Institute of Cytology and Genetics, Siberian Branch of the Russian Academy of Sciences, Novosibirsk, Russia.
PLoS One. 2013 Jun 17;8(6):e65395. doi: 10.1371/journal.pone.0065395. Print 2013.
Regional-based association analysis instead of individual testing of each SNP was introduced in genome-wide association studies to increase the power of gene mapping, especially for rare genetic variants. For regional association tests, the kernel machine-based regression approach was recently proposed as a more powerful alternative to collapsing-based methods. However, the vast majority of existing algorithms and software for the kernel machine-based regression are applicable only to unrelated samples. In this paper, we present a new method for the kernel machine-based regression association analysis of quantitative traits in samples of related individuals. The method is based on the GRAMMAR+ transformation of phenotypes of related individuals, followed by use of existing kernel machine-based regression software for unrelated samples. We compared the performance of kernel-based association analysis on the material of the Genetic Analysis Workshop 17 family sample and real human data by using our transformation, the original untransformed trait, and environmental residuals. We demonstrated that only the GRAMMAR+ transformation produced type I errors close to the nominal value and that this method had the highest empirical power. The new method can be applied to analysis of related samples by using existing software for kernel-based association analysis developed for unrelated samples.
基于区域的关联分析，而不是对每个 SNP 进行个体测试，被引入全基因组关联研究中，以提高基因定位的能力，特别是对于罕见的遗传变异。对于区域关联测试，最近提出了基于核机器的回归方法作为一种更强大的替代基于合并的方法。然而，绝大多数现有的基于核机器的回归算法和软件仅适用于无关样本。在本文中，我们提出了一种新的方法，用于对相关个体样本中的定量性状进行基于核机器的回归关联分析。该方法基于相关个体表型的 GRAMMAR+转换，然后使用现有的基于无关样本的核机器回归软件。我们通过使用我们的转换、原始未转换的性状和环境残差，比较了基于核的关联分析在遗传分析研讨会 17 家系样本和真实人类数据上的性能。我们证明，只有 GRAMMAR+转换产生的 I 型错误接近名义值，并且该方法具有最高的经验能力。该新方法可以应用于使用为无关样本开发的基于核的关联分析的现有软件对相关样本进行分析。
