Effects of normalization on quantitative traits in association test
1 Cancer & Stem Cell Biology Program, Duke-National University of Singapore Graduate Medical School, Singapore
2 Department of Statistics and Applied Probability, National University of Singapore, Singapore
BMC Bioinformatics 2009, 10:415 doi:10.1186/1471-2105-10-415Published: 14 December 2009
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.