Lineage: Visualizing Multivariate Clinical Data in Genealogy Graphs

Abstract

The majority of diseases that are a significant challenge for public and individual heath are caused by a combination of hereditary and environmental factors. In this paper, we introduce Lineage, a novel visual analysis tool, designed to support domain experts that study such multifactorial diseases in the context of genealogies. Incorporating familial relationships between cases can provide insights into shared genomic variants that could be implicated in diseases, but also into shared environmental exposures. We introduce a data and task abstraction and argue that the problem of analyzing such diseases based on genealogical, clinical, and genetic data can be mapped to a multivariate graph visualization problem. Our main contribution is a novel visual representation for tree-like, multivariate graphs, which we apply to genealogies and clinical data about the individuals in these families. We introduce data-driven aggregation methods to scale to multiple families with hundreds of members across several generations. By designing the genealogy graph layout to align with a tabular view that displays clinical data for each family member, we are able to incorporate extensive, multivariate attributes in the analysis of the genealogy without cluttering the graph. We also discuss how the principles of our methodology can be generalized to other scenarios. We validate our designs using an illustrative example based on real-world data, and report of feedback from domain experts.

Citation

Carolina Nobre, Nils Gehlenborg, Hilary Coon, Alexander Lex
Lineage: Visualizing Multivariate Clinical Data in Genealogy Graphs
bioRxiv preprint, doi:10.1101/128579, 2017.

BibTeX

@article{2017_preprint_lineage,
  title = {Lineage: Visualizing Multivariate Clinical Data in Genealogy Graphs},
  author = {Carolina Nobre and Nils Gehlenborg and Hilary Coon and Alexander Lex},
  journal = {bioRxiv preprint},
  doi = {10.1101/128579},
  year = {2017}
}

Acknowledgements

We thank Asmaa Aljuhani and Annie Cherkaev for their contributions. We also thank our collaborators and the Visualization Design Lab at the University of Utah for the feedback, and the Caleydo team for their technical support. This work was supported in part by the US National Institutes of Health (U01 CA198935, R00 HG007583, R01MH099134) and the DoD - Office of Economic Adjustment (OEA), ST1605-16-01. We thank the Pedigree and Population Resource of the Huntsman Cancer Institute, University of Utah (funded in part by the Huntsman Cancer Foundation) for its role in the ongoing collection, maintenance and support of the Utah Population Database (UPDB). We also acknowledge partial support for the UPDB through grant P30 CA2014 from the National Cancer Institute, and from the University of Utah’s Program in Personalized Health and Center for Clinical and Translational Science.

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Lineage Introduction

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