Center for Cancer Genomics, Wake Forest University School of Medicine, Winston-Salem, NC, USA.
Pharmacogenomics. 2010 Oct;11(10):1403-25. doi: 10.2217/pgs.10.99.
Polymorphisms of genes involved in the pharmacokinetic and pharmacodynamic processes underlie the divergent drug responses among individuals. Despite some successes in identifying these polymorphisms, the candidate gene approach suffers from insufficient gene coverage whereas the genome-wide association approach is limited by less than ideal coverage of SNPs in some important genes. To expand the potential of the candidate approach, we aim to delineate a comprehensive network of drug-response genes for in-depth genetic studies.
MATERIALS & METHODS: Pharmacologically important genes were extracted from various sources including literatures and web resources. These genes, along with their homologs and regulatory miRNAs, were organized based on their pharmacological functions and weighted by literature evidence and confidence levels. Their coverage was evaluated by analyzing three commercial SNP chips commonly used for genome-wide association studies: Affymetrix SNP array 6.0, Illumina HumanHap1M and Illumina Omni.
A panel of drug-response genes was constructed, which contains 923 pharmacokinetic genes, 703 pharmacodynamic genes and 720 miRNAs. There are only 16.7% of these genes whose all known SNPs can be directly or indirectly (r(2) > 0.8) captured by the SNP chips with coverage of more than 80%. This is possibly because these SNPs chips have notably poor performance over rare SNPs and miRNA genes.
We have compiled a panel of candidate genes that may be pharmacologically important. Using this knowledgebase, we are able to systematically evaluate genes and their variants that govern an individual's response to a given pharmaceutical therapy. This approach can serve as a necessary complement to genome-wide associations.
参与药代动力学和药效动力学过程的基因多态性是个体间药物反应差异的基础。尽管在鉴定这些多态性方面取得了一些成功,但候选基因方法存在基因覆盖不足的问题,而全基因组关联方法则受到一些重要基因中 SNP 覆盖不理想的限制。为了扩大候选方法的潜力,我们旨在描绘一个全面的药物反应基因网络,以进行深入的遗传研究。
从文献和网络资源等各种来源提取药理学上重要的基因。这些基因及其同源物和调节 miRNA 根据其药理学功能进行组织,并根据文献证据和置信水平进行加权。通过分析三种常用于全基因组关联研究的商业 SNP 芯片:Affymetrix SNP 阵列 6.0、Illumina HumanHap1M 和 Illumina Omni,评估它们的覆盖范围。
构建了一个药物反应基因面板,其中包含 923 个药代动力学基因、703 个药效动力学基因和 720 个 miRNA。这些基因中只有 16.7%的基因,其所有已知的 SNP 可以直接或间接(r(2) > 0.8)被 SNP 芯片覆盖超过 80%。这可能是因为这些 SNP 芯片在稀有 SNP 和 miRNA 基因方面表现不佳。
我们已经编译了一组候选基因,这些基因可能在药理学上很重要。利用这个知识库,我们能够系统地评估个体对特定药物治疗反应的基因及其变体。这种方法可以作为全基因组关联的必要补充。