Polyunphased:基于家庭的遗传关联分析的Unphased软件包的多元体结果的扩展

IF 0.9 4区 数学 Q3 Mathematics
A. Bureau, J. Croteau
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引用次数: 0

摘要

摘要当一种疾病具有多种亚型或同时分析两种二分表型时,就会出现多染色体表型。在家族研究中,很少有软件程序提供分析此类表型的选项,也没有一个软件程序实现对群体分层稳健的家族内分析的条件多元逻辑回归。我们介绍了Polyunphased,它是Unphased软件包的多晶表型的扩展,是一种用于核心家族遗传关联分析的灵活软件工具。与Unphased一样,Polyunphased是用C++编写的,可以从命令行或Java图形用户界面运行。在Polyunphased中,大多数无相位选项仍然可用,包括那些处理缺失的亲本基因型同时保持对群体分层的稳健性的选项,以及建模选项。模拟研究证实了当似然函数的父母项中的相应关联参数设置为0时,条件逻辑回归模型的关联参数的最大似然估计的预期统计行为,但揭示了单独估计这些父母关联参数时的收敛问题。因此,前一种方法被推荐用于多胞表型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Polyunphased: an extension to polytomous outcomes of the Unphased package for family-based genetic association analysis
Abstract Polytomous phenotypes arise when a disease has multiple subtypes or when two dichotomous phenotypes are analyzed simultaneously. Few software programs offer the option to analyze such phenotypes in family studies, and none implements conditional polytomous logistic regression for within-family analysis robust to population stratification. We introduce Polyunphased, an extension to polytomous phenotypes of the Unphased package, a flexible software tool for genetic association analysis in nuclear families. Like Unphased, Polyunphased is written in C++ and runs from the command line or from a Java graphical user interface. Most Unphased options remain available in Polyunphased, including those handling missing parental genotypes while preserving robustness to population stratification, and the modelling options. Simulation studies confirmed the expected statistical behaviour of the maximum likelihood estimates of the association parameters of the conditional logistic regression model when the corresponding association parameters in the parental term of the likelihood function are set to 0, but revealed convergence problems when estimating these parental association parameters separately. The former approach is thus recommended with polytomous phenotypes.
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来源期刊
CiteScore
1.20
自引率
11.10%
发文量
8
审稿时长
6-12 weeks
期刊介绍: Statistical Applications in Genetics and Molecular Biology seeks to publish significant research on the application of statistical ideas to problems arising from computational biology. The focus of the papers should be on the relevant statistical issues but should contain a succinct description of the relevant biological problem being considered. The range of topics is wide and will include topics such as linkage mapping, association studies, gene finding and sequence alignment, protein structure prediction, design and analysis of microarray data, molecular evolution and phylogenetic trees, DNA topology, and data base search strategies. Both original research and review articles will be warmly received.
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