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

G3 : Genes - Genomes - Genetics
Guillaume P RamsteinMichael D Casler

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 adequat...Continue Reading

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Methods Mentioned

BETA
genotyping
FCS

Software Mentioned

BLAST
mice
mgcv R
R
rrBLUP
rpart
JM
MI
R package
FCS

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