lrgpr：用于基因组关联研究的交互式线性混合模型分析，具有综合假设检验和 R 中的回归诊断。
lrgpr: interactive linear mixed model analysis of genome-wide association studies with composite hypothesis testing and regression diagnostics in R.
机构信息
Department of Genetics and Genomic Sciences, Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, USA, Department of Biological Statistics and Computational Biology, Cornell University, Ithaca, USA and Department of Genetic Medicine, Weill Cornell Medical College, New York, NY, USA Department of Genetics and Genomic Sciences, Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, USA, Department of Biological Statistics and Computational Biology, Cornell University, Ithaca, USA and Department of Genetic Medicine, Weill Cornell Medical College, New York, NY, USA Department of Genetics and Genomic Sciences, Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, USA, Department of Biological Statistics and Computational Biology, Cornell University, Ithaca, USA and Department of Genetic Medicine, Weill Cornell Medical College, New York, NY, USA.
Department of Genetics and Genomic Sciences, Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, USA, Department of Biological Statistics and Computational Biology, Cornell University, Ithaca, USA and Department of Genetic Medicine, Weill Cornell Medical College, New York, NY, USA Department of Genetics and Genomic Sciences, Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, USA, Department of Biological Statistics and Computational Biology, Cornell University, Ithaca, USA and Department of Genetic Medicine, Weill Cornell Medical College, New York, NY, USA.
出版信息
Bioinformatics. 2014 Nov 1;30(21):3134-5. doi: 10.1093/bioinformatics/btu435. Epub 2014 Jul 16.
UNLABELLED
The linear mixed model is the state-of-the-art method to account for the confounding effects of kinship and population structure in genome-wide association studies (GWAS). Current implementations test the effect of one or more genetic markers while including prespecified covariates such as sex. Here we develop an efficient implementation of the linear mixed model that allows composite hypothesis tests to consider genotype interactions with variables such as other genotypes, environment, sex or ancestry. Our R package, lrgpr, allows interactive model fitting and examination of regression diagnostics to facilitate exploratory data analysis in the context of the linear mixed model. By leveraging parallel and out-of-core computing for datasets too large to fit in main memory, lrgpr is applicable to large GWAS datasets and next-generation sequencing data.
AVAILABILITY AND IMPLEMENTATION
lrgpr is an R package available from lrgpr.r-forge.r-project.org.
未加标签
线性混合模型是全基因组关联研究（GWAS）中用于解释亲缘关系和群体结构混杂效应的最先进方法。目前的实现方法测试一个或多个遗传标记的效果，同时包括性别等预设协变量。在这里，我们开发了一种有效的线性混合模型实现方法，允许复合假设检验考虑基因型与其他基因型、环境、性别或祖源等变量的相互作用。我们的 R 包 lrgpr 允许交互式模型拟合和回归诊断检查，以方便线性混合模型背景下的探索性数据分析。通过利用并行和外存计算，对于无法装入主存的大型数据集，lrgpr 适用于大型 GWAS 数据集和下一代测序数据。
可用性和实现
lrgpr 是一个 R 包，可从 lrgpr.r-forge.r-project.org 获取。
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