Effects of normalization on quantitative traits in association test

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 simulat...

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Bibliographic Details
Published in:BMC bioinformatics 2009-12, Vol.10 (1), p.415-415, Article 415
Main Authors: Goh, Liang, Yap, Von Bing
Format: Article
Language:English
Subjects:
Computational biology
Computational Biology - methods
Genetic algorithms
Genetic Variation
Polymorphism, Single Nucleotide
Quantitative Trait Loci
Research article
Sample Size
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Summary: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.
ISSN:1471-2105
1471-2105
DOI:10.1186/1471-2105-10-415