Q-SCAN: Whole Genome Association Study Data for Signal Region Detection

A method and likely associated data for detecting signal regions in whole genome association studies, developed by Zilin Li of Harvard University. The proposed Q-SCAN statistic scans the genome continuously to detect regions associated with disease outcomes, accounting for genetic variant correlation and allowing for mixed effect directions. The method was evaluated using simulation studies and applied to WGS data from the Atherosclerosis Risk in Communities study.

Use Cases

• Detecting genomic signal regions associated with disease outcomes based on continuous genome scanning described in the paper.
• Benchmarking new region-based association methods against the proposed Q-SCAN statistic framework.
• Analyzing genetic data where signal regions may contain both causal and neutral variants with effects in different directions, as the method is designed for this scenario.

Strengths

• Method accounts for linkage disequilibrium (correlation) among genetic variants, a key factor in genetic analysis.
• Proposed approach allows signal regions to contain both causal and neutral variants, increasing biological realism.
• The method is described as computationally efficient for scanning the genome continuously.

Limitations

• Row count and dataset scale are unknown, which may limit suitability assessment.
• Column-level documentation is absent; field semantics must be inferred after download.
• Last update date is unknown; freshness unverified.

Provenance

Source

paperswithcode, author Zilin Li (Harvard University)

Collection Method

Likely derived from the Atherosclerosis Risk in Communities (ARIC) study whole genome sequencing data, as referenced in the paper's application.