Hypothesis-Driven Candidate Gene Association Studies: Practical Design and Analytical Considerations
Hypothesis-Driven Candidate Gene Association Studies: Practical Design and Analytical Considerations
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DOI：
10.1093/aje/kwp242
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发表时间：
2009-10-15
影响因子：
5
通讯作者：
Alberg, Anthony J.
中科院分区：
文献类型：
--
作者：
Jorgensen, Timothy J.;Ruczinski, Ingo;Alberg, Anthony J.
候选基因关联研究(CGAs)是一种有用的流行病学方法，可用于推断基因与疾病之间的关系，特别是当实验数据支持特定生化途径的参与时。当等位基因频率低，效应大小小，或者研究人群有限或独特时，cGAS的价值就很明显。CGAS对于验证先前关于不同人群中的遗传与疾病相关的报道也很有价值。尽管cGAS有许多优点，但由于低效的研究设计或次优的分析方法，cGAS产生的信息有时会受到影响。在这里，作者讨论了与cGAS的研究设计和统计分析有关的问题，以帮助优化其有用性和信息内容。这些问题包括明智的假设驱动的生化途径、基因和单核苷酸多态的选择，以及用于衡量主要影响和评估环境暴露修改和相互作用的适当质量控制和分析程序。为了说明的目的，提出了一个使用DNA修复基因和癌症的例子的研究设计算法。
Candidate gene association studies (CGAS) are a useful epidemiologic approach to drawing inferences about relations between genes and disease, especially when experimental data support the involvement of specific biochemical pathways. The value of CGAS is apparent when allele frequencies are low, effect sizes are small, or the study population is limited or unique. CGAS is also valuable for validating previous reports of genetic associations with disease in different populations. Despite the many advantages, the information generated from CGAS is sometimes compromised because of either inefficient study design or suboptimal analytical approaches. Here the authors discuss issues related to the study design and statistical analyses of CGAS that can help to optimize their usefulness and information content. These issues include judicious hypothesis-driven selection of biochemical pathways, genes, and single nucleotide polymorphisms, as well as appropriate quality control and analytical procedures for measuring main effects and for evaluating environmental exposure modifications and interactions. A study design algorithm using the example of DNA repair genes and cancer is presented for purposes of illustration.