Pei Yu-Fang, Zhang Lei, Liu Jianfeng, Deng Hong-Wen
The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Molecular Genetics, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, P. R. China.
Ann Hum Genet. 2009 Jul;73(Pt 4):456-64. doi: 10.1111/j.1469-1809.2009.00527.x. Epub 2009 Jun 1.
Genetic association analyses with haplotypes may be more powerful than analyses with single markers, under certain conditions. Furthermore, simultaneously considering multiple correlated traits may make use of additional information that would not be considered when analyzing individual traits. In this study, we propose a haplotype based test of association for multivariate quantitative traits in unrelated samples. Specifically, we extend a population based haplotype trend regression (HTR) approach to multivariate scenarios. We mainly focused on bivariate HTR, and the simulation results showed that the proposed method had correct pre-specified type-I error rates. The power of the proposed method was largely influenced by the size and source of correlation between variables, being greatest when correlation of a specific gene was opposite in sign to the residual correlation.
在某些条件下，对单倍型进行基因关联分析可能比单标记分析更有效。此外，同时考虑多个相关性状可能会利用到分析单个性状时未被考虑的额外信息。在本研究中，我们提出了一种针对非亲缘样本中多变量定量性状的基于单倍型的关联检验方法。具体而言，我们将基于群体的单倍型趋势回归（HTR）方法扩展到多变量情形。我们主要关注双变量HTR，模拟结果表明所提出的方法具有正确的预先设定的I型错误率。所提出方法的效能在很大程度上受变量间相关性的大小和来源影响，当特定基因的相关性与残差相关性符号相反时效能最大。
