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)

Access to Document

10.1371/journal.pone.0016542

Cite this

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

@article{3fe786a96148488a96978afa52c27465,

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",

author = "Karina Banasik and Justesen, {Johanne M} and Malene Hornbak and Krarup, {Nikolaj T} and Gjesing, {Anette P} and Sandholt, {Camilla Helene} and {S{\o}ndergaard Jensen}, Thomas and Niels Grarup and {\AA}sa Andersson and Torben J{\o}rgensen and Witte, {Daniel R} and Annelli Sandb{\ae}k and Torsten Lauritzen and Bernard Thorens and S{\o}ren Brunak and S{\o}rensen, {Thorkild I A} and Oluf Pedersen and Torben Hansen",

year = "2011",

month = jan,

day = "1",

doi = "10.1371/journal.pone.0016542",

language = "English",

volume = "6",

pages = "e16542",

journal = "P L o S One",

issn = "1932-6203",

publisher = "Public Library of Science",

number = "1",

}

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 - P L o S One

JF - P L o S One

IS - 1

ER -

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

Abstract

Keywords

UN SDGs

Access to Document

Fingerprint

Cite this