Aulchenko Yurii S, de Koning Dirk-Jan, Haley Chris
Department of Epidemiology and Biostatistics, Erasmus MC, 3000 CA Rotterdam, The Netherlands.
Genetics. 2007 Sep;177(1):577-85. doi: 10.1534/genetics.107.075614. Epub 2007 Jul 29.
For pedigree-based quantitative trait loci (QTL) association analysis, a range of methods utilizing within-family variation such as transmission-disequilibrium test (TDT)-based methods have been developed. In scenarios where stratification is not a concern, methods exploiting between-family variation in addition to within-family variation, such as the measured genotype (MG) approach, have greater power. Application of MG methods can be computationally demanding (especially for large pedigrees), making genomewide scans practically infeasible. Here we suggest a novel approach for genomewide pedigree-based quantitative trait loci (QTL) association analysis: genomewide rapid association using mixed model and regression (GRAMMAR). The method first obtains residuals adjusted for family effects and subsequently analyzes the association between these residuals and genetic polymorphisms using rapid least-squares methods. At the final step, the selected polymorphisms may be followed up with the full measured genotype (MG) analysis. In a simulation study, we compared type 1 error, power, and operational characteristics of the proposed method with those of MG and TDT-based approaches. For moderately heritable (30%) traits in human pedigrees the power of the GRAMMAR and the MG approaches is similar and is much higher than that of TDT-based approaches. When using tabulated thresholds, the proposed method is less powerful than MG for very high heritabilities and pedigrees including large sibships like those observed in livestock pedigrees. However, there is little or no difference in empirical power of MG and the proposed method. In any scenario, GRAMMAR is much faster than MG and enables rapid analysis of hundreds of thousands of markers.
对于基于家系的数量性状基因座（QTL）关联分析，已经开发了一系列利用家系内变异的方法，例如基于传递不平衡检验（TDT）的方法。在分层不是问题的情况下，除了家系内变异外还利用家系间变异的方法，例如测量基因型（MG）方法，具有更高的效能。MG方法的应用在计算上要求较高（特别是对于大型家系），使得全基因组扫描实际上不可行。在此，我们提出一种基于家系的全基因组数量性状基因座（QTL）关联分析的新方法：使用混合模型和回归的全基因组快速关联（GRAMMAR）。该方法首先获得针对家系效应进行调整的残差，随后使用快速最小二乘法分析这些残差与基因多态性之间的关联。在最后一步，可以对所选的多态性进行完整的测量基因型（MG）分析。在一项模拟研究中，我们将所提出方法的I型错误、效能和操作特性与MG方法和基于TDT的方法进行了比较。对于人类家系中中度遗传力（30%）的性状，GRAMMAR方法和MG方法的效能相似，并且远高于基于TDT的方法。当使用列表阈值时，对于非常高的遗传力以及包含大型同胞组的家系（如在牲畜家系中观察到的那样），所提出的方法比MG方法的效能低。然而，MG方法和所提出方法的经验效能几乎没有差异。在任何情况下，GRAMMAR都比MG快得多，并且能够快速分析数十万标记。
