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

Karina Banasik; Johanne M Justesen; Malene Hornbak; Nikolaj T Krarup; Anette P Gjesing; Camilla Helene Sandholt; Thomas Søndergaard Jensen; Niels Grarup; Åsa Andersson; Torben Jørgensen; Daniel R Witte; Annelli Sandbæk; Torsten Lauritzen; Bernard Thorens; Søren Brunak; Thorkild I A Sørensen; Oluf Pedersen; Torben Hansen

doi:10.1371/journal.pone.0016542

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

Karina Banasik, Johanne M Justesen, Malene Hornbak, Nikolaj T Krarup, Anette P Gjesing, Camilla Helene Sandholt, Thomas Søndergaard Jensen, Niels Grarup, Åsa Andersson, Torben Jørgensen, Daniel R Witte, Annelli Sandbæk, Torsten Lauritzen, Bernard Thorens, Søren Brunak, Thorkild I A Sørensen, Oluf Pedersen, Torben Hansen

17 Citations (Scopus)

Abstract

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.

Original language	English
Journal	P L o S One
Volume	6
Issue number	1
Pages (from-to)	e16542
ISSN	1932-6203
DOIs	https://doi.org/10.1371/journal.pone.0016542
Publication status	Published - 1 Jan 2011

Keywords

Case-Control Studies
Computational Biology
Data Mining
Denmark
Diabetes Mellitus, Type 2
Fatty Liver
Humans
Metabolic Syndrome X
Middle Aged
Obesity
Phenotype
Polymorphism, Single Nucleotide
Protein Binding
Quantitative Trait Loci

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1371/journal.pone.0016542

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Banasik, K., Justesen, J. M., Hornbak, M., Krarup, N. T., Gjesing, A. P., Sandholt, C. H., Søndergaard Jensen, T., Grarup, N., Andersson, Å., Jørgensen, T., Witte, D. R., Sandbæk, A., Lauritzen, T., Thorens, B., Brunak, S., Sørensen, T. I. A., Pedersen, O., & Hansen, T. (2011). Bioinformatics-driven identification and examination of candidate genes for non-alcoholic fatty liver disease. P L o S One, 6(1), e16542. https://doi.org/10.1371/journal.pone.0016542

Banasik, K , Justesen, JM, Hornbak, M, Krarup, NT, Gjesing, AP, Sandholt, CH, Søndergaard Jensen, T, Grarup, N , Andersson, Å, Jørgensen, T, Witte, DR, Sandbæk, A, Lauritzen, T, Thorens, B, Brunak, S , Sørensen, TIA , Pedersen, O & Hansen, T 2011, 'Bioinformatics-driven identification and examination of candidate genes for non-alcoholic fatty liver disease', P L o S One, vol. 6, no. 1, pp. e16542. https://doi.org/10.1371/journal.pone.0016542

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title = "Bioinformatics-driven identification and examination of candidate genes for non-alcoholic fatty liver disease",

abstract = "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.",

keywords = "Case-Control Studies, Computational Biology, Data Mining, Denmark, Diabetes Mellitus, Type 2, Fatty Liver, Humans, Metabolic Syndrome X, Middle Aged, Obesity, Phenotype, Polymorphism, Single Nucleotide, Protein Binding, Quantitative Trait Loci",

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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.

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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 -

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

Abstract

Keywords

UN SDGs

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