Evaluation and classification of severity for 176 genes on an expanded carrier screening panel.
Adolescent
Algorithms
Child
Child, Preschool
Congenital Abnormalities
/ classification
Female
Genes, Developmental
/ genetics
Genetic Carrier Screening
/ methods
Genetic Counseling
/ methods
Genetic Diseases, Inborn
/ classification
Genetic Predisposition to Disease
Humans
Infant
Infant, Newborn
Male
Practice Guidelines as Topic
Pregnancy
Prenatal Diagnosis
/ methods
Severity of Illness Index
Young Adult
Journal
Prenatal diagnosis
ISSN: 1097-0223
Titre abrégé: Prenat Diagn
Pays: England
ID NLM: 8106540
Informations de publication
Date de publication:
09 2020
09 2020
Historique:
received:
03
04
2020
revised:
20
05
2020
accepted:
22
05
2020
pubmed:
1
6
2020
medline:
10
11
2021
entrez:
1
6
2020
Statut:
ppublish
Résumé
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). 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. 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. 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.
Sections du résumé
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.
Identifiants
pubmed: 32474937
doi: 10.1002/pd.5762
pmc: PMC7540025
doi:
Types de publication
Evaluation Study
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
1246-1257Informations de copyright
© 2020 The Authors. Prenatal Diagnosis published by John Wiley & Sons Ltd.
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