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Activity Number:
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457
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Type:
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Topic Contributed
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Date/Time:
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Wednesday, August 9, 2006 : 2:00 PM to 3:50 PM
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Sponsor:
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Section on Statistical Graphics
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| Abstract - #306143 |
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Title:
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Visualization of Features in Curve Estimates and Application to Genetic Loci Mapping
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Author(s):
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Myung Hee Lee*+ and Ivan Rusyn and David Threadgill and J. Stephen Marron
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Companies:
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The University of North Carolina at Chapel Hill and The University of North Carolina at Chapel Hill and The University of North Carolina at Chapel Hill and The University of North Carolina at Chapel Hill
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Address:
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210 Smith Building, CB 3260, Chapel Hill, NC, 27599,
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Keywords:
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visualization ; nonparametric smoothing ; scale space ; genetic loci mapping
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Abstract:
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Statistical smoothing methods are useful tools for exploratory data analysis. SiZer (based on studying statistical SIgnificance of ZERo crossings of smoothed estimates) is a visualization tool that provides insight as to whether the observed features (e.g., peaks and valleys) in a curve estimate are statistically significant. In this work, to study the genetic association with quantitative traits, we utilize a dense genotyping data obtained across a large panel of inbred mouse strains. As an exploratory analysis tool, SiZer enables us to provide insight into the density of the genetic markers on the chromosomes map genetic loci associated with a specific continuous phenotype measured in inbred mice.
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