Cancer & Stem Cell Biology Program, Duke-National University of Singapore Graduate Medical School, Singapore.
BMC Bioinformatics. 2009 Dec 14;10:415. doi: 10.1186/1471-2105-10-415.
Quantitative trait loci analysis assumes that the trait is normally distributed. In reality, this is often not observed and one strategy is to transform the trait. However, it is not clear how much normality is required and which transformation works best in association studies.
We performed simulations on four types of common quantitative traits to evaluate the effects of normalization using the logarithm, Box-Cox, and rank-based transformations. The impact of sample size and genetic effects on normalization is also investigated. Our results show that rank-based transformation gives generally the best and consistent performance in identifying the causal polymorphism and ranking it highly in association tests, with a slight increase in false positive rate.
For small sample size or genetic effects, the improvement in sensitivity for rank transformation outweighs the slight increase in false positive rate. However, for large sample size and genetic effects, normalization may not be necessary since the increase in sensitivity is relatively modest.
数量性状基因座分析假设性状呈正态分布。但实际上，这种情况并不常见，一种策略是对性状进行转换。然而，在关联研究中，尚不清楚需要多少正态性，哪种转换效果最好。
我们对四种常见的数量性状进行了模拟，以评估对数、Box-Cox 和基于秩的转换对正态化的影响。还研究了样本量和遗传效应对正态化的影响。结果表明，基于秩的转换通常在识别因果多态性和在关联检验中对其进行高度排序方面表现最佳且一致，假阳性率略有增加。
对于小样本量或遗传效应，基于秩的转换的灵敏度提高超过了假阳性率的轻微增加。然而，对于大样本量和遗传效应，由于灵敏度的增加相对较小，因此可能不需要进行归一化。
