Systematic Review of Polygenic Risk Scores for Type 1 and Type 2 Diabetes.
diagnosis
genetics
polygenic risk score
type 1 diabetes
type 2 diabetes
Journal
International journal of molecular sciences
ISSN: 1422-0067
Titre abrégé: Int J Mol Sci
Pays: Switzerland
ID NLM: 101092791
Informations de publication
Date de publication:
02 Mar 2020
02 Mar 2020
Historique:
received:
03
02
2020
revised:
28
02
2020
accepted:
28
02
2020
entrez:
6
3
2020
pubmed:
7
3
2020
medline:
18
11
2020
Statut:
epublish
Résumé
Recent studies have led to considerable advances in the identification of genetic variants associated with type 1 and type 2 diabetes. An approach for converting genetic data into a predictive measure of disease susceptibility is to add the risk effects of loci into a polygenic risk score. In order to summarize the recent findings, we conducted a systematic review of studies comparing the accuracy of polygenic risk scores developed during the last two decades. We selected 15 risk scores from three databases (Scopus, Web of Science and PubMed) enrolled in this systematic review. We identified three polygenic risk scores that discriminate between type 1 diabetes patients and healthy people, one that discriminate between type 1 and type 2 diabetes, two that discriminate between type 1 and monogenic diabetes and nine polygenic risk scores that discriminate between type 2 diabetes patients and healthy people. Prediction accuracy of polygenic risk scores was assessed by comparing the area under the curve. The actual benefits, potential obstacles and possible solutions for the implementation of polygenic risk scores in clinical practice were also discussed. Develop strategies to establish the clinical validity of polygenic risk scores by creating a framework for the interpretation of findings and their translation into actual evidence, are the way to demonstrate their utility in medical practice.
Identifiants
pubmed: 32131491
pii: ijms21051703
doi: 10.3390/ijms21051703
pmc: PMC7084489
pii:
doi:
Types de publication
Journal Article
Systematic Review
Langues
eng
Sous-ensembles de citation
IM
Subventions
Organisme : Horizon 2020
ID : 754432
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