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Defining window-boundaries for genomic analyses using smoothing spline techniques [electronic resource].

Published
Washington, D.C. : United States. Dept. of Energy. Office of Science, 2015.
Oak Ridge, Tenn. : Distributed by the Office of Scientific and Technical Information, U.S. Dept. of Energy
Physical Description
Article numbers 30 : digital, PDF file
Additional Creators
University of Wisconsin--Madison, United States. Department of Energy. Office of Science, and United States. Department of Energy. Office of Scientific and Technical Information
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Summary
High-density genomic data is often analyzed by combining information over windows of adjacent markers. Interpretation of data grouped in windows versus at individual locations may increase statistical power, simplify computation, reduce sampling noise, and reduce the total number of tests performed. However, use of adjacent marker information can result in over- or under-smoothing, undesirable window boundary specifications, or highly correlated test statistics. We introduce a method for defining windows based on statistically guided breakpoints in the data, as a foundation for the analysis of multiple adjacent data points. This method involves first fitting a cubic smoothing spline to the data and then identifying the inflection points of the fitted spline, which serve as the boundaries of adjacent windows. This technique does not require prior knowledge of linkage disequilibrium, and therefore can be applied to data collected from individual or pooled sequencing experiments. Moreover, in contrast to existing methods, an arbitrary choice of window size is not necessary, since these are determined empirically and allowed to vary along the genome.
Report Numbers
E 1.99:1184786
Subject(s)
Note
Published through SciTech Connect.
04/17/2015.
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Genetics Selection Evolution (Online) 47 1 ISSN 1297-9686 AM
Beissinger, Timothy; Rosa, Guilherme; Kaeppler, Shawn; Gianola, Daniel; de Leon, Natalia.
Funding Information
FC02-07ER64494
View MARC record | catkey: 23498455