Aarhus University Seal / Aarhus Universitets segl

Peter Sørensen

Gene prioritization for livestock diseases by data integration

Publikation: Bidrag til tidsskrift/Konferencebidrag i tidsskrift /Bidrag til avisTidsskriftartikelForskningpeer review

Standard

Gene prioritization for livestock diseases by data integration. / Jiang, Li; Sørensen, Peter; Thomsen, Bo; Høj-Edwards, Stefan McKinnon; Skarman, Axel; Røntved, Christine M; Lund, Mogens S; Workman, Christopher.

I: Physiological Genomics, Bind 44, Nr. 5, 03.2012, s. 305-317.

Publikation: Bidrag til tidsskrift/Konferencebidrag i tidsskrift /Bidrag til avisTidsskriftartikelForskningpeer review

Harvard

Jiang, L, Sørensen, P, Thomsen, B, Høj-Edwards, SM, Skarman, A, Røntved, CM, Lund, MS & Workman, C 2012, 'Gene prioritization for livestock diseases by data integration', Physiological Genomics, bind 44, nr. 5, s. 305-317. https://doi.org/10.1152/physiolgenomics.00047.2011

APA

Jiang, L., Sørensen, P., Thomsen, B., Høj-Edwards, S. M., Skarman, A., Røntved, C. M., Lund, M. S., & Workman, C. (2012). Gene prioritization for livestock diseases by data integration. Physiological Genomics, 44(5), 305-317. https://doi.org/10.1152/physiolgenomics.00047.2011

CBE

Jiang L, Sørensen P, Thomsen B, Høj-Edwards SM, Skarman A, Røntved CM, Lund MS, Workman C. 2012. Gene prioritization for livestock diseases by data integration. Physiological Genomics. 44(5):305-317. https://doi.org/10.1152/physiolgenomics.00047.2011

MLA

Vancouver

Jiang L, Sørensen P, Thomsen B, Høj-Edwards SM, Skarman A, Røntved CM o.a. Gene prioritization for livestock diseases by data integration. Physiological Genomics. 2012 mar;44(5):305-317. https://doi.org/10.1152/physiolgenomics.00047.2011

Author

Jiang, Li ; Sørensen, Peter ; Thomsen, Bo ; Høj-Edwards, Stefan McKinnon ; Skarman, Axel ; Røntved, Christine M ; Lund, Mogens S ; Workman, Christopher. / Gene prioritization for livestock diseases by data integration. I: Physiological Genomics. 2012 ; Bind 44, Nr. 5. s. 305-317.

Bibtex

@article{d475e1b5fba74590a38da9428f39b490,
title = "Gene prioritization for livestock diseases by data integration",
abstract = "Identifying causal genes that underlie complex traits such as susceptibility to disease is a primary aim of genetic and biomedical studies. Genetic mapping of quantitative trait loci (QTL) and gene expression profiling based on high-throughput technologies are common first approaches towards identifying associations between genes and traits; however, it is often difficult to assess whether the biological function of a putative candidate gene is consistent with a particular phenotype. Here, we have implemented a network-based disease gene prioritization approach for ranking of genes associated with quantitative traits and diseases in livestock species. The approach uses ortholog mapping and integrates information of disease or trait phenotypes, gene-associated phenotypes and protein-protein interactions. It was used for ranking all known genes present in cattle genome for their potential roles in bovine mastitis. Gene-associated phenome profile and transcriptome profile in response to E. coli infection in the mammary gland were integrated to make a global inference of bovine genes involved in mastitis. The top ranked genes were highly enriched for pathways and biological processes underlying inflammation and immune responses, which supports the validity of our approach for identifying genes that are relevant to animal health and disease. These gene-associated phenotypes were used for a local prioritization of candidate genes located in a QTL affecting the susceptibility to mastitis. Our study provides a general framework for prioritizing genes associated with various complex traits in different species. To our knowledge this is the first time that gene expression, ortholog mapping, protein interactions and biomedical text data were integrated systematically for ranking candidate genes in any livestock species.",
keywords = "protein complexes, phenotype similarity, gene expression",
author = "Li Jiang and Peter S{\o}rensen and Bo Thomsen and H{\o}j-Edwards, {Stefan McKinnon} and Axel Skarman and R{\o}ntved, {Christine M} and Lund, {Mogens S} and Christopher Workman",
year = "2012",
month = mar,
doi = "10.1152/physiolgenomics.00047.2011",
language = "English",
volume = "44",
pages = "305--317",
journal = "Physiological Genomics",
issn = "1094-8341",
publisher = "American Physiological Society",
number = "5",

}

RIS

TY - JOUR

T1 - Gene prioritization for livestock diseases by data integration

AU - Jiang, Li

AU - Sørensen, Peter

AU - Thomsen, Bo

AU - Høj-Edwards, Stefan McKinnon

AU - Skarman, Axel

AU - Røntved, Christine M

AU - Lund, Mogens S

AU - Workman, Christopher

PY - 2012/3

Y1 - 2012/3

N2 - Identifying causal genes that underlie complex traits such as susceptibility to disease is a primary aim of genetic and biomedical studies. Genetic mapping of quantitative trait loci (QTL) and gene expression profiling based on high-throughput technologies are common first approaches towards identifying associations between genes and traits; however, it is often difficult to assess whether the biological function of a putative candidate gene is consistent with a particular phenotype. Here, we have implemented a network-based disease gene prioritization approach for ranking of genes associated with quantitative traits and diseases in livestock species. The approach uses ortholog mapping and integrates information of disease or trait phenotypes, gene-associated phenotypes and protein-protein interactions. It was used for ranking all known genes present in cattle genome for their potential roles in bovine mastitis. Gene-associated phenome profile and transcriptome profile in response to E. coli infection in the mammary gland were integrated to make a global inference of bovine genes involved in mastitis. The top ranked genes were highly enriched for pathways and biological processes underlying inflammation and immune responses, which supports the validity of our approach for identifying genes that are relevant to animal health and disease. These gene-associated phenotypes were used for a local prioritization of candidate genes located in a QTL affecting the susceptibility to mastitis. Our study provides a general framework for prioritizing genes associated with various complex traits in different species. To our knowledge this is the first time that gene expression, ortholog mapping, protein interactions and biomedical text data were integrated systematically for ranking candidate genes in any livestock species.

AB - Identifying causal genes that underlie complex traits such as susceptibility to disease is a primary aim of genetic and biomedical studies. Genetic mapping of quantitative trait loci (QTL) and gene expression profiling based on high-throughput technologies are common first approaches towards identifying associations between genes and traits; however, it is often difficult to assess whether the biological function of a putative candidate gene is consistent with a particular phenotype. Here, we have implemented a network-based disease gene prioritization approach for ranking of genes associated with quantitative traits and diseases in livestock species. The approach uses ortholog mapping and integrates information of disease or trait phenotypes, gene-associated phenotypes and protein-protein interactions. It was used for ranking all known genes present in cattle genome for their potential roles in bovine mastitis. Gene-associated phenome profile and transcriptome profile in response to E. coli infection in the mammary gland were integrated to make a global inference of bovine genes involved in mastitis. The top ranked genes were highly enriched for pathways and biological processes underlying inflammation and immune responses, which supports the validity of our approach for identifying genes that are relevant to animal health and disease. These gene-associated phenotypes were used for a local prioritization of candidate genes located in a QTL affecting the susceptibility to mastitis. Our study provides a general framework for prioritizing genes associated with various complex traits in different species. To our knowledge this is the first time that gene expression, ortholog mapping, protein interactions and biomedical text data were integrated systematically for ranking candidate genes in any livestock species.

KW - protein complexes

KW - phenotype similarity

KW - gene expression

U2 - 10.1152/physiolgenomics.00047.2011

DO - 10.1152/physiolgenomics.00047.2011

M3 - Journal article

C2 - 22234994

VL - 44

SP - 305

EP - 317

JO - Physiological Genomics

JF - Physiological Genomics

SN - 1094-8341

IS - 5

ER -