R for genome-wide association studies

Gondro, Cedric, Porto-Neto, Laercio R. and Lee, Seung Hwan (2013). R for genome-wide association studies. In Genome-Wide Association Studies and Genomic Prediction (pp. 1-17) New York, NY United States: Humana Press. doi:10.1007/978-1-62703-447-0-1

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Author Gondro, Cedric
Porto-Neto, Laercio R.
Lee, Seung Hwan
Title of chapter R for genome-wide association studies
Title of book Genome-Wide Association Studies and Genomic Prediction
Place of Publication New York, NY United States
Publisher Humana Press
Publication Year 2013
Sub-type Research book chapter (original research)
DOI 10.1007/978-1-62703-447-0-1
Open Access Status
Year available 2013
Series Methods in Molecular Biology
ISBN 9781627034463
9781627034470
ISSN 1064-3745
1940-6029
Volume number 1019
Start page 1
End page 17
Total pages 17
Language eng
Subjects 1312 Molecular Biology
1311 Genetics
Abstract/Summary In recent years R has become de facto statistical programming language of choice for statisticians and it is also arguably the most widely used generic environment for analysis of high-throughput genomic data. In this chapter we discuss some approaches to improve performance of R when working with large SNP datasets.
Keyword Genome-wide association studies
High-throughput analysis
Parallel computation
R programming
Q-Index Code BX
Q-Index Status Confirmed Code
Institutional Status UQ

Document type: Book Chapter
Collections: Non HERDC
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Created: Tue, 03 Dec 2013, 01:53:26 EST by System User on behalf of School of Veterinary Science