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Some pitfalls in application of functional data analysis approach to association studies
G R Svishcheva1,2, N M Belonogova1, T I Axenovich1,3
1Institute of Cytology and Genetics, Siberian Branch of the Russian Academy of Sciences, Novosibirsk, Russia.
Scientific Reports
|April 5, 2016
Summary
The beta-smooth only model in functional data analysis can be analytically equivalent to the full functional linear model. However, differences in basis functions can lead to misinterpretation and reduced statistical power in gene-based mapping.
Area of Science:
- Genetics
- Statistical Genetics
- Bioinformatics
Background:
- Functional data analysis is key for gene-based mapping, utilizing basis functions for data smoothing.
- Two models exist: the full functional linear model and the beta-smooth only model, differing in how genotype effects are smoothed.
Purpose of the Study:
- To analytically compare the full and beta-smooth only functional models.
- To elucidate the benefits and limitations of the beta-smooth only model in gene-based mapping.
Main Methods:
- Analytical comparison of the full and beta-smooth only models under diverse scenarios.
- Evaluation of model behavior based on the type and number of basis functions used.
Main Results:
- When basis functions are equal in type and number, the full model simplifies to the beta-smooth only model.
- Discrepancies in basis function type redefine genotype smoothing, potentially causing misinterpretation and power loss.
- Unequal numbers of basis functions prevent direct analytical comparison and can disadvantage the full model.
Conclusions:
- The choice and configuration of basis functions critically impact functional model performance in gene-based mapping.
- Careful consideration of basis function properties is essential to avoid misinterpretation and maintain statistical power.
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