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    This study introduces Lineage, a visual tool for analyzing multifactorial diseases by integrating genetic and environmental data within family histories. It aids experts in understanding disease origins through novel graph visualizations.

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    Area of Science:

    • Genomics
    • Epidemiology
    • Bioinformatics
    • Human Genetics

    Background:

    • Many significant public health diseases stem from complex interactions between hereditary and environmental factors.
    • Analyzing these multifactorial diseases requires integrating diverse data types, including familial relationships, clinical information, and genetic data.

    Purpose of the Study:

    • To introduce Lineage, a novel visual analysis tool designed for domain experts studying multifactorial diseases within genealogical contexts.
    • To address the challenge of visualizing and analyzing complex, multivariate data related to hereditary and environmental disease factors.

    Main Methods:

    • Developed a data and task abstraction, mapping disease analysis to a multivariate graph visualization problem.
    • Created a novel visual representation for tree-like, multivariate graphs, applied to genealogies and clinical data.
    • Implemented data-driven aggregation methods for scalability across multiple families.
    • Designed a genealogy graph layout aligned with a tabular view to incorporate extensive attributes without clutter.

    Main Results:

    • The Lineage tool enables the integration of familial relationships with genomic and environmental data for disease analysis.
    • A novel visualization approach effectively represents multivariate attributes within genealogical structures.
    • Data-driven aggregation and aligned tabular views enhance the scalability and usability of the analysis.

    Conclusions:

    • Lineage provides a powerful new approach for domain experts to investigate multifactorial diseases.
    • The visual analysis tool facilitates deeper insights into the interplay of genetic and environmental factors in disease etiology.
    • Case studies with domain collaborators validated the tool's effectiveness and utility.