KinVis

KinVis visualizes and detects cryptic relatedness within genetic datasets to support accurate population structure analysis and genome-wide association studies (GWAS).

Key Features:

• Multi-dimensional visualization: Implements multi-dimensional scaling (MDS) and visual outputs such as bar charts, heat maps, and node-link visualizations to represent relationships between individuals.
• Cryptic relatedness detection: Identifies hidden or non-obvious familial connections within cohorts that can confound association analyses.
• Population structure characterization: Characterizes individual relatedness in the context of familial relationships and underlying population structure.
• Reference panel validation: Detects relatedness within cohorts used as reference panels to ensure panels reflect intended population diversity and independence.

Scientific Applications:

• Genome-wide association studies (GWAS): Improves reliability of GWAS by identifying and accounting for cryptic relatedness among study participants.
• Ethnic diversity identification: Aids identification of ethnic and population substructure within genetic datasets.
• Reference panel development: Supports validation and refinement of reference panels used in association tests by revealing hidden relatedness.

Methodology:

Uses multi-dimensional scaling (MDS) for dimensionality reduction and produces bar charts, heat maps, and node-link network visualizations to represent relatedness.