Skip to main content
SpringerLink
Log in

R for Genome-Wide Association Studies

  • Protocol
  • First Online:
Genome-Wide Association Studies and Genomic Prediction

Part of the book series: Methods in Molecular Biology ((MIMB,volume 1019))

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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Protocol
USD 49.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 279.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. R Development Core Team (2012) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria

    Google Scholar 

  2. Gentleman RC, Carey VJ, Bates DM, Bolstad B, Dettling M, Dudoit S, Ellis B, Gautier L, Ge YC, Gentry J, Hornik K, Hothorn T, Huber W, Iacus S, Irizarry R, Leisch F, Li C, Maechler M, Rossini AJ, Sawitzki G, Smith C, Smyth G, Tierney L, Yang JYH, Zhang JH (2004) Bioconductor: open software development for computational biology and bioinformatics. Genome Biol 5(10):R80

    Article  PubMed  Google Scholar 

  3. Chambers JM (2008) Software for data analysis—programming with R. Springer, New York

    Book  Google Scholar 

  4. Jones O, Maillardet R, Robinson A (2009) Introduction to scientific programming and simulation using R chapman & Hall/CRC, Boca Raton

    Google Scholar 

  5. Tierney L, Rossini AJ, Li N (2009) Snow: a parallel computing framework for the R system. Int J Parallel Program 37:78–90

    Article  Google Scholar 

  6. Rossini AJ, Tierney L, Li NM (2007) Simple parallel statistical computing in R. J Comput Graph Stat 16(2):399–420

    Article  Google Scholar 

  7. Schmidberger M, Morgan M, Eddelbuettel D, Yu H, Tierney L, Mansmann U (2009) State of the art in parallel computing with R. J Stat Softw 31(1):27p

    Google Scholar 

  8. Eugster MJA, Knaus J, Porzelius C, Schmidberger M, Vicedo E (2011) Hands-on tutorial for parallel computing with R. Comput Stat 26:219–239

    Article  Google Scholar 

  9. Gentleman RC (2009) R programming for bioinformatics. Chapman & Hall/CRC, Boca Raton

    Google Scholar 

  10. Rizzo ML (2007) Statistical computing with R. Chapman & Hall/CRC, Boca Raton

    Google Scholar 

Download references

Acknowledgments

This work was supported by a grant from the Next-Generation BioGreen 21 Program (No. PJ008196), Rural Development Administration, Republic of Korea.

Author information

Authors and Affiliations

  1. The Centre for Genetic Analysis and Applications, University of New England, Armidale, NSW, Australia

    Cedric Gondro

  2. School of Environmental and Rural Science, University of New England, Armidale, NSW, Australia

    Laercio R. Porto-Neto

  3. School of Veterinary Science, The University of Queensland, St. Lucia, Brisbane, QLD, Australia

    Laercio R. Porto-Neto

  4. Hanwoo Experimental Station, National Institute of Animal Science, RDA, Pyongchang, Republic of Korea

    Seung Hwan Lee

Authors
  1. Cedric Gondro
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Laercio R. Porto-Neto
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Seung Hwan Lee
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. , Ctr. Genetic Analysis and Applications, University of New England, Homestead Building 18, Armidale, 2351, New South Wales, Australia

    Cedric Gondro

  2. School of Environmental and Rural Scienc, Div. Animal Science, University of New England, Homestead Building 36, Armidale, 2351, New South Wales, Australia

    Julius van der Werf

  3. , Biosciences Research Division, Department of Primary Industries, Ring Road 5, Bundoora, 3083, Victoria, Australia

    Ben Hayes

Rights and permissions

Reprints and permissions

Copyright information

© 2013 Springer Science+Business Media, LLC

About this protocol

Cite this protocol

Gondro, C., Porto-Neto, L.R., Lee, S.H. (2013). R for Genome-Wide Association Studies. In: Gondro, C., van der Werf, J., Hayes, B. (eds) Genome-Wide Association Studies and Genomic Prediction. Methods in Molecular Biology, vol 1019. Humana Press, Totowa, NJ. https://doi.org/10.1007/978-1-62703-447-0_1

Download citation

  • DOI: https://doi.org/10.1007/978-1-62703-447-0_1

  • Published:

  • Publisher Name: Humana Press, Totowa, NJ

  • Print ISBN: 978-1-62703-446-3

  • Online ISBN: 978-1-62703-447-0

  • eBook Packages: Springer Protocols

Publish with us

Policies and ethics

Search

Navigation