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Normalizing a large number of quantitative traits using empirical normal quantile transformation.
Bo Peng1, Robert K Yu, Kevin L Dehoff
1Department of Epidemiology, The University of Texas, M.D. Anderson Cancer Center, 1155 Pressler Boulevard, Unit 1340, Houston, Texas 77030, USA. bpeng@mdanderson.org
BMC Proceedings
|May 10, 2008
Summary
Empirical normal quantile transformation effectively normalizes trait data for quantitative trait loci mapping. This method maintains good statistical power and type I error control, even for large datasets with numerous traits.
Area of Science:
- Genetics
- Statistical Genetics
- Bioinformatics
Background:
- Quantitative trait loci (QTL) mapping commonly uses variance-components and regression methods.
- Normality assumption violation in trait values can negatively impact QTL analysis power and type I error.
- Manual trait transformation is impractical for large-scale genetic studies with many traits.
Purpose of the Study:
- To introduce and evaluate the empirical normal quantile transformation for normalizing trait data in QTL analysis.
- To assess the performance of this transformation against established methods, particularly for large datasets.
Main Methods:
- Proposed empirical normal quantile transformation using inverse normal transformation on scaled ranks.
- Compared transformation performance via extensive simulations.
- Applied variance-components and variance-regression methods to Genetic Analysis Workshop 15 (GAW15) expression data before and after transformation.
Main Results:
- The empirical normal quantile transformation demonstrated good control of power and type I error in simulations.
- Performance was comparable to computationally intensive semiparametric methods.
- Analysis of GAW15 data showed the impact of transformation on variance-components and variance-regression results.
Conclusions:
- Empirical normal quantile transformation is a simple yet effective method for normalizing trait data in QTL analysis.
- It offers robust control over statistical power and type I error, suitable for large, complex datasets.
- The transformation provides a viable alternative to more complex methods, enhancing the analysis of genetic data.
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