Yang Runqing, Wang Xin, Li Jian, Deng Hongwen
School of Agriculture and Biology, Shanghai Jiaotong University, Shanghai, PR China.
Bioinformatics. 2009 Apr 15;25(8):1033-9. doi: 10.1093/bioinformatics/btn558. Epub 2008 Oct 30.
In most quantitative trait locus (QTL) mapping studies, phenotypes are assumed to follow normal distributions. Deviations from this assumption may affect the accuracy of QTL detection and lead to detection of spurious QTLs. To improve the robustness of QTL mapping methods, we replaced the normal distribution for residuals in multiple interacting QTL models with the normal/independent distributions that are a class of symmetric and long-tailed distributions and are able to accommodate residual outliers. Subsequently, we developed a Bayesian robust analysis strategy for dissecting genetic architecture of quantitative traits and for mapping genome-wide interacting QTLs in line crosses.
Through computer simulations, we showed that our strategy had a similar power for QTL detection compared with traditional methods assuming normal-distributed traits, but had a substantially increased power for non-normal phenotypes. When this strategy was applied to a group of traits associated with physical/chemical characteristics and quality in rice, more main and epistatic QTLs were detected than traditional Bayesian model analyses under the normal assumption.
在大多数数量性状基因座(QTL)定位研究中,假定表型服从正态分布。偏离这一假设可能会影响QTL检测的准确性,并导致检测到虚假的QTL。为提高QTL定位方法的稳健性,我们将多个相互作用QTL模型中残差的正态分布替换为正态/独立分布,这是一类对称且长尾的分布,能够容纳残差异常值。随后,我们开发了一种贝叶斯稳健分析策略,用于剖析数量性状的遗传结构,并在回交中定位全基因组相互作用的QTL。
通过计算机模拟,我们表明,与假设性状服从正态分布的传统方法相比,我们的策略在QTL检测方面具有相似的功效,但对于非正态表型,其功效显著提高。当将该策略应用于一组与水稻物理/化学特性及品质相关的性状时,与正态假设下的传统贝叶斯模型分析相比,检测到了更多的主效和上位性QTL。