中文摘要：

过去20多年复杂疾病易感基因鉴定的主要方法是连锁分析和关联研究。因为连锁分析确定的数量性状位点通常很宽，加之对区域内大部分基因的功能以及基因功能和疾病之间联系的认识十分有限，所以从数量性状位点到基因的识别是一个挑战。近年来发展了一些利用公共数据库的信息预测疾病易感基因的计算生物学方法。文章简要介绍了DGP、Gene Seeker、Prioritizer、PROSPECTR and SUSPECTS及Endeavor5种计算生物学方法的基本原理，以2型糖尿病／肥胖和骨质疏松症易感基因的预测为例说明它们的应用方法，并讨论了这些方法的局限及应用前景。

英文摘要：

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.