Wang Y, Tong C, Wang Z, Wang Z, Mauger D, Tantisira K G, Israel E, Szefler S J, Chinchilli V M, Boushey H A, Lazarus S C, Lemanske R F, Wu R
Division of Biostatistics and Bioinformatics, Department of Public Health Sciences, Penn State College of Medicine, Hershey, PA, USA.
Division of Biostatistics, Yale School of Public Health, New Haven, CT, USA.
Pharmacogenomics J. 2015 Oct;15(5):422-9. doi: 10.1038/tpj.2014.83. Epub 2015 Jan 20.
Asthma is a chronic lung disease that has a high prevalence. The therapeutic intervention of this disease can be made more effective if genetic variability in patients' response to medications is implemented. However, a clear picture of the genetic architecture of asthma intervention response remains elusive. We conducted a genome-wide association study (GWAS) to identify drug response-associated genes for asthma, in which 909 622 SNPs were genotyped for 120 randomized participants who inhaled multiple doses of glucocorticoids. By integrating pharmacodynamic properties of drug reactions, we implemented a mechanistic model to analyze the GWAS data, enhancing the scope of inference about the genetic architecture of asthma intervention. Our pharmacodynamic model observed associations of genome-wide significance between dose-dependent response to inhaled glucocorticoids (measured as %FEV1) and five loci (P=5.315 × 10(-7) to 3.924 × 10(-9)), many of which map to metabolic genes related to lung function and asthma risk. All significant SNPs detected indicate a recessive effect, at which the homozygotes for the mutant alleles drive variability in %FEV1. Significant associations were well replicated in three additional independent GWAS studies. Pooled together over these three trials, two SNPs, chr6 rs6924808 and chr11 rs1353649, display an increased significance level (P=6.661 × 10(-16) and 5.670 × 10(-11)). Our study reveals a general picture of pharmacogenomic control for asthma intervention. The results obtained help to tailor an optimal dose for individual patients to treat asthma based on their genetic makeup.
哮喘是一种高发性的慢性肺部疾病。如果能考虑到患者对药物反应的基因变异性,这种疾病的治疗干预可能会更有效。然而,哮喘干预反应的基因结构仍不清楚。我们进行了一项全基因组关联研究(GWAS),以确定与哮喘药物反应相关的基因,对120名吸入多剂量糖皮质激素的随机参与者进行了909622个单核苷酸多态性(SNP)的基因分型。通过整合药物反应的药效学特性,我们采用了一种机制模型来分析GWAS数据,扩大了对哮喘干预基因结构的推断范围。我们的药效学模型观察到吸入糖皮质激素的剂量依赖性反应(以FEV1%衡量)与五个基因座之间存在全基因组显著关联(P = 5.315×10^(-7)至3.924×10^(-9)),其中许多基因座映射到与肺功能和哮喘风险相关的代谢基因。检测到的所有显著SNP均显示出隐性效应,即突变等位基因的纯合子驱动FEV1%的变异性。这些显著关联在另外三项独立的GWAS研究中得到了很好的重复验证。在这三项试验中汇总后,两个SNP,即chr6 rs6924808和chr11 rs1353649,显示出更高的显著水平(P = 6.661×10^(-16)和5.670×10^(-11))。我们的研究揭示了哮喘干预的药物基因组学控制的总体情况。所得结果有助于根据个体患者的基因构成量身定制最佳剂量以治疗哮喘。