TY - JOUR
T1 - Bioinformatics-driven identification and examination of candidate genes for non-alcoholic fatty liver disease
AU - Banasik, Karina
AU - Justesen, Johanne M
AU - Hornbak, Malene
AU - Krarup, Nikolaj T
AU - Gjesing, Anette P
AU - Sandholt, Camilla Helene
AU - Søndergaard Jensen, Thomas
AU - Grarup, Niels
AU - Andersson, Åsa
AU - Jørgensen, Torben
AU - Witte, Daniel R
AU - Sandbæk, Annelli
AU - Lauritzen, Torsten
AU - Thorens, Bernard
AU - Brunak, Søren
AU - Sørensen, Thorkild I A
AU - Pedersen, Oluf
AU - Hansen, Torben
PY - 2011/1/1
Y1 - 2011/1/1
N2 - 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. Twentyone 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.
AB - 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. Twentyone 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.
KW - Case-Control Studies
KW - Computational Biology
KW - Data Mining
KW - Denmark
KW - Diabetes Mellitus, Type 2
KW - Fatty Liver
KW - Humans
KW - Metabolic Syndrome X
KW - Middle Aged
KW - Obesity
KW - Phenotype
KW - Polymorphism, Single Nucleotide
KW - Protein Binding
KW - Quantitative Trait Loci
U2 - 10.1371/journal.pone.0016542
DO - 10.1371/journal.pone.0016542
M3 - Journal article
C2 - 21339799
SN - 1932-6203
VL - 6
SP - e16542
JO - PLoS Computational Biology
JF - PLoS Computational Biology
IS - 1
ER -
