WITHDRAWN WaveKAT-F: A Genotype-Informed Wavelet-Fourier Transformation for Rare Variant Association Testing

Music City Center 

Rare variant association testing presents challenges due to sparcity and the complex nature of genetic non-linear interactions. SKAT and similar methods struggle to fully capture multi-scale patterns in these type genetic variations. We introduce WaveKAT-F, a novel wavelet-Fourier framework that applies custom-designed wavelet transformation with adaptive weighting and a specialized kernel tailored for genetic signal processing to enhance rare variant detection. By transforming genotype data into a multi-resolution representation, WaveKAT-F improves signal extraction while preserving both localized and global effects. Simulations and real-world datasets demonstrate that WaveKAT-F achieves higher power than existing methods, particularly in scenarios with mixed effect directions with weak association and low-frequency variants, while maintaining well-controlled Type I error rates. By integrating custom wavelet transforms, Fourier-based spectral analysis, and a unique kernel, WaveKAT-F provides a robust and flexible approach for identifying rare variant associations.

Rare Variant Association Testing

Custom Wavelet Transforms

Fourier Methods

Spectral Methods

Multi-Resolution Signal Processing

Genetic Association Studies 

Section on Statistics in Genomics and Genetics