lme4qtl: linear mixed models with flexible covariance structure for genetic studies of related individuals.
lme4qtl: linear mixed models with flexible covariance structure for genetic studies of related individuals.
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DOI：
10.1186/s12859-018-2057-x
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发表时间：
2018-02-27
影响因子：
3
通讯作者：
Soria JM
中科院分区：
文献类型：
--
作者：
Ziyatdinov A;Vázquez-Santiago M;Brunel H;Martinez-Perez A;Aschard H;Soria JM
遗传数据中的数量性状基因座（QTL）定位通常涉及相关观察结果的分析，这些观察结果需要被解释以避免错误的关联信号。这通常通过在线性混合模型（Linear Mixed Models，LMM）中将此类相关性建模为随机效应来执行。R软件包lme 4是一个成熟的工具，使用稀疏矩阵方法实现主要的LMM功能;然而，它并不完全适合QTL作图关联和连锁研究。特别是，两个LMM功能缺乏的基础版本的lme 4：自定义协方差矩阵的随机效应的定义和参数约束，这是必不可少的先进的QTL模型。除了在相关个体的连锁研究中的应用外，这些功能对于需要建模多个协方差矩阵的情况下的关联研究具有高度的兴趣，这是许多全基因组关联研究（GWAS）软件所不涵盖的场景。为了解决上述限制，我们开发了一个新的R包lme 4 qtl作为lme 4的扩展。首先，lme 4 qtl在与lme 4及其配套软件包集成的单一工具中为遗传研究提供了新模型。其次，lme 4 qtl提供了一个灵活的框架，多层次的相关性的情况下，变得有效的协方差矩阵稀疏。我们在特发性血栓形成倾向2（GAIT 2）的遗传分析项目中使用真实的基于家族的数据显示了我们的包的价值。我们的软件lme 4 qtl使QTL作图模型具有通用的随机效应结构和稀疏协方差的有效计算。lme 4 qtl可从https://github.com/variani/lme4qtl获得。本文的在线版本（10.1186/s12859-018-2057-x）包含补充材料，可供授权用户使用。
Quantitative trait locus (QTL) mapping in genetic data often involves analysis of correlated observations, which need to be accounted for to avoid false association signals. This is commonly performed by modeling such correlations as random effects in linear mixed models (LMMs). The R package lme4 is a well-established tool that implements major LMM features using sparse matrix methods; however, it is not fully adapted for QTL mapping association and linkage studies. In particular, two LMM features are lacking in the base version of lme4: the definition of random effects by custom covariance matrices; and parameter constraints, which are essential in advanced QTL models. Apart from applications in linkage studies of related individuals, such functionalities are of high interest for association studies in situations where multiple covariance matrices need to be modeled, a scenario not covered by many genome-wide association study (GWAS) software. To address the aforementioned limitations, we developed a new R package lme4qtl as an extension of lme4. First, lme4qtl contributes new models for genetic studies within a single tool integrated with lme4 and its companion packages. Second, lme4qtl offers a flexible framework for scenarios with multiple levels of relatedness and becomes efficient when covariance matrices are sparse. We showed the value of our package using real family-based data in the Genetic Analysis of Idiopathic Thrombophilia 2 (GAIT2) project. Our software lme4qtl enables QTL mapping models with a versatile structure of random effects and efficient computation for sparse covariances. lme4qtl is available at https://github.com/variani/lme4qtl. The online version of this article (10.1186/s12859-018-2057-x) contains supplementary material, which is available to authorized users.
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影响因子：
30.8
作者：
Zhou, Xiang;Stephens, Matthew
通讯作者：
Stephens, Matthew
影响因子：
3.7
作者：
Martin-Fernandez L;Ziyatdinov A;Carrasco M;Millon JA;Martinez-Perez A;Vilalta N;Brunel H;Font M;Hamsten A;Souto JC;Soria JM
通讯作者：
Soria JM
影响因子：
5.8
作者：
Ziyatdinov, Andrey;Brunel, Helena;Manuel Soria, Jose
通讯作者：
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作者：
Blangero, John;Diego, Vincent P.;Dyer, Thomas D.;Almeida, Marcio;Peralta, Juan;Kent, Jack W., Jr.;Williams, Jeff T.;Almasy, Laura;Goering, Harald H. H.
通讯作者：
Goering, Harald H. H.
影响因子：
9.8
作者：
Almasy, L;Blangero, J
通讯作者：
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