STEGO

STEGO identifies genetic outliers in whole-genome sequencing (WGS) and genome-wide association study (GWAS) datasets to mitigate confounding from population substructure and cryptic relatedness by leveraging rare variant information.

Key Features:

• Genetic heterogeneity assessment: Uses rare variants to assess recent ancestry and identify individuals who are genetically too similar (indicative of cryptic relationships) or too different (suggestive of population substructure).
• Computational efficiency: Designed for application to large-scale datasets such as whole-genome sequencing without prohibitive computational cost.
• Formal testing framework: Implements a statistical testing framework for systematic assessment and identification of relatedness and substructure within study populations.

Scientific Applications:

• Simulation validation: Validated in simulation studies showing effectiveness in detecting genetic outliers with moderate sample sizes.
• Empirical analysis of reference cohorts: Applied to the 1000 Genomes Project dataset to identify likely related subjects that passed standard quality control filters, demonstrating sensitivity to cryptic relatedness in real-world WGS/GWAS data.

Methodology:

Utilizes rare variant data to capture recent ancestral information for detecting subtle genetic similarities and differences among individuals.