PLoS One. 2011 Jan 27;6(1):e16542.
Bioinformatics-driven identification and examination of candidate genes for non-alcoholic Fatty liver disease.
Banasik K, Justesen JM, Hornbak M, Krarup NT, Gjesing AP, Sandholt CH, Jensen TS, Grarup N, Andersson A, Jørgensen T, Witte DR, Sandbæk A, Lauritzen T, Thorens B, Brunak S, Sørensen TI, Pedersen O, Hansen T.
Hagedorn Research Institute, Gentofte, Denmark.
OBJECTIVE: Candidate 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 METHODS: By 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).
RESULTS: 273 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.
CONCLUSIONS: Using 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.
PMID: 21339799 [PubMed – in process]