MTG2: an efficient algorithm for multivariate linear mixed model analysis based on genomic information.
MTG2: an efficient algorithm for multivariate linear mixed model analysis based on genomic information.
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DOI：
10.1093/bioinformatics/btw012
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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：
van der Werf JH
中科院分区：
文献类型：
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作者：
Lee SH;van der Werf JH
摘要：我们开发了一种算法，用于在线性混合模型框架中使用全基因组snp对复杂性状进行遗传分析。与目前基于混合模型方程的标准REML软件相比，我们的方法要快得多。当只有单一遗传协方差结构时，优势最大。该方法特别适用于多变量分析，包括研究反应规范的多特征模型和随机回归模型。我们将提出的方法应用于公开的小鼠和人类数据，并讨论了其优点和局限性。可用性和实现：MTG2可从https://sites.google.com/site/honglee0707/mtg2获得。补充信息：补充数据可在Bioinformatics在线获取。
Summary: We have developed an algorithm for genetic analysis of complex traits using genome-wide SNPs in a linear mixed model framework. Compared to current standard REML software based on the mixed model equation, our method is substantially faster. The advantage is largest when there is only a single genetic covariance structure. The method is particularly useful for multivariate analysis, including multi-trait models and random regression models for studying reaction norms. We applied our proposed method to publicly available mice and human data and discuss the advantages and limitations. Availability and implementation: MTG2 is available in https://sites.google.com/site/honglee0707/mtg2. Contact: hong.lee@une.edu.au Supplementary information: Supplementary data are available at Bioinformatics online.