title: Genome-wide association study based on multiple imputation with low-depth sequencing data: application to biofuel traits in reed canarygrass.
abstract: Genotyping by sequencing allows for large-scale genetic analyses in plant species with no reference genome, but sets the challenge of sound inference in presence of uncertain genotypes. We report an imputation-based genome-wide association study (GWAS) in reed canarygrass (Phalaris arundinacea L., Phalaris caesia Nees), a cool-season grass species with potential as a biofuel crop. Our study involved two linkage populations and an association panel of 590 reed canarygrass genotypes. Plants were assayed for up to 5228 single nucleotide polymorphism markers and 35 traits. The genotypic markers were derived from low-depth sequencing with 78% missing data on average. To soundly infer marker-trait associations, multiple imputation (MI) was used: several imputes of the marker data were generated to reflect imputation uncertainty and association tests were performed on marker effects across imputes. A total of nine significant markers were identified, three of which showed significant homology with the Brachypodium dystachion genome. Because no physical map of the reed canarygrass genome was available, imputation was conducted using classification trees. In general, MI showed good consistency with the complete-case analysis and adequate control over imputation uncertainty. A gain in significance of marker effects was achieved through MI, but only for rare cases when missing data were <45%. In addition to providing insight into the genetic basis of important traits in reed canarygrass, this study presents one of the first applications of MI to genome-wide analyses and provides useful guidelines for conducting GWAS based on genotyping-by-sequencing data.
authors.affiliation: Department of Agronomy, University of Wisconsin-Madison, Madison, Wisconsin 53706 ramstein@wisc.edu.
authors.forename: Guillaume P
authors.initials: GP
authors.lastname: Ramstein
authors.affiliation: Institute for Genomic Diversity, Cornell University, Ithaca, New York 14853.
authors.forename: Alexander E
authors.initials: AE
authors.lastname: Lipka
authors.affiliation: Institute for Genomic Diversity, Cornell University, Ithaca, New York 14853.
authors.forename: Fei
authors.initials: F
authors.lastname: Lu
authors.affiliation: International Maize and Wheat Improvement Center (CIMMYT), 56237 Texcoco, Mexico.
authors.forename: Denise E
authors.initials: DE
authors.lastname: Costich
authors.affiliation: Soil and Crops Section, School of Integrative Plant Science, Cornell University, Ithaca, New York 14853.
authors.forename: Jerome H
authors.initials: JH
authors.lastname: Cherney
authors.affiliation: Institute for Genomic Diversity, Cornell University, Ithaca, New York 14853 Agricultural Research Service, United States Department of Agriculture, Ithaca, New York 14853 Department of Plant Breeding and Genetics, Cornell University, Ithaca, New York 14853.
authors.forename: Edward S
authors.initials: ES
authors.lastname: Buckler
authors.affiliation: Department of Agronomy, University of Wisconsin-Madison, Madison, Wisconsin 53706 Agricultural Research Service, United States Department of Agriculture, Madison, Wisconsin 53706.
authors.forename: Michael D
authors.initials: MD
authors.lastname: Casler
mesh_terms: D000465:Algorithms; D056804:Biofuels; D018745:Genome, Plant; D055106:Genome-Wide Association Study; D059014:High-Throughput Nucleotide Sequencing; D008962:Models, Theoretical; D019655:Quantitative Trait, Heritable
chemical_list: D056804:Biofuels
keywords: Phalaris spp.; bioenergy; genome-wide association study; genotyping by sequencing; multiple imputation