KinVis: a visualization tool to detect cryptic relatedness in genetic datasets.

Area of Science:

• Genetics
• Bioinformatics
• Computational Biology

Background:

• Accurate characterization of individual relatedness is essential for genome-wide association studies (GWAS).
• Understanding familial relationships and population structure is vital for downstream genetic analyses.
• Cohorts used as reference panels require robust relatedness information for association tests and ethnic diversity identification.

Purpose of the Study:

• To develop and present a novel kinship visualization tool, KinVis.
• To facilitate the detection of cryptic relatedness among subjects in genetic studies.
• To enhance the analysis of relatedness information for improved genetic research.

Main Methods:

• Utilized multi-dimensional scaling for data reduction and visualization.
• Employed bar charts, heat maps, and node-link diagrams to represent relatedness.
• Developed an interactive visualization tool for exploring kinship data.

Main Results:

• Demonstrated the utility of KinVis in detecting cryptic relatedness.
• Provided multiple visualization methods for comprehensive relatedness analysis.
• Enabled efficient exploration of familial relationships and population structures.

Conclusions:

• KinVis offers a valuable approach for characterizing individual relatedness in genetic cohorts.
• The tool supports accurate downstream analysis in genome-wide association studies.
• KinVis aids in identifying ethnic diversities and strengthens the utility of reference panels.