rvGWAS

rvGWAS performs rare variant association analyses to identify and prioritize rare genetic variants associated with complex diseases by integrating individual- or variant-specific covariates and full-model Bayesian estimation using BATI (Bayesian rare variant association test using Integrated Nested Laplace Approximation).

Key Features:

• Bayesian framework: BATI employs a Bayesian approach using Integrated Nested Laplace Approximation to estimate posterior distributions of model parameters and enable full-model estimation.
• Covariate integration: Supports incorporation of individual- or variant-specific features and functional annotations as covariates in association tests.
• Enhanced statistical power: Demonstrates greater than 75% power in scenarios where risk variants collectively explain less than 0.5% of phenotypic variance, suitable for small to medium-sized cohorts.
• Flexible aggregation units: Allows testing on biological units such as genes or promoters.
• Comprehensive framework integration: Integrates BATI with five other established rare variant association study (RVAS) tests for comparative analysis.
• Sequencing data support: Designed for analysis of whole-exome sequencing and whole-genome sequencing data.
• Quality control and filtering: Includes quality control and filtering steps for sequence data prior to association testing.

Scientific Applications:

• Complex disease genetics: Applied to studies of complex diseases by focusing on rare variants (minor allele frequency <1%) that may contribute to disease susceptibility beyond common variant signals.
• Chronic Lymphocytic Leukemia: Used to identify candidate predisposition genes in Chronic Lymphocytic Leukemia, including eight candidate genes such as EHMT2 and COPS7A.

Methodology:

Uses the BATI Bayesian rare variant association test with Integrated Nested Laplace Approximation to estimate posterior distributions, integrates individual- or variant-specific covariates and functional annotations, combines BATI with five other RVAS tests, and operates on whole-exome and whole-genome sequencing data with quality control and filtering.