Linear Score Tests for Variance Components in Linear Mixed Models and Applications to Genetic Association Studies
Linear Score Tests for Variance Components in Linear Mixed Models and Applications to Genetic Association Studies
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DOI：
10.1111/biom.12095
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发表时间：
2013-12-01
期刊：
影响因子：
1.9
通讯作者：
Marshall, Scott L.
中科院分区：
文献类型：
--
作者：
Qu, Long;Guennel, Tobias;Marshall, Scott L.
随着基因组规模的基因分型技术的快速发展，遗传关联作图已成为检测某些（疾病）表型的基因组区域的流行工具，特别是在样本量有限的早期药物基因组学研究中。响应于这样的应用，良好的关联测试需要（1）适用于广泛范围的可能的遗传模型，包括但不限于基因与环境或基因与基因的相互作用的存在以及一组标记效应的非线性，（2）在小样本中准确，在基因组规模上快速计算，并且适合于大规模多重测试校正，和（3）合理地强大定位致病基因组区域。以线性混合模型为代表的核机器方法通过将问题转化为检验方差分量的零性，提供了一种可行的解决方案。在这项研究中，我们考虑基于分数的测试，通过选择一个统计线性的分数函数。当原假设下的模型只有一个误差方差参数时，我们的检验在有限样本下是精确的。当零模型有一个以上的方差参数，我们开发了一个新的基于矩的近似，在模拟中表现良好。通过对真实的数据的模拟和分析，我们证明了新的检验具有上述大部分特征，特别是与现有的二次得分检验或限制似然比检验相比。
Following the rapid development of genome-scale genotyping technologies, genetic association mapping has become a popular tool to detect genomic regions responsible for certain (disease) phenotypes, especially in early-phase pharmacogenomic studies with limited sample size. In response to such applications, a good association test needs to be (1)applicable to a wide range of possible genetic models, including, but not limited to, the presence of gene-by-environment or gene-by-gene interactions and non-linearity of a group of marker effects, (2)accurate in small samples, fast to compute on the genomic scale, and amenable to large scale multiple testing corrections, and (3)reasonably powerful to locate causal genomic regions. The kernel machine method represented in linear mixed models provides a viable solution by transforming the problem into testing the nullity of variance components. In this study, we consider score-based tests by choosing a statistic linear in the score function. When the model under the null hypothesis has only one error variance parameter, our test is exact in finite samples. When the null model has more than one variance parameter, we develop a new moment-based approximation that performs well in simulations. Through simulations and analysis of real data, we demonstrate that the new test possesses most of the aforementioned characteristics, especially when compared to existing quadratic score tests or restricted likelihood ratio tests.