RVTESTS: an efficient and comprehensive tool for rare variant association analysis using sequence data.
RVTESTS: an efficient and comprehensive tool for rare variant association analysis using sequence data.
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DOI：
10.1093/bioinformatics/btw079
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发表时间：
2016-05-01
期刊：
影响因子：
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通讯作者：
Liu DJ
中科院分区：
文献类型：
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作者：
Zhan X;Hu Y;Li B;Abecasis GR;Liu DJ
动机：下一代测序技术能够大规模评估罕见和低频遗传变异对复杂人类疾病的影响。通常进行基因水平关联测试来分析罕见变异，其中联合分析基因区域中的多个罕见变异。应用基因级关联测试来分析序列数据通常需要整合多个异质信息源（例如注释、功能预测评分、等位基因频率、基因型和表型），以确定最佳分析单元并优先考虑因果变异。鉴于当前序列数据集和生物信息学数据库的复杂性和规模，迫切需要更高效的软件工具来促进这些分析。为了应对这一挑战，我们开发了 RVTESTS，它实现了一组广泛的罕见变异关联统计数据，并支持对不相关和相关个体的常染色体和 X 连锁变异进行分析。 RVTESTS 还提供有用的配套功能，用于注释序列变异、集成生物信息学数据库、执行数据质量控制和样本选择。我们使用千人基因组计划数据说明了 RVTESTS 在功能和效率方面的优势。可用性和实施：RVTETS 可在 Linux、MacOS 和 Windows 上使用。源代码和可执行文件可以在https://github.com/zhanxw/rvtests获取联系方式：zhanxw@gmail.com； goncalo@umich.edu； dajian.liu@outlook.com 补充信息：补充数据可在生物信息学在线获取。
Motivation: Next-generation sequencing technologies have enabled the large-scale assessment of the impact of rare and low-frequency genetic variants for complex human diseases. Gene-level association tests are often performed to analyze rare variants, where multiple rare variants in a gene region are analyzed jointly. Applying gene-level association tests to analyze sequence data often requires integrating multiple heterogeneous sources of information (e.g. annotations, functional prediction scores, allele frequencies, genotypes and phenotypes) to determine the optimal analysis unit and prioritize causal variants. Given the complexity and scale of current sequence datasets and bioinformatics databases, there is a compelling need for more efficient software tools to facilitate these analyses. To answer this challenge, we developed RVTESTS, which implements a broad set of rare variant association statistics and supports the analysis of autosomal and X-linked variants for both unrelated and related individuals. RVTESTS also provides useful companion features for annotating sequence variants, integrating bioinformatics databases, performing data quality control and sample selection. We illustrate the advantages of RVTESTS in functionality and efficiency using the 1000 Genomes Project data. Availability and implementation: RVTESTS is available on Linux, MacOS and Windows. Source code and executable files can be obtained at https://github.com/zhanxw/rvtests Contact: zhanxw@gmail.com; goncalo@umich.edu; dajiang.liu@outlook.com Supplementary information: Supplementary data are available at Bioinformatics online.