunan, Aishwarya; Bellerose, Holly; Torres, Raul; Ben‐Shachar, Rotem; Hoffman, Jodi D.; Angle, Brad; Slotnick, Robert Nathan; Simpson, Brittany N.; Lewis, Andrea M.; Magoulas, Pilar L.; Bontempo, Kelly; Schulze, Jeanine; Tarpinian, Jennifer; Bucher, Jessica A.; Dineen, Richard; Goetsch, Allison; Lazarin, Gabriel A.; Johansen Taber, Katherine
• Is Part Of: Prenatal diagnosis. Volume 40:Number 10(2020)
• Page Start: 1246
• Page End: 1257
• Publication Date: 2020-06-16
• Subjects: Prenatal diagnosis -- Periodicals; Fetus -- Diseases -- Diagnosis -- Periodicals; Electronic journals; 618.32075
• DOI: 10.1002/pd.5762
• Languages: English
• ISSNs: 0197-3851

Abstract

Abstract: Background: Disease severity is important when considering genes for inclusion on reproductive expanded carrier screening (ECS) panels. We applied a validated and previously published algorithm that classifies diseases into four severity categories (mild, moderate, severe, and profound) to 176 genes screened by ECS. Disease traits defining severity categories in the algorithm were then mapped to four severity‐related ECS panel design criteria cited by the American College of Obstetricians and Gynecologists (ACOG). Methods: Eight genetic counselors (GCs) and four medical geneticists (MDs) applied the severity algorithm to subsets of 176 genes. MDs and GCs then determined by group consensus how each of these disease traits mapped to ACOG severity criteria, enabling determination of the number of ACOG severity criteria met by each gene. Results: Upon consensus GC and MD application of the severity algorithm, 68 (39%) genes were classified as profound, 71 (40%) as severe, 36 (20%) as moderate, and one (1%) as mild. After mapping of disease traits to ACOG severity criteria, 170 out of 176 genes (96.6%) were found to meet at least one of the four criteria, 129 genes (73.3%) met at least two, 73 genes (41.5%) met at least three, and 17 genes (9.7%) met all four. Conclusion: This study classified the severity of a large set of Mendelian genes by collaborative clinical expert application of a trait‐based algorithm. Further, it operationalized difficult to interpret ACOG severity criteria via mapping of disease traits, thereby promoting consistency of ACOG criteria interpretation.