PLINK: A tool set for whole-genome association and population-based linkage analyses
PLINK: A tool set for whole-genome association and population-based linkage analyses
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DOI：
10.1086/519795
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发表时间：
2007-09-01
影响因子：
9.8
通讯作者：
Sham, Pak C.
中科院分区：
文献类型：
--
作者：
Purcell, Shaun;Neale, Benjamin;Sham, Pak C.
全基因组协会研究（WGAS）为研究人员带来了新的计算以及分析性的挑战。许多现有的遗传分析工具并非旨在以方便的方式处理如此大的数据集，并且不一定利用全基因组数据带来的新机会。为了解决这些问题，我们开发了PLINK，这是一个开源C/C ++ WGAS工具集。借助PLINK，可以迅速操纵和分析的大量数据集，其中包括成千上万个个体的数十万个标记基因分型。除了提供使基本分析步骤计算高效的工具外，PLINK还支持一些利用全基因组覆盖范围的全基因组数据的新颖方法。我们介绍PLINK并描述功能的五个主要领域：数据管理，摘要统计，人口分层，关联分析和逐个估计估计。特别是，我们专注于在基于人群的全基因组研究的背景下逐个状态和逐个状态信息的估计和使用。该信息可用于检测和纠正种群分层，并确定扩展的染色体片段，这些细分通过非常遥远的个体之间的下降共享相同的染色体段。分析分段共享模式的分析有可能绘制疾病基因座，这些疾病基因座在基于人群的链接分析中包含多个稀有变体。
Whole-genome association studies (WGAS) bring new computational, as well as analytic, challenges to researchers. Many existing genetic-analysis tools are not designed to handle such large data sets in a convenient manner and do not necessarily exploit the new opportunities that whole-genome data bring. To address these issues, we developed PLINK, an open-source C/C++ WGAS tool set. With PLINK, large data sets comprising hundreds of thousands of markers genotyped for thousands of individuals can be rapidly manipulated and analyzed in their entirety. As well as providing tools to make the basic analytic steps computationally efficient, PLINK also supports some novel approaches to whole-genome data that take advantage of whole-genome coverage. We introduce PLINK and describe the five main domains of function: data management, summary statistics, population stratification, association analysis, and identity-by-descent estimation. In particular, we focus on the estimation and use of identity-by-state and identity-by-descent information in the context of population-based whole-genome studies. This information can be used to detect and correct for population stratification and to identify extended chromosomal segments that are shared identical by descent between very distantly related individuals. Analysis of the patterns of segmental sharing has the potential to map disease loci that contain multiple rare variants in a population-based linkage analysis.