Effects of normalization on quantitative traits in association test.
BACKGROUND
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.


RESULTS
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.


CONCLUSIONS
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.
Goh
Liang
L
Cancer & Stem Cell Biology Program, Duke-National University of Singapore Graduate Medical School, Singapore. liang.goh@duke-nus.edu.sg
Yap
Von Bing
VB
D019295:Computational Biology / Q000379:methods*; D014644:Genetic Variation; D020641:Polymorphism, Single Nucleotide; D040641:Quantitative Trait Loci; D018401:Sample Size