Visual exploration of genetic likelihood space

  • Authors:

  • Juw Won Park;James F. Cremer;Alberto M. Segre

  • Affiliations:

  • The University of Iowa, Iowa City, IA;The University of Iowa, Iowa City, IA;The University of Iowa, Iowa City, IA

  • Venue:
  • Proceedings of the 2006 ACM symposium on Applied computing
  • Year:
  • 2006

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Abstract

Linkage analysis is used to localize human disease genes on the genome and it can involve the exploration and interpretation of a seven-dimensional genetic likelihood space. Existing genetic likelihood exploration techniques are quite cumbersome and slow, and do not help provide insight into the shape and features of the high-dimensional likelihood surface. The objective of our visualization is to provide an efficient visual exploration of the complex genetic likelihood space so that researchers can assimilate more information in the least possible time. In this paper, we present new visualization tools for interactive and efficient exploration of the multi-dimensional likelihood space. Our tools provide interactive manipulation of active ranges of the six model parameters determining the dependent variable, scaled genetic likelihood, or HLOD. Using filtering, color, and an approach inspired by "worlds-within-worlds" [5, 6], researchers can quickly obtain a more informative and insightful visual interpretation of the space.