Computational disease gene identification: a concert of methods prioritizes type 2 diabetes and obesity candidate genes

Nicki Tiffin; Euan Adie; Frances Turner; Han G Brunner; Marc A van Driel; Martin Oti; Nuria Lopez-Bigas; Christos Ouzounis; Carolina Perez-Iratxeta; Miguel A Andrade-Navarro; Adebowale Adeyemo; Mary Elizabeth Patti; Colin A M Semple; Winston Hide

doi:10.1093/nar/gkl381

Computational disease gene identification: a concert of methods prioritizes type 2 diabetes and obesity candidate genes

Nicki Tiffin, Euan Adie, Frances Turner, Han G Brunner, Marc A van Driel, Martin Oti, Nuria Lopez-Bigas, Christos Ouzounis, Carolina Perez-Iratxeta, Miguel A Andrade-Navarro, Adebowale Adeyemo, Mary Elizabeth Patti, Colin A M Semple, Winston Hide

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Abstract / Description of output

Genome-wide experimental methods to identify disease genes, such as linkage analysis and association studies, generate increasingly large candidate gene sets for which comprehensive empirical analysis is impractical. Computational methods employ data from a variety of sources to identify the most likely candidate disease genes from these gene sets. Here, we review seven independent computational disease gene prioritization methods, and then apply them in concert to the analysis of 9556 positional candidate genes for type 2 diabetes (T2D) and the related trait obesity. We generate and analyse a list of nine primary candidate genes for T2D genes and five for obesity. Two genes, LPL and BCKDHA, are common to these two sets. We also present a set of secondary candidates for T2D (94 genes) and for obesity (116 genes) with 58 genes in common to both diseases.

Original language	English
Pages (from-to)	3067-81
Number of pages	15
Journal	Nucleic Acids Research
Volume	34
Issue number	10
DOIs	https://doi.org/10.1093/nar/gkl381
Publication status	Published - 2006

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10.1093/nar/gkl381

Computational disease gene identification: a concert of methods prioritizes type 2 diabetes and obesity candidate genes.
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/ by-nc/2.0/uk/) which permits unrestricted non-commerical use, distribution, and reproduction in any medium, provided the original work is properly cited.
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http://nar.oxfordjournals.org/content/34/10/3067

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Tiffin, N., Adie, E., Turner, F., Brunner, H. G., van Driel, M. A., Oti, M., Lopez-Bigas, N., Ouzounis, C., Perez-Iratxeta, C., Andrade-Navarro, M. A., Adeyemo, A., Patti, M. E., Semple, C. A. M., & Hide, W. (2006). Computational disease gene identification: a concert of methods prioritizes type 2 diabetes and obesity candidate genes. Nucleic Acids Research, 34(10), 3067-81. https://doi.org/10.1093/nar/gkl381

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title = "Computational disease gene identification: a concert of methods prioritizes type 2 diabetes and obesity candidate genes",

abstract = "Genome-wide experimental methods to identify disease genes, such as linkage analysis and association studies, generate increasingly large candidate gene sets for which comprehensive empirical analysis is impractical. Computational methods employ data from a variety of sources to identify the most likely candidate disease genes from these gene sets. Here, we review seven independent computational disease gene prioritization methods, and then apply them in concert to the analysis of 9556 positional candidate genes for type 2 diabetes (T2D) and the related trait obesity. We generate and analyse a list of nine primary candidate genes for T2D genes and five for obesity. Two genes, LPL and BCKDHA, are common to these two sets. We also present a set of secondary candidates for T2D (94 genes) and for obesity (116 genes) with 58 genes in common to both diseases.",

author = "Nicki Tiffin and Euan Adie and Frances Turner and Brunner, {Han G} and {van Driel}, {Marc A} and Martin Oti and Nuria Lopez-Bigas and Christos Ouzounis and Carolina Perez-Iratxeta and Andrade-Navarro, {Miguel A} and Adebowale Adeyemo and Patti, {Mary Elizabeth} and Semple, {Colin A M} and Winston Hide",

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doi = "10.1093/nar/gkl381",

language = "English",

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Tiffin, N, Adie, E, Turner, F, Brunner, HG, van Driel, MA, Oti, M, Lopez-Bigas, N, Ouzounis, C, Perez-Iratxeta, C, Andrade-Navarro, MA, Adeyemo, A, Patti, ME, Semple, CAM & Hide, W 2006, 'Computational disease gene identification: a concert of methods prioritizes type 2 diabetes and obesity candidate genes', Nucleic Acids Research, vol. 34, no. 10, pp. 3067-81. https://doi.org/10.1093/nar/gkl381

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T1 - Computational disease gene identification

T2 - a concert of methods prioritizes type 2 diabetes and obesity candidate genes

AU - Tiffin, Nicki

AU - Adie, Euan

AU - Turner, Frances

AU - Brunner, Han G

AU - van Driel, Marc A

AU - Oti, Martin

AU - Lopez-Bigas, Nuria

AU - Ouzounis, Christos

AU - Perez-Iratxeta, Carolina

AU - Andrade-Navarro, Miguel A

AU - Adeyemo, Adebowale

AU - Patti, Mary Elizabeth

AU - Semple, Colin A M

AU - Hide, Winston

PY - 2006

Y1 - 2006

N2 - Genome-wide experimental methods to identify disease genes, such as linkage analysis and association studies, generate increasingly large candidate gene sets for which comprehensive empirical analysis is impractical. Computational methods employ data from a variety of sources to identify the most likely candidate disease genes from these gene sets. Here, we review seven independent computational disease gene prioritization methods, and then apply them in concert to the analysis of 9556 positional candidate genes for type 2 diabetes (T2D) and the related trait obesity. We generate and analyse a list of nine primary candidate genes for T2D genes and five for obesity. Two genes, LPL and BCKDHA, are common to these two sets. We also present a set of secondary candidates for T2D (94 genes) and for obesity (116 genes) with 58 genes in common to both diseases.

AB - Genome-wide experimental methods to identify disease genes, such as linkage analysis and association studies, generate increasingly large candidate gene sets for which comprehensive empirical analysis is impractical. Computational methods employ data from a variety of sources to identify the most likely candidate disease genes from these gene sets. Here, we review seven independent computational disease gene prioritization methods, and then apply them in concert to the analysis of 9556 positional candidate genes for type 2 diabetes (T2D) and the related trait obesity. We generate and analyse a list of nine primary candidate genes for T2D genes and five for obesity. Two genes, LPL and BCKDHA, are common to these two sets. We also present a set of secondary candidates for T2D (94 genes) and for obesity (116 genes) with 58 genes in common to both diseases.

U2 - 10.1093/nar/gkl381

DO - 10.1093/nar/gkl381

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SN - 0305-1048

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SP - 3067

EP - 3081

JO - Nucleic Acids Research

JF - Nucleic Acids Research

IS - 10

ER -

Computational disease gene identification: a concert of methods prioritizes type 2 diabetes and obesity candidate genes

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