Effects of normalization on quantitative traits in association test.

ABSTRACT:

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.

SUBMITTER: Goh L 

PROVIDER: S-EPMC2800123 | biostudies-literature | 2009 Dec

REPOSITORIES: biostudies-literature