RegionScan: a comprehensive R package for region-level genome-wide association testing with integration and visualization of multiple-variant and single-variant hypothesis testing
Myriam Brossard, Delnaz Roshandel, Kexin Luo, Fatemeh Yavartanoo, Andrew D Paterson, Yun J Yoo, Shelley B Bull

TL;DR
RegionScan is an R package that enables genome-wide association testing at the region level, integrating and visualizing multiple and single-variant hypothesis tests.
Contribution
RegionScan introduces a comprehensive framework for region-level genome-wide association testing with multiple-variant and single-variant methods.
Findings
RegionScan implements three state-of-the-art region-level tests for association detection.
The package supports analysis of multi-allelic variants and unbalanced binary phenotypes.
RegionScan provides detailed outputs and utilities for result comparison and visualization.
Abstract
RegionScan is designed for scalable genome-wide association testing of both multiple-variant and single-variant region-level statistics, with visualization of the results. For detection of association under various regional architectures, it implements three classes of state-of-the-art region-level tests, including multiple-variant linear/logistic regression (with and without dimension reduction), a variance-component score test, and region-level minP tests. RegionScan also supports the analysis of multi-allelic variants and unbalanced binary phenotypes and is compatible with widely used variant call format (VCF) files for both genotyped and imputed variants. Association testing leverages linkage disequilibrium (LD) structure in pre-defined regions, for example, LD-adaptive regions obtained by genomic partitioning, and accommodates parallel processing to improve computational and memory…
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Taxonomy
TopicsGenetic Associations and Epidemiology · Gene expression and cancer classification · Genetic Syndromes and Imprinting
