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ARS Home » Midwest Area » Madison, Wisconsin » U.S. Dairy Forage Research Center » Dairy Forage Research » Research » Publications at this Location » Publication #313113

Research Project: Redesigning Forage Genetics, Management, and Harvesting for Efficiency, Profit, and Sustainability in Dairy and Bioenergy Production Systems

Location: Dairy Forage Research

Title: Genome-wide association study based on multiple imputation with low-depth sequencing data: application to biofuel traits in reed canarygrass

Author
item RAMSTEIN, GUILLAUME - UNIVERSITY OF WISCONSIN
item Casler, Michael

Submitted to: Plant and Animal Genome Conference
Publication Type: Abstract Only
Publication Acceptance Date: 12/1/2014
Publication Date: 1/10/2015
Citation: Ramstein, G., Casler, M.D. 2015. Genome-wide association study based on multiple imputation with low-depth sequencing data: application to biofuel traits in reed canarygrass [abstract]. Plant and Animal Genome Conference. Paper No. W567.

Interpretive Summary:

Technical 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 5,228 single nucleotide polymorphism markers and 39 traits. The genotypic markers were derived from low-depth sequencing with 83% missing data in 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 supervised learning (classification trees or random forests). 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 the amount of missing data was < 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.