title: Genome-Wide Association Analysis Using R.
abstract: This chapter provides a practical overview of the statistical analysis using R [1] and genotype by sequencing (GBS) markers for genome-wide association studies (GWAS) in oats. Statistical analysis is performed by R package rrBLUP [2] and issues associated with the analysis are addressed along with the R code. The ultimate aim of this chapter is to provide a practical guideline to do GWAS analysis using R, rather than describe the theory in depth. For more details about the subject, readers are referred to the excellent resource book in GWAS [3]. A basic programming experience in R is assumed.
authors.affiliation: Agriculture and Food Science, University College Dublin, Room 1.38, Belfield, Dublin 4, Ireland. j.isidro@ucd.ie.
authors.forename: Julio
authors.initials: J
authors.lastname: Isidro-Sánchez
authors.affiliation: Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY, USA.
authors.forename: Deniz
authors.initials: D
authors.lastname: Akdemir
authors.affiliation: Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY, USA.
authors.forename: Gracia
authors.initials: G
authors.lastname: Montilla-Bascón
mesh_terms: D005828:Genetics, Population; D055106:Genome-Wide Association Study / Q000379:methods* / Q000592:standards*; D005838:Genotype; D015810:Linkage Disequilibrium; D010641:Phenotype; D011110:Polymorphism, Genetic; D040641:Quantitative Trait Loci; D000073886:Scientific Experimental Error / Q000706:statistics & numerical data*; D012984:Software; D064878:Web Browser
keywords: Bonferroni correction; False discovery rate; GWAS; Linkage disequilibrium; Population structure