Incorporating prior knowledge to increase the power of genome-wide association studies.
Incorporating prior knowledge to increase the power of genome-wide association studies.
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DOI：
10.1007/978-1-62703-447-0_25
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发表时间：
2013-01-01
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影响因子：
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通讯作者：
Tintle, Nathan L
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作者：
Petersen, Ashley;Spratt, Justin;Tintle, Nathan L
分析病例和对照组全基因组单核苷酸变异（SNV）数据的典型方法包括分别检测每个变异的基因型以确定表型关联，然后使用大量的多次检测惩罚来最小化假阳性率。然而，这种方法可能导致适度相关的snv的低功耗。此外，简单地观察最相关的snv可能无法直接获得关于疾病病因的生物学见解。SNVset方法试图通过测试具有生物学意义的snv集（例如，基因或通路）来解决传统方法的两个局限性。在SNVset分析中运行的测试数量通常比传统分析低得多（数百或数千而不是数百万），因此假阳性率更低。此外，通过测试具有生物学意义的snv集，发现一组显著的snv集可以更快地深入了解疾病的病因。在本章中，我们总结了SNVset测试的简短历史，并概述了许多最近提出的方法。此外，我们提供了关于如何执行SNVset分析的详细分步说明，包括研究人员在进行SNVset分析之前应该考虑的大量实用技巧和问题。最后，我们描述了一个配套的R包（snvset），它实现了最近提出的snvset方法。虽然SNVset测试是一种新方法，许多新方法仍在开发中，还有许多悬而未决的问题，但在考虑GWAS的分析方法时，该方法的前景值得认真考虑。
Typical methods of analyzing genome-wide single nucleotide variant (SNV) data in cases and controls involve testing each variant's genotypes separately for phenotype association, and then using a substantial multiple-testing penalty to minimize the rate of false positives. This approach, however, can result in low power for modestly associated SNVs. Furthermore, simply looking at the most associated SNVs may not directly yield biological insights about disease etiology. SNVset methods attempt to address both limitations of the traditional approach by testing biologically meaningful sets of SNVs (e.g., genes or pathways). The number of tests run in a SNVset analysis is typically much lower (hundreds or thousands instead of millions) than in a traditional analysis, so the false-positive rate is lower. Additionally, by testing SNVsets that are biologically meaningful finding a significant set may more quickly yield insights into disease etiology.In this chapter we summarize the short history of SNVset testing and provide an overview of the many recently proposed methods. Furthermore, we provide detailed step-by-step instructions on how to perform a SNVset analysis, including a substantial number of practical tips and questions that researchers should consider before undertaking a SNVset analysis. Lastly, we describe a companion R package (snvset) that implements recently proposed SNVset methods. While SNVset testing is a new approach, with many new methods still being developed and many open questions, the promise of the approach is worth serious consideration when considering analytic methods for GWAS.