Prenatal diagnosis study using array comparative genomic hybridization for genotype-phenotype correlation in 772 fetuses.


Journal

Annals of diagnostic pathology
ISSN: 1532-8198
Titre abrégé: Ann Diagn Pathol
Pays: United States
ID NLM: 9800503

Informations de publication

Date de publication:
Dec 2022
Historique:
received: 26 10 2022
accepted: 26 10 2022
pubmed: 8 11 2022
medline: 30 11 2022
entrez: 7 11 2022
Statut: ppublish

Résumé

The aim was to evaluate the main indications for prenatal diagnosis, the prevalence of abnormal copy number variations (CNVs), correlate them with clinical findings, analyze the prevalence of VUS, report the rare variants found and additionally highlight the clinical importance of microarray-based comparative genomic hybridization (aCGH) in prenatal diagnosis. We retrospectively analyzed a cohort of 772 fetuses with indication for genetic study in two tertiary hospitals, in a 9-years-period, using aCGH. Our results demonstrated 8.3 % (6.4-10.5 %, 95 % CI) detection rate of pathogenic CNVs. Within this group, the main indication was structural malformations (57 %) mainly involving central nervous system, skeletal and cardiac systems. Pathogenic results in cases with multiple malformations were higher than in cases with isolated anatomical system malformations showing statistical significant differences (p < 0.001). The second indication where we found more pathogenic CNVs was increased nuchal translucency (5-6.4 mm). In fact, the rate of pathogenic CNVs did not show significant differences between structural and non-structural malformations (p > 0.001), highlighting the relevance of genetic study by aCGH also in cases with no structural malformations. A total of 217 fetuses with CNVs classified as VUS were identified, mainly involving chromosomes X, 1 and 16. Our findings demonstrate 4.9 % (4.2-5.6 %, 95 % CI) increased in the diagnostic yield using aCGH compared to the use of conventional karyotype alone, confirming that the aCGH can improve the accuracy of prenatal diagnosis. Our survey provides a full genotype-phenotype analysis that can be clinically useful for the classification of variants in the context of prenatal setting, helping to provide a better reproductive genetic counselling.

Identifiants

pubmed: 36343605
pii: S1092-9134(22)00161-7
doi: 10.1016/j.anndiagpath.2022.152059
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

152059

Informations de copyright

Copyright © 2022 Elsevier Inc. All rights reserved.

Déclaration de conflit d'intérêts

Declaration of competing interest The authors have no conflict of interests to declare. This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Auteurs

Beatriz C Costa (BC)

Department of Pathology, Genetics Service, Faculty of Medicine, University of Porto, Portugal.

Ana Grangeia (A)

Department of Pathology, Genetics Service, Faculty of Medicine, University of Porto, Portugal; Medical Genetics Service, Centro Hospitalar Universitário de São João, Porto, Portugal; I3S-Instituto de Investigação e Inovação em Saúde, University of Porto, Porto, Portugal.

Joana Galvão (J)

Department of Obstetrics and Gynecology, Centro Hospitalar Vila Nova Gaia/Espinho (CHVNG), Vila Nova de Gaia, Portugal.

Diane Vaz (D)

Department of Pathology, Genetics Service, Faculty of Medicine, University of Porto, Portugal.

Mónica Melo (M)

Department of Obstetrics and Gynecology, Centro Hospitalar Vila Nova Gaia/Espinho (CHVNG), Vila Nova de Gaia, Portugal.

Teresa Carraca (T)

Department of Obstetrics and Gynecology, Hospital São João, Porto, Portugal.

Carla Ramalho (C)

I3S-Instituto de Investigação e Inovação em Saúde, University of Porto, Porto, Portugal; Department of Obstetrics and Gynecology, Hospital São João, Porto, Portugal; Obstetrics and Gynecology Service, Faculty of Medicine, Porto, Portugal.

Sofia Dória (S)

Department of Pathology, Genetics Service, Faculty of Medicine, University of Porto, Portugal; I3S-Instituto de Investigação e Inovação em Saúde, University of Porto, Porto, Portugal. Electronic address: sdoria@med.up.pt.

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Classifications MeSH