KBAT

KBAT performs kernel-based association testing to detect genetic associations in genome-wide and candidate-region studies by combining single-locus P-values with kernel weights that reflect intermarker distances and linkage disequilibrium.

Key Features:

• Composite test: Combines P-values from single-locus association tests with kernel weights linked to intermarker distances and linkage disequilibrium.
• Generalized test statistic: Implements a generalized form of existing test statistics to provide a flexible analytical framework.
• Robustness and invariance: Maintains performance in the presence of nuisance markers and is invariant to the map scale used.
• Chromosome-wide scanning: Supports a moving-average procedure to facilitate chromosome-wide analysis of association signals.

Scientific Applications:

• Genome-wide scans: Applied for genome-wide association mapping using moving-average chromosome scans to locate regions of interest.
• Candidate-region mapping and positional cloning: Used in candidate-region analyses to support positional cloning of disease genes.
• Simulation-based evaluation: Evaluated across evolutionary parameters, disease models, sample sizes, kernel functions, test statistics, window attributes, and genetic/physical maps.
• Real-data analysis: Applied to the Collaborative Study on the Genetics of Alcoholism to identify genes associated with alcohol dependence.

Methodology:

Combines P-values from single-locus association tests with kernel weights based on intermarker distance and linkage disequilibrium and applies a moving-average chromosome-scanning procedure.