For the past two decades, the dominant methods to identify susceptibility genes of complex disease were linkage analysis and association study. Linkage analysis usually identifies broad intervals, which can encompass dozens to hundreds of candidate genes. Transition from quantitative trait loci to gene has been a challenge due to the absence of com-plete functional information for the majority of genes in this susceptibility locus and limited knowledge of the link between gene function and disease. Recently, computational biology tools that employ information extracted from public online da-tabases have been developed. In this review, we introduced principles of DGP, GeneSeeker, Prioritizer, PROSPECTR and SUSPECTS (P and S), and Endeavor, then used the prediction of susceptibility genes for type 2 diabetes mellitus/obesity and osteoporosis as examples to elucidate the application of computational biology strategies, and finally discuss the limita-tions and prospects of these methods.
WANG Min, ZHANG Shuang, HUANG Qing-Yang
. Computational biology strategy for identification of complex disease genes[J]. Hereditas(Beijing), 2009
, 31(6)
: 581
-586
.
DOI: 10.3724/SP.J.1005.2009.00581
