VikNGS

VikNGS integrates next-generation sequencing (NGS) datasets across multiple studies to enable combined rare and common variant genetic association testing while accounting for differential genotype uncertainty and increasing effective sample size.

Key Features:

• C++ implementation and cross-platform support: Implemented in C++ with cross-platform functionality for deployment across computing environments.
• Dataset integration: Integrates NGS datasets from multiple studies to increase sample size and reduce the need for sequencing controls.
• Genetic association analysis: Performs association testing for both rare and common variants and supports quantitative and binary traits.
• Covariate adjustment: Provides mechanisms to adjust for covariates in association analyses.
• Genotype uncertainty handling: Accounts for differential genotype uncertainty across studies to prevent spurious associations.
• Power simulation: Includes power simulation functionality to estimate statistical power and inform study design.
• Data visualization: Provides data visualization features to assist interpretation and communication of results.

Scientific Applications:

• Enhanced association testing: Combining cohorts to increase statistical power for rare and common variant association tests of quantitative and binary traits.
• Meta-analysis and collaboration: Supporting large-scale meta-analyses and collaborative projects across multiple cohorts.
• Integration across heterogeneous data: Enabling integration of datasets with differing genotype uncertainty to maintain result validity and avoid spurious associations.

Methodology:

VikNGS employs algorithms for NGS data integration that account for genotype uncertainty, supports covariate adjustment in association analyses, and implements power simulation.