Multikernel linear mixed models for complex phenotype prediction.
Multikernel linear mixed models for complex phenotype prediction.
复制标题
用于复杂表型预测的多级线性混合模型。
DOI：
10.1101/gr.201996.115
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发表时间：
2016-07
期刊：
影响因子：
7
通讯作者：
Rosset S
中科院分区：
文献类型：
--
作者：
Weissbrod O;Geiger D;Rosset S
线性混合模型（LMM）及其扩展最近已成为复杂性状表型预测的首选方法。然而，迄今为止，LMM 的使用通常受到假设简单遗传结构的限制。在这里，我们提出了多内核线性混合模型 (MKLMM)，这是一种使用多内核机器学习方法扩展标准 LMM 的预测建模框架。 MKLMM 可以对遗传相互作用进行建模，特别适合对附近变异之间复杂的局部相互作用进行建模。我们还提出了 MKLMM-Adapt，它可以自动推断多个基因组区域之间的相互作用类型。在对来自 Wellcome Trust 病例对照联盟的八个病例对照数据集和一百多个小鼠表型的分析中，MKLMM-Adapt 在表型预测方面始终优于竞争方法。 MKLMM 的计算效率与标准 LMM 一样，并且不需要存储基因型，从而在不影响计算可行性或基因组隐私的情况下实现最先进的预测能力。
Linear mixed models (LMMs) and their extensions have recently become the method of choice in phenotype prediction for complex traits. However, LMM use to date has typically been limited by assuming simple genetic architectures. Here, we present multikernel linear mixed model (MKLMM), a predictive modeling framework that extends the standard LMM using multiple-kernel machine learning approaches. MKLMM can model genetic interactions and is particularly suitable for modeling complex local interactions between nearby variants. We additionally present MKLMM-Adapt, which automatically infers interaction types across multiple genomic regions. In an analysis of eight case-control data sets from the Wellcome Trust Case Control Consortium and more than a hundred mouse phenotypes, MKLMM-Adapt consistently outperforms competing methods in phenotype prediction. MKLMM is as computationally efficient as standard LMMs and does not require storage of genotypes, thus achieving state-of-the-art predictive power without compromising computational feasibility or genomic privacy.