Please use this identifier to cite or link to this item: https://hdl.handle.net/1959.11/14595
Title: R for Genome-Wide Association Studies
Contributor(s): Gondro, Cedric (author)orcid ; Porto-Neto, Laercio R (author); Lee, S H (author)
Publication Date: 2013
DOI: 10.1007/978-1-62703-447-0_1
Handle Link: https://hdl.handle.net/1959.11/14595
Abstract: 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.
Publication Type: Book Chapter
Source of Publication: Genome-Wide Association Studies and Genomic Predictions, p. 1-17
Publisher: Humana Press
Place of Publication: New York, United States of America
ISBN: 9781627034470
9781627034463
Field of Research (FOR): 060412 Quantitative Genetics (incl Disease and Trait Mapping Genetics)
HERDC Category Description: B1 Chapter in a Scholarly Book
Other Links: http://trove.nla.gov.au/version/198468706
Series Name: Methods in Molecular Biology
Series Number : 1019
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Appears in Collections:Book Chapter

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