Skip to Main Content (Press Enter)

Logo UNIPV
  • ×
  • Home
  • Corsi
  • Insegnamenti
  • Professioni
  • Persone
  • Pubblicazioni
  • Strutture

UNIFIND
Logo UNIPV

|

UNIFIND

unipv.it
  • ×
  • Home
  • Corsi
  • Insegnamenti
  • Professioni
  • Persone
  • Pubblicazioni
  • Strutture
  1. Pubblicazioni

NGS analysis in Marfan syndrome spectrum: Combination of rare and common genetic variants to improve genotype-phenotype correlation analysis

Articolo
Data di Pubblicazione:
2019
Abstract:
The diagnosis of Marfan spectrum includes a large number of clinical criteria. Although the identification of pathogenic variants contributes to the diagnostic process, its value to the prediction of clinical outcomes is still limited. An important novelty of the present study is represented by the statistical approach adopted to investigate genotype-phenotype correlation. The analysis has been improved considering the extended genetic information obtained by Next Generation Sequencing (NGS) and combining the effects of both rare and common genetic variants in an inclusive model. To this aim a cohort of 181 patients were analyzed with a NGS panel including 11 genes associated with Marfan spectrum. The genotype-phenotype correlation was also investigated considering the possibility to predict presence of a pathological mutation in Marfan syndrome (MFS) main genes based only on the analysis of phenotypic traits. Results obtained indicate that information about clinical traits can be summarized in a new variable that resulted significantly associated with the probability to find a pathological mutation in MFS main genes. This is important since the choice of the genetic test is often influenced by the phenotypic characterization of patients. Moreover, both rare and common variants were found to significantly contribute to clinical spectrum and their combination allowed to increase the percentage of phenotype variability that could be explained based on genetic factors. Results highlight the opportunity to take advantage of the overall genetic information obtained by NGS data to have a better clinical classification of patients.
Tipologia CRIS:
1.1 Articolo in rivista
Elenco autori:
Gentilini, D.; Oliveri, A.; Fazia, T.; Pini, A.; Marelli, S.; Bernardinelli, L.; Di Blasio, M.
Autori di Ateneo:
BERNARDINELLI LUISA
FAZIA TERESA
GENTILINI DAVIDE
Link alla scheda completa:
https://iris.unipv.it/handle/11571/1317626
Pubblicato in:
PLOS ONE
Journal
  • Dati Generali

Dati Generali

URL

https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0222506&type=printable
  • Utilizzo dei cookie

Realizzato con VIVO | Designed by Cineca | 25.11.5.0