Torkamani Ali, Schork Nicholas J
Department of Medicine, Center for Human Genetics and Genomics, The Scripps Research Institute, University of California, San Diego, La Jolla, CA 92093, USA.
Bioinformatics. 2007 Nov 1;23(21):2918-25. doi: 10.1093/bioinformatics/btm437. Epub 2007 Sep 12.
Contemporary, high-throughput sequencing efforts have identified a rich source of naturally occurring single nucleotide polymorphisms (SNPs), a subset of which occur in the coding region of genes and result in a change in the encoded amino acid sequence (non-synonymous coding SNPs or 'nsSNPs'). It is hypothesized that a subset of these nsSNPs may underlie common human disease. Testing all these polymorphisms for disease association would be time consuming and expensive. Thus, computational methods have been developed to both prioritize candidate nsSNPs and make sense of their likely molecular physiologic impact.
We have developed a method to prioritize nsSNPs and have applied it to the human protein kinase gene family. The results of our analyses provide high quality predictions and outperform available whole genome prediction methods (74% versus 83% prediction accuracy). Our analyses and methods consider both DNA sequence conservation, which most traditional methods are based on, as well unique structural and functional features of kinases. We provide a ranked list of common kinase nsSNPs that have a higher probability of impacting human disease based on our analyses.
当代高通量测序工作已发现大量自然发生的单核苷酸多态性（SNP），其中一部分出现在基因的编码区，导致编码的氨基酸序列发生改变（非同义编码SNP或“nsSNP”）。据推测，这些nsSNP中的一部分可能是常见人类疾病的潜在病因。检测所有这些多态性与疾病的关联既耗时又昂贵。因此，已开发出计算方法来对候选nsSNP进行优先级排序，并了解它们可能的分子生理影响。
我们开发了一种对nsSNP进行优先级排序的方法，并将其应用于人类蛋白激酶基因家族。我们的分析结果提供了高质量的预测，并且优于现有的全基因组预测方法（预测准确率分别为74%和83%）。我们的分析和方法既考虑了大多数传统方法所基于的DNA序列保守性，也考虑了激酶独特的结构和功能特征。基于我们的分析，我们提供了一份常见激酶nsSNP的排名列表，这些nsSNP更有可能影响人类疾病。
