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简单表型:多效性、连锁和上位性表型的模拟。

simplePHENOTYPES: SIMulation of pleiotropic, linked and epistatic phenotypes.

机构信息

Department of Crop Sciences, University of Illinois at Urbana-Champaign, Urbana, 61801, USA.

出版信息

BMC Bioinformatics. 2020 Oct 31;21(1):491. doi: 10.1186/s12859-020-03804-y.

DOI:10.1186/s12859-020-03804-y
PMID:33129253
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7603745/
Abstract

BACKGROUND

Advances in genotyping and phenotyping techniques have enabled the acquisition of a great amount of data. Consequently, there is an interest in multivariate statistical analyses that identify genomic regions likely to contain causal mutations affecting multiple traits (i.e., pleiotropy). As the demand for multivariate analyses increases, it is imperative that optimal tools are available to assess their performance. To facilitate the testing and validation of these multivariate approaches, we developed simplePHENOTYPES, an R/CRAN package that simulates pleiotropy, partial pleiotropy, and spurious pleiotropy in a wide range of genetic architectures, including additive, dominance and epistatic models.

RESULTS

We illustrate simplePHENOTYPES' ability to simulate thousands of phenotypes in less than one minute. We then provide two vignettes illustrating how to simulate sets of correlated traits in simplePHENOTYPES. Finally, we demonstrate the use of results from simplePHENOTYPES in a standard GWAS software, as well as the equivalence of simulated phenotypes from simplePHENOTYPES and other packages with similar capabilities.

CONCLUSIONS

simplePHENOTYPES is a R/CRAN package that makes it possible to simulate multiple traits controlled by loci with varying degrees of pleiotropy. Its ability to interface with both commonly-used marker data formats and downstream quantitative genetics software and packages should facilitate a rigorous assessment of both existing and emerging statistical GWAS and GS approaches. simplePHENOTYPES is also available at https://github.com/samuelbfernandes/simplePHENOTYPES .

摘要

背景

基因分型和表型分析技术的进步使得大量数据的获取成为可能。因此,人们对多元统计分析方法产生了兴趣,这些方法可以识别可能包含影响多个性状(即多效性)的因果突变的基因组区域。随着对多元分析需求的增加,必须提供最佳工具来评估它们的性能。为了方便这些多元方法的测试和验证,我们开发了 simplePHENOTYPES,这是一个 R/CRAN 包,可以模拟广泛的遗传结构中的多效性、部分多效性和虚假多效性,包括加性、显性和上位性模型。

结果

我们说明了 simplePHENOTYPES 在不到一分钟的时间内模拟数千个表型的能力。然后,我们提供了两个示例,说明如何在 simplePHENOTYPES 中模拟一组相关性状。最后,我们展示了如何在标准 GWAS 软件中使用 simplePHENOTYPES 的结果,以及 simplePHENOTYPES 和其他具有类似功能的模拟表型包的模拟表型的等效性。

结论

simplePHENOTYPES 是一个 R/CRAN 包,它可以模拟由具有不同程度多效性的基因座控制的多个性状。它能够与常用的标记数据格式以及下游数量遗传学软件和包进行接口,这应该有助于对现有的和新兴的统计 GWAS 和 GS 方法进行严格评估。simplePHENOTYPES 也可以在 https://github.com/samuelbfernandes/simplePHENOTYPES 上获得。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8db4/7603745/1594929ab2ad/12859_2020_3804_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8db4/7603745/90ac72c96428/12859_2020_3804_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8db4/7603745/07f78cea7bd9/12859_2020_3804_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8db4/7603745/1ec30557a397/12859_2020_3804_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8db4/7603745/c8da76fbab9a/12859_2020_3804_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8db4/7603745/1594929ab2ad/12859_2020_3804_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8db4/7603745/90ac72c96428/12859_2020_3804_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8db4/7603745/07f78cea7bd9/12859_2020_3804_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8db4/7603745/1ec30557a397/12859_2020_3804_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8db4/7603745/c8da76fbab9a/12859_2020_3804_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8db4/7603745/1594929ab2ad/12859_2020_3804_Fig5_HTML.jpg

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