Comparison of statistical approaches to rare variant analysis for quantitative traits.
Comparison of statistical approaches to rare variant analysis for quantitative traits.
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DOI：
10.1186/1753-6561-5-s9-s113
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发表时间：
2011-11-29
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影响因子：
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通讯作者：
Liu, Ching-Ti
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作者：
Chen, Han;Hendricks, Audrey E;Liu, Ching-Ti
随着最近技术的进步，深度测序数据将被广泛用于进一步了解基因对感兴趣的性状的影响。因此，不仅需要更好地利用常见的变异体，而且需要更好地使用稀有变异体来利用深度测序数据提供的新信息。最近，已经提出了一些统计方法来分析遗传关联研究中的罕见变异，但其中许多方法只针对两种结果而设计。我们比较了几种适用于数量性状的统计方法的I型错误和威力，这些方法用于折叠和分析定义的基因区域内的罕见变异数据。除了比较只考虑稀有变量的方法，如指标、计数和数据自适应折叠方法外，我们还比较了结合常见变量分析和稀有变量分析的方法，如CMC和套索回归。我们发现，用于崩溃稀有变量的三种方法在这个模拟环境中执行类似的操作，其中所有风险变量都被模拟为在相同方向上具有影响。此外，我们发现，纳入共同变量是有益的，当与风险变量总数相比，共同风险变量较少时，使用套索回归选择要包括的共同变量是最有用的。
With recent advances in technology, deep sequencing data will be widely used to further the understanding of genetic influence on traits of interest. Therefore not only common variants but also rare variants need to be better used to exploit the new information provided by deep sequencing data. Recently, statistical approaches for analyzing rare variants in genetic association studies have been proposed, but many of them were designed only for dichotomous outcomes. We compare the type I error and power of several statistical approaches applicable to quantitative traits for collapsing and analyzing rare variant data within a defined gene region. In addition to comparing methods that consider only rare variants, such as indicator, count, and data-adaptive collapsing methods, we also compare methods that incorporate the analysis of common variants along with rare variants, such as CMC and LASSO regression. We find that the three methods used to collapse rare variants perform similarly in this simulation setting where all risk variants were simulated to have effects in the same direction. Further, we find that incorporating common variants is beneficial and using a LASSO regression to choose which common variants to include is most useful when there is are few common risk variants compared to the total number of risk variants.