BETASEQ: a powerful novel method to control type-I error inflation in partially sequenced data for rare variant association testing.
BETASEQ: a powerful novel method to control type-I error inflation in partially sequenced data for rare variant association testing.
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BETASEQ：一种强大的新颖方法，用于控制部分测序数据中的 I 型错误膨胀，以进行罕见变异关联测试。
DOI：
10.1093/bioinformatics/btt719
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发表时间：
2014
期刊：
影响因子：
--
通讯作者：
Li,Yun
中科院分区：
文献类型：
--
作者：
Yan,Song;Li,Yun
摘要：尽管下一代测序技术具有检测罕见变异关联的强大能力，但在应用于大样本时仍然非常昂贵。因此，在病例对照研究中，仅对病例的一个子集进行测序以发现变异并对对照中识别出的变异进行基因分型是有吸引力的，并且在合理的假设下对其余病例进行基因分型，即因果变异通常在病例中富集。然而，这种方法会导致膨胀的I型错误，如果天真地分析罕见的变异关联。在最近的文献中已经提出了几种方法来控制I型错误，其代价是排除一些测序的情况或纠正发现的罕见变异的基因型。因此，所有这些方法都遭受一定程度的信息损失，因此动力不足。我们提出了一种新的方法（BETASEQ），它通过补充伪变体来纠正I型错误的膨胀，同时保持原始序列和基因型数据的完整性。广泛的模拟和真实的数据分析表明，在大多数实际情况下，BETASEQ导致更高的测试功率比现有的方法与保证（受控或保守）的I型错误。可用性和实施：BETASEQ和相关的R文件，包括文档，示例，可在http：//www.unc.edu/yunmli/betaseqContact：songyan@unc.edu/yunli@med.unc.edu/betaseqContact：songyan@unc.edu/yunli@med.unc.edu/betaseqContact：songyan@med.unc.edu/betaseqContact：songyan@med.unc.edu/betaseqcontact：songyan@med.unc.edu/betaseqcontact：songyan@med.unc.edu/betaseqcontact：songyan@med.unc.edu/betaseqcont
Summary:Despite its great capability to detect rare variant associations, next-generation sequencing is still prohibitively expensive when applied to large samples. In case-control studies, it is thus appealing to sequence only a subset of cases to discover variants and genotype the identified variants in controls and the remaining cases under the reasonable assumption that causal variants are usually enriched among cases. However, this approach leads to inflated type-I error if analyzed naively for rare variant association. Several methods have been proposed in recent literature to control type-I error at the cost of either excluding some sequenced cases or correcting the genotypes of discovered rare variants. All of these approaches thus suffer from certain extent of information loss and thus are underpowered. We propose a novel method (BETASEQ), which corrects inflation of type-I error by supplementing pseudo-variants while keeps the original sequence and genotype data intact. Extensive simulations and real data analysis demonstrate that, in most practical situations, BETASEQ leads to higher testing powers than existing approaches with guaranteed (controlled or conservative) type-I error.Availability and implementation:BETASEQ and associated R files, including documentation, examples, are available at http://www.unc.edu/∼yunmli/betaseqContact:songyan@unc.edu or yunli@med.unc.eduSupplementary information:Supplementary data are available atBioinformaticsonline.