Lee Phil Hyoun, Shatkay Hagit
Computational Biology and Machine Learning Lab,School of Computing, Queen's University, Kingston, ON, Canada.
AMIA Annu Symp Proc. 2008 Nov 6;2008:667-71.
Identifying single nucleotide polymorphisms (SNPs) that are responsible for common and complex diseases, such as cancer, is of major interest in current molecular epidemiology. However, due to the tremendous number of SNPs on the human genome, to expedite genotyping and analysis, there is a clear need to prioritize SNPs according to their potentially deleterious effects to human health. As of yet, there have been few efforts to quantitatively assess the possible deleterious effects of SNPs for effective association studies. Here we propose a new integrative scoring system for prioritizing SNPs based on their possible deleterious effects in a probabilistic framework. We also provide the evaluation result of our system on the OMIM (Online Mendelian Inheritance in Man) database, which is one of the most widely-used databases of human genes and genetic disorders.
识别导致诸如癌症等常见复杂疾病的单核苷酸多态性(SNP)是当前分子流行病学的主要研究兴趣所在。然而,由于人类基因组上存在大量的SNP,为了加快基因分型和分析,显然需要根据SNP对人类健康的潜在有害影响对其进行优先级排序。到目前为止,几乎没有人为有效的关联研究定量评估SNP可能的有害影响。在此,我们提出一种新的综合评分系统,用于在概率框架内根据SNP可能的有害影响对其进行优先级排序。我们还提供了我们的系统在OMIM(人类在线孟德尔遗传)数据库上的评估结果,该数据库是人类基因和遗传疾病最广泛使用的数据库之一。