Japan Biological Information Research Center, Japan Biological Informatics Consortium, AIST Bio-IT Research Building 7F, 2-4-7 Aomi, Tokyo 135-0064, Japan.
Genomics. 2012 Jan;99(1):1-9. doi: 10.1016/j.ygeno.2011.10.002. Epub 2011 Oct 14.
Complex diseases result from contributions of multiple genes that act in concert through pathways. Here we present a method to prioritize novel candidates of disease-susceptibility genes depending on the biological similarities to the known disease-related genes. The extent of disease-susceptibility of a gene is prioritized by analyzing seven features of human genes captured in H-InvDB. Taking rheumatoid arthritis (RA) and prostate cancer (PC) as two examples, we evaluated the efficiency of our method. Highly scored genes obtained included TNFSF12 and OSM as candidate disease genes for RA and PC, respectively. Subsequent characterization of these genes based upon an extensive literature survey reinforced the validity of these highly scored genes as possible disease-susceptibility genes. Our approach, Prioritization ANalysis of Disease Association (PANDA), is an efficient and cost-effective method to narrow down a large set of genes into smaller subsets that are most likely to be involved in the disease pathogenesis.
复杂疾病是由多个基因共同作用通过途径产生的。在这里，我们提出了一种根据与已知疾病相关基因的生物学相似性来优先考虑疾病易感性基因新候选者的方法。通过分析 H-InvDB 中捕获的人类基因的七个特征，对一个基因的疾病易感性程度进行优先级排序。以类风湿关节炎 (RA) 和前列腺癌 (PC) 为例，我们评估了我们方法的效率。获得的高评分基因包括 TNFSF12 和 OSM，分别为 RA 和 PC 的候选疾病基因。基于广泛文献调查对这些基因进行的后续特征描述，证实了这些高评分基因作为可能的疾病易感性基因的有效性。我们的方法，即疾病关联分析优先级 (PANDA)，是一种有效且具有成本效益的方法，可以将一大组基因缩小为更有可能参与疾病发病机制的较小子集。
