Journal Article

Prioritizing risk pathways: a novel association approach to searching for disease pathways fusing SNPs and pathways

Lina Chen, Liangcai Zhang, Yan Zhao, Liangde Xu, Yukui Shang, Qian Wang, Wan Li, Hong Wang and Xia Li

in Bioinformatics

Volume 25, issue 2, pages 237-242
Published in print January 2009 | ISSN: 1367-4803
Published online November 2008 | e-ISSN: 1460-2059 | DOI:
Prioritizing risk pathways: a novel association approach to searching for disease pathways fusing SNPs and pathways

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Motivation: Complex diseases are generally thought to be under the influence of one or more mutated risk genes as well as genetic and environmental factors. Many traditional methods have been developed to identify susceptibility genes assuming a single-gene disease model (‘single-locus methods’). Pathway-based approaches, combined with traditional methods, consider the joint effects of genetic factor and biologic network context. With the accumulation of high-throughput SNP datasets and human biologic pathways, it becomes feasible to search for risk pathways associated with complex diseases using bioinformatics methods. By analyzing the contribution of genetic factor and biologic network context in KEGG (Kyoto Encyclopedia of Genes and Genomes) pathways, we proposed an approach to prioritize risk pathways for complex diseases: Prioritizing Risk Pathways fusing SNPs and pathways (PRP). A risk-scoring (RS) measurement was used to prioritize risk biologic pathways. This could help to demonstrate the pathogenesis of complex diseases from a new perspective and provide new hypotheses. We introduced this approach to five complex diseases and found that these five diseases not only share common risk pathways, but also have their specific risk pathways, which is verified by literature retrieval.

Availability: Genotype frequencies of five case–control samples were downloaded from the WTCCC online system and the address is


Supplementary information: Supplementary data are available at Bioinformatics online.

Journal Article.  4292 words.  Illustrated.

Subjects: Bioinformatics and Computational Biology

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