Bioinformatics-driven identification and examination of candidate genes for non-alcoholic fatty liver disease.

Détails

Ressource 1Télécharger: BIB_A59C7FE62C49.P001.pdf (643.27 [Ko])
Etat: Public
Version: de l'auteur
ID Serval
serval:BIB_A59C7FE62C49
Type
Article: article d'un périodique ou d'un magazine.
Collection
Publications
Titre
Bioinformatics-driven identification and examination of candidate genes for non-alcoholic fatty liver disease.
Périodique
PLoS One
Auteur(s)
Banasik K., Justesen J.M., Hornbak M., Krarup N.T., Gjesing A.P., Sandholt C.H., Jensen T.S., Grarup N., Andersson A., Jørgensen T., Witte D.R., Sandbæk A., Lauritzen T., Thorens B., Brunak S., Sørensen T.I., Pedersen O., Hansen T.
ISSN
1932-6203 (Electronic)
ISSN-L
1932-6203
Statut éditorial
Publié
Date de publication
2011
Volume
6
Numéro
1
Pages
e16542
Langue
anglais
Résumé
ObjectiveCandidate genes for non-alcoholic fatty liver disease (NAFLD) identified by a bioinformatics approach were examined for variant associations to quantitative traits of NAFLD-related phenotypes.Research Design and MethodsBy integrating public database text mining, trans-organism protein-protein interaction transferal, and information on liver protein expression a protein-protein interaction network was constructed and from this a smaller isolated interactome was identified. Five genes from this interactome were selected for genetic analysis. Twenty-one tag single-nucleotide polymorphisms (SNPs) which captured all common variation in these genes were genotyped in 10,196 Danes, and analyzed for association with NAFLD-related quantitative traits, type 2 diabetes (T2D), central obesity, and WHO-defined metabolic syndrome (MetS).Results273 genes were included in the protein-protein interaction analysis and EHHADH, ECHS1, HADHA, HADHB, and ACADL were selected for further examination. A total of 10 nominal statistical significant associations (P<0.05) to quantitative metabolic traits were identified. Also, the case-control study showed associations between variation in the five genes and T2D, central obesity, and MetS, respectively. Bonferroni adjustments for multiple testing negated all associations.ConclusionsUsing a bioinformatics approach we identified five candidate genes for NAFLD. However, we failed to provide evidence of associations with major effects between SNPs in these five genes and NAFLD-related quantitative traits, T2D, central obesity, and MetS.
Mots-clé
Case-Control Studies, Computational Biology/methods, Data Mining, Type="Geographic">Denmark, Diabetes Mellitus, Type 2/genetics, Fatty Liver/genetics, Humans, Metabolic Syndrome X/genetics, Middle Aged, Obesity/genetics, Phenotype, Polymorphism, Single Nucleotide, Protein Binding, Quantitative Trait Loci
Pubmed
Web of science
Open Access
Oui
Création de la notice
22/03/2011 15:19
Dernière modification de la notice
20/08/2019 15:10
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