title: [Computational biology strategy for identification of complex disease genes].
abstract: 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 complete 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 databases 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 limitations and prospects of these methods.
authors: College of Life Sciences, Central China Normal University, Wuhan 430079, China. xiaomin163-111@163.com
authors: Min
authors: M
authors: Wang
authors: Shuang
authors: S
authors: Zhang
authors: Qing-Yang
authors: QY
authors: Huang
mesh_terms: D001321:Autistic Disorder; D019295:Computational Biology; D003198:Computer Simulation; D030541:Databases, Genetic; D003924:Diabetes Mellitus, Type 2 / Q000235:genetics; D020022:Genetic Predisposition to Disease; D005820:Genetic Testing; D006801:Humans; D020407:Internet; D009765:Obesity / Q000235:genetics; D010024:Osteoporosis; D010375:Pedigree; D040641:Quantitative Trait Loci; D012984:Software