title: R for genome-wide association studies.
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.
authors.affiliation: The Centre for Genetic Analysis and Applications, University of New England, Armidale, NSW, Australia.
authors.forename: Cedric
authors.initials: C
authors.lastname: Gondro
authors.forename: Laercio R
authors.initials: LR
authors.lastname: Porto-Neto
authors.forename: Seung Hwan
authors.initials: SH
authors.lastname: Lee
mesh_terms: D003198:Computer Simulation; D015894:Genome, Human; D055106:Genome-Wide Association Study / Q000706:statistics & numerical data*; D023281:Genomics / Q000379:methods* / Q000706:statistics & numerical data; D006801:Humans; D020641:Polymorphism, Single Nucleotide; D011381:Programming Languages; D013997:Time Factors