A prioritization analysis of disease association by data-mining of functional annotation of human genes

Takayuki Taniya; Susumu Tanaka; Yumi Yamaguchi-Kabata; Hideki Hanaoka; Chisato Yamasaki; Harutoshi Maekawa; Roberto A. Barrero; Boris Lenhard; Milton W. Datta; Mary Shimoyama; Roger Bumgarner; Ranajit Chakraborty; Ian Hopkinson; Libin Jia; Winston Hide; Charles Auffray; Shinsei Minoshima; Tadashi Imanishi; Takashi Gojobori

doi:10.1016/j.ygeno.2011.10.002

A prioritization analysis of disease association by data-mining of functional annotation of human genes

Takayuki Taniya, Susumu Tanaka, Yumi Yamaguchi-Kabata, Hideki Hanaoka, Chisato Yamasaki, Harutoshi Maekawa, Roberto A. Barrero, Boris Lenhard, Milton W. Datta, Mary Shimoyama, Roger Bumgarner, Ranajit Chakraborty, Ian Hopkinson, Libin Jia, Winston Hide, Charles Auffray, Shinsei Minoshima, Tadashi Imanishi, Takashi Gojobori

School of Biomedical Sciences

Research output: Contribution to journal › Article › peer-review

8 Scopus citations

Abstract

Complex diseases result from contributions of multiple genes that act in concert through pathways. Here we present a method to prioritize novel candidates of disease-susceptibility genes depending on the biological similarities to the known disease-related genes. The extent of disease-susceptibility of a gene is prioritized by analyzing seven features of human genes captured in H-InvDB. Taking rheumatoid arthritis (RA) and prostate cancer (PC) as two examples, we evaluated the efficiency of our method. Highly scored genes obtained included TNFSF12 and OSM as candidate disease genes for RA and PC, respectively. Subsequent characterization of these genes based upon an extensive literature survey reinforced the validity of these highly scored genes as possible disease-susceptibility genes. Our approach, Prioritization ANalysis of Disease Association (PANDA), is an efficient and cost-effective method to narrow down a large set of genes into smaller subsets that are most likely to be involved in the disease pathogenesis.

Original language	English
Pages (from-to)	1-9
Number of pages	9
Journal	Genomics
Volume	99
Issue number	1
DOIs	https://doi.org/10.1016/j.ygeno.2011.10.002
State	Published - Jan 2012

Keywords

Data-mining
Discriminant analysis
Disease
Gene function
Prostate cancer
Rheumatoid arthritis

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10.1016/j.ygeno.2011.10.002

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Taniya, T., Tanaka, S., Yamaguchi-Kabata, Y., Hanaoka, H., Yamasaki, C., Maekawa, H., Barrero, R. A., Lenhard, B., Datta, M. W., Shimoyama, M., Bumgarner, R., Chakraborty, R., Hopkinson, I., Jia, L., Hide, W., Auffray, C., Minoshima, S., Imanishi, T., & Gojobori, T. (2012). A prioritization analysis of disease association by data-mining of functional annotation of human genes. Genomics, 99(1), 1-9. https://doi.org/10.1016/j.ygeno.2011.10.002

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title = "A prioritization analysis of disease association by data-mining of functional annotation of human genes",

abstract = "Complex diseases result from contributions of multiple genes that act in concert through pathways. Here we present a method to prioritize novel candidates of disease-susceptibility genes depending on the biological similarities to the known disease-related genes. The extent of disease-susceptibility of a gene is prioritized by analyzing seven features of human genes captured in H-InvDB. Taking rheumatoid arthritis (RA) and prostate cancer (PC) as two examples, we evaluated the efficiency of our method. Highly scored genes obtained included TNFSF12 and OSM as candidate disease genes for RA and PC, respectively. Subsequent characterization of these genes based upon an extensive literature survey reinforced the validity of these highly scored genes as possible disease-susceptibility genes. Our approach, Prioritization ANalysis of Disease Association (PANDA), is an efficient and cost-effective method to narrow down a large set of genes into smaller subsets that are most likely to be involved in the disease pathogenesis.",

keywords = "Data-mining, Discriminant analysis, Disease, Gene function, Prostate cancer, Rheumatoid arthritis",

author = "Takayuki Taniya and Susumu Tanaka and Yumi Yamaguchi-Kabata and Hideki Hanaoka and Chisato Yamasaki and Harutoshi Maekawa and Barrero, {Roberto A.} and Boris Lenhard and Datta, {Milton W.} and Mary Shimoyama and Roger Bumgarner and Ranajit Chakraborty and Ian Hopkinson and Libin Jia and Winston Hide and Charles Auffray and Shinsei Minoshima and Tadashi Imanishi and Takashi Gojobori",

note = "Funding Information: We thank Drs. Peter Tonellato, Arek Kasprzyk, Teruyoshi Hishiki, Craig Gough, Makoto Shimada, and Naoki Nagata for their helpful discussion and comments on this study. We also thank all the members of the H-Invitational consortium and all the staff of JBIRC for construction of H-InvDB and the PANDA system. This work is financially supported by the Ministry of Economy, Trade and Industry of Japan (METI) , the Ministry of Education, Culture, Sports, Science and Technology of Japan (MEXT) , the Japan Biological Informatics Consortium (JBiC) , and National Institute of Advanced Industrial Science and Technology (AIST) .",

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Taniya, T, Tanaka, S, Yamaguchi-Kabata, Y, Hanaoka, H, Yamasaki, C, Maekawa, H, Barrero, RA, Lenhard, B, Datta, MW, Shimoyama, M, Bumgarner, R, Chakraborty, R, Hopkinson, I, Jia, L, Hide, W, Auffray, C, Minoshima, S, Imanishi, T & Gojobori, T 2012, 'A prioritization analysis of disease association by data-mining of functional annotation of human genes', Genomics, vol. 99, no. 1, pp. 1-9. https://doi.org/10.1016/j.ygeno.2011.10.002

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AU - Taniya, Takayuki

AU - Tanaka, Susumu

AU - Yamaguchi-Kabata, Yumi

AU - Hanaoka, Hideki

AU - Yamasaki, Chisato

AU - Maekawa, Harutoshi

AU - Barrero, Roberto A.

AU - Lenhard, Boris

AU - Datta, Milton W.

AU - Shimoyama, Mary

AU - Bumgarner, Roger

AU - Chakraborty, Ranajit

AU - Hopkinson, Ian

AU - Jia, Libin

AU - Hide, Winston

AU - Auffray, Charles

AU - Minoshima, Shinsei

AU - Imanishi, Tadashi

AU - Gojobori, Takashi

N1 - Funding Information: We thank Drs. Peter Tonellato, Arek Kasprzyk, Teruyoshi Hishiki, Craig Gough, Makoto Shimada, and Naoki Nagata for their helpful discussion and comments on this study. We also thank all the members of the H-Invitational consortium and all the staff of JBIRC for construction of H-InvDB and the PANDA system. This work is financially supported by the Ministry of Economy, Trade and Industry of Japan (METI) , the Ministry of Education, Culture, Sports, Science and Technology of Japan (MEXT) , the Japan Biological Informatics Consortium (JBiC) , and National Institute of Advanced Industrial Science and Technology (AIST) .

PY - 2012/1

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N2 - Complex diseases result from contributions of multiple genes that act in concert through pathways. Here we present a method to prioritize novel candidates of disease-susceptibility genes depending on the biological similarities to the known disease-related genes. The extent of disease-susceptibility of a gene is prioritized by analyzing seven features of human genes captured in H-InvDB. Taking rheumatoid arthritis (RA) and prostate cancer (PC) as two examples, we evaluated the efficiency of our method. Highly scored genes obtained included TNFSF12 and OSM as candidate disease genes for RA and PC, respectively. Subsequent characterization of these genes based upon an extensive literature survey reinforced the validity of these highly scored genes as possible disease-susceptibility genes. Our approach, Prioritization ANalysis of Disease Association (PANDA), is an efficient and cost-effective method to narrow down a large set of genes into smaller subsets that are most likely to be involved in the disease pathogenesis.

AB - Complex diseases result from contributions of multiple genes that act in concert through pathways. Here we present a method to prioritize novel candidates of disease-susceptibility genes depending on the biological similarities to the known disease-related genes. The extent of disease-susceptibility of a gene is prioritized by analyzing seven features of human genes captured in H-InvDB. Taking rheumatoid arthritis (RA) and prostate cancer (PC) as two examples, we evaluated the efficiency of our method. Highly scored genes obtained included TNFSF12 and OSM as candidate disease genes for RA and PC, respectively. Subsequent characterization of these genes based upon an extensive literature survey reinforced the validity of these highly scored genes as possible disease-susceptibility genes. Our approach, Prioritization ANalysis of Disease Association (PANDA), is an efficient and cost-effective method to narrow down a large set of genes into smaller subsets that are most likely to be involved in the disease pathogenesis.

KW - Data-mining

KW - Discriminant analysis

KW - Disease

KW - Gene function

KW - Prostate cancer

KW - Rheumatoid arthritis

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A prioritization analysis of disease association by data-mining of functional annotation of human genes

Abstract

Keywords

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