Defining window-boundaries for genomic analyses using smoothing spline techniques
Abstract
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.
- Authors:
-
- Univ. of California, Davis, CA (United States). Dept. Plant Sciences.
- Univ. of Wisconsin, Madison, WI (United States). Dept. of Animal Sciences and Dept. of Biostatistics and Medical Information.
- Univ. of Wisconsin, Madison, WI (United States). Dept. of Agronomy and Dept. of Energy Great Lakes Bioenergy Research Center.
- Univ. of Wisconsin, Madison, WI (United States). Dept. of Animal Sciences, Dept. of Biostatistics and Medical Information and Dept. of Dairy Science.
- Publication Date:
- Research Org.:
- Univ. of Wisconsin, Madison, WI (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC), Biological and Environmental Research (BER)
- OSTI Identifier:
- 1184786
- Grant/Contract Number:
- FC02-07ER64494
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Genetics Selection Evolution (Online)
- Additional Journal Information:
- Journal Name: Genetics Selection Evolution (Online); Journal Volume: 47; Journal Issue: 1; Journal ID: ISSN 1297-9686
- Publisher:
- BioMed Central
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 59 BASIC BIOLOGICAL SCIENCES
Citation Formats
Beissinger, Timothy M., Rosa, Guilherme J.M., Kaeppler, Shawn M., Gianola, Daniel, and de Leon, Natalia. Defining window-boundaries for genomic analyses using smoothing spline techniques. United States: N. p., 2015.
Web. doi:10.1186/s12711-015-0105-9.
Beissinger, Timothy M., Rosa, Guilherme J.M., Kaeppler, Shawn M., Gianola, Daniel, & de Leon, Natalia. Defining window-boundaries for genomic analyses using smoothing spline techniques. United States. https://doi.org/10.1186/s12711-015-0105-9
Beissinger, Timothy M., Rosa, Guilherme J.M., Kaeppler, Shawn M., Gianola, Daniel, and de Leon, Natalia. Fri .
"Defining window-boundaries for genomic analyses using smoothing spline techniques". United States. https://doi.org/10.1186/s12711-015-0105-9. https://www.osti.gov/servlets/purl/1184786.
@article{osti_1184786,
title = {Defining window-boundaries for genomic analyses using smoothing spline techniques},
author = {Beissinger, Timothy M. and Rosa, Guilherme J.M. and Kaeppler, Shawn M. and Gianola, Daniel and de Leon, Natalia},
abstractNote = {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.},
doi = {10.1186/s12711-015-0105-9},
journal = {Genetics Selection Evolution (Online)},
number = 1,
volume = 47,
place = {United States},
year = {Fri Apr 17 00:00:00 EDT 2015},
month = {Fri Apr 17 00:00:00 EDT 2015}
}
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Genome-wide association study of endo-parasite phenotypes using imputed whole-genome sequence data in dairy and beef cattle
journal, April 2019
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Genome-wide association study on legendre random regression coefficients for the growth and feed intake trajectory on Duroc Boars
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QTL-mapping and genomic prediction for bovine respiratory disease in U.S. Holsteins using sequence imputation and feature selection
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Reaffirmation of known major genes and the identification of novel candidate genes associated with carcass-related metrics based on whole genome sequence within a large multi-breed cattle population
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Functional models in genome-wide selection
journal, October 2019
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Exploring Evolutionary Relationships Across the Genome Using Topology Weighting
journal, March 2017
- Martin, Simon H.; Van Belleghem, Steven M.
- Genetics, Vol. 206, Issue 1
The identification of novel regions for reproduction trait in Landrace and Large White pigs using a single step genome-wide association study
journal, December 2018
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