CNVassoc: Association analysis of CNV data using R.
CNVassoc: Association analysis of CNV data using R.
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DOI：
10.1186/1755-8794-4-47
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发表时间：
2011-05-24
影响因子：
2.7
通讯作者：
Gonzalez JR
中科院分区：
文献类型：
--
作者：
Subirana I;Diaz-Uriarte R;Lucas G;Gonzalez JR
拷贝数变异(CNV)是常见复杂疾病风险遗传贡献的潜在重要组成部分。分析CNV和疾病之间的联系需要考虑CNV拷贝数调用的不确定性，这种不确定性可能是很大的；如果不考虑这种不确定性，可能会导致偏颇的结果。因此，有必要开发和使用适当的统计工具。为了解决这个问题，我们开发了CNVassoc，这是一个R包，用于在基于人群的研究中对常见的拷贝数变异进行关联分析。该软件包包括在一系列研究设计(病例对照、队列等)和遗传模型下测试与不同类别的反应变量(例如，类别状态、经审查的数据、计数)的关联性，并对协变量进行调整。该程序包包括推断拷贝数(CNV基因调用)的功能，但也可以接受其他算法(如Canary、CGHcall、Pupute)产生的拷贝数数据。在这里，我们介绍了一个新的R包，CNVassoc，它可以处理来自不同平台的不同类型的CNV，如MLPA o aCGH。通过一个实际数据的例子说明了我们的方法能够将不确定性融入到关联过程中。我们还展示了在分析推定的SNP时，我们的程序包在分析推定的数据时也是如何有用的。通过仿真研究表明，CNVassoc在计算时间和收敛失败率方面均优于CNV工具。我们提供了一套在建模灵活性、能力、收敛速度、协变量调整的简易性以及对样本大小和信号质量的要求方面优于现有方案的方案。因此，我们提供CNVassoc作为CNV相关性研究的常规方法。
Copy number variants (CNV) are a potentially important component of the genetic contribution to risk of common complex diseases. Analysis of the association between CNVs and disease requires that uncertainty in CNV copy-number calls, which can be substantial, be taken into account; failure to consider this uncertainty can lead to biased results. Therefore, there is a need to develop and use appropriate statistical tools. To address this issue, we have developed CNVassoc, an R package for carrying out association analysis of common copy number variants in population-based studies. This package includes functions for testing for association with different classes of response variables (e.g. class status, censored data, counts) under a series of study designs (case-control, cohort, etc) and inheritance models, adjusting for covariates. The package includes functions for inferring copy number (CNV genotype calling), but can also accept copy number data generated by other algorithms (e.g. CANARY, CGHcall, IMPUTE). Here we present a new R package, CNVassoc, that can deal with different types of CNV arising from different platforms such as MLPA o aCGH. Through a real data example we illustrate that our method is able to incorporate uncertainty in the association process. We also show how our package can also be useful when analyzing imputed data when analyzing imputed SNPs. Through a simulation study we show that CNVassoc outperforms CNVtools in terms of computing time as well as in convergence failure rate. We provide a package that outperforms the existing ones in terms of modelling flexibility, power, convergence rate, ease of covariate adjustment, and requirements for sample size and signal quality. Therefore, we offer CNVassoc as a method for routine use in CNV association studies.
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影响因子：
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通讯作者：
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