wdCor

wdCor applies weighted distance correlation to assess associations between genetic variants and voxel-based brain imaging phenotypes in genome-wide association studies (GWAS), enabling multivariate analysis that preserves spatial continuity of imaging data.

Key Features:

• Multivariate Phenotype Analysis: wdCor treats voxel-based imaging data as a multivariate phenotype, preserving spatial continuity across voxels to increase association power.
• Weighted Distance Correlation: The core method extends distance correlation by incorporating weights that account for spatial relationships among voxels to capture complex dependencies.
• Adaptive Permutation Procedure: Statistical significance (P-values) is determined via an adaptive permutation procedure to provide robust inference while reducing computational demands.
• Enhanced Performance: Simulation studies reported superior performance compared with original distance correlation methods for high-dimensional voxel-based imaging data.

Scientific Applications:

• Imaging genetics association studies: Applied in GWAS to identify associations between genetic markers and voxel-based brain imaging phenotypes.
• Investigation of neuropsychiatric risk genes: Used to study genetic underpinnings of variations in brain structure and function relevant to neuropsychiatric disorders.
• Large-scale dataset analyses: Applicable to large-scale imaging genetics datasets, including the Alzheimer's Disease Neuroimaging Initiative (ADNI).

Methodology:

wdCor computes weighted distance correlation between genetic data and voxel-based imaging phenotypes and assesses significance using an adaptive permutation procedure.