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Analysis of protein sequence and interaction data for candidate disease gene prediction.

Abstract：

:Linkage analysis is a successful procedure to associate diseases with specific genomic regions. These regions are often large, containing hundreds of genes, which make experimental methods employed to identify the disease gene arduous and expensive. We present two methods to prioritize candidates for further experimental study: Common Pathway Scanning (CPS) and Common Module Profiling (CMP). CPS is based on the assumption that common phenotypes are associated with dysfunction in proteins that participate in the same complex or pathway. CPS applies network data derived from protein-protein interaction (PPI) and pathway databases to identify relationships between genes. CMP identifies likely candidates using a domain-dependent sequence similarity approach, based on the hypothesis that disruption of genes of similar function will lead to the same phenotype. Both algorithms use two forms of input data: known disease genes or multiple disease loci. When using known disease genes as input, our combined methods have a sensitivity of 0.52 and a specificity of 0.97 and reduce the candidate list by 13-fold. Using multiple loci, our methods successfully identify disease genes for all benchmark diseases with a sensitivity of 0.84 and a specificity of 0.63. Our combined approach prioritizes good candidates and will accelerate the disease gene discovery process.

journal_name

Nucleic Acids Res

journal_title

Nucleic acids research

authors

George RA,Liu JY,Feng LL,Bryson-Richardson RJ,Fatkin D,Wouters MA

doi

10.1093/nar/gkl707

subject

Has Abstract

pub_date

2006-01-01 00:00:00

pages

e130

issue

19

eissn

0305-1048

issn

1362-4962

pii

gkl707

journal_volume

34

pub_type

杂志文章

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