Faculty Collaboration Database

A prioritization analysis of disease association by data-mining of functional annotation of human genes. Genomics 2012 Jan;99(1):1-9

Date

10/25/2011

Pubmed ID

22019378

DOI

10.1016/j.ygeno.2011.10.002

Scopus ID

2-s2.0-84855195943 (requires institutional sign-in at Scopus site)   8 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.

Author List

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

MESH terms used to index this publication - Major topics in bold

Arthritis, Rheumatoid
Cost-Benefit Analysis
Cytokine TWEAK
Data Mining
Genetic Association Studies
Genetic Predisposition to Disease
Genomics
Humans
Male
Oncostatin M
Prostatic Neoplasms
Tumor Necrosis Factors