Weiss S T, Litonjua A A, Lange C, Lazarus R, Liggett S B, Bleecker E R, Tantisira K G
Channing Laboratory, Brigham and Women's Hospital, Boston, MA 02115, USA.
Pharmacogenomics J. 2006 Sep-Oct;6(5):311-26. doi: 10.1038/sj.tpj.6500387. Epub 2006 Apr 25.
Asthma affects approximately 300 million individuals worldwide. Medications comprise a substantial portion of asthma expenditures. Despite the availability of three primary therapeutic classes of medications, there are a significant number of nonresponders to therapy. Available data, as well as previous pharmacogenetic studies, suggest that genetics may contribute as much as 60-80% to the interindividual variability in treatment response. In this methodologic review, after providing a broad overview of the asthma pharmacogenetics literature to date, we describe the application of a novel family-based screening algorithm to the analysis of pharmacogenetic data and highlight our approach to identifying and verifying loci influencing asthma treatment response. This approach seeks to address issues related to multiple comparisons, statistical power, population stratification, and failure to replicate from which previous population-based or case-control pharmacogenetic association studies may suffer. Identification of such replicable loci is the next step towards the goal of 'individualized therapy' for asthma.
哮喘影响着全球约3亿人。药物治疗占哮喘治疗费用的很大一部分。尽管有三类主要的治疗药物,但仍有相当数量的患者对治疗无反应。现有数据以及先前的药物遗传学研究表明,遗传学因素对个体治疗反应差异的影响可能高达60%-80%。在本方法学综述中,在对迄今为止的哮喘药物遗传学文献进行广泛概述之后,我们描述了一种基于家系的新型筛查算法在药物遗传学数据分析中的应用,并强调了我们识别和验证影响哮喘治疗反应的基因座的方法。这种方法旨在解决先前基于人群或病例对照的药物遗传学关联研究可能面临的多重比较、统计效力、群体分层和无法重复等问题。识别此类可重复的基因座是实现哮喘“个体化治疗”目标的下一步。