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Genome-wide association study based on multiple imputation with low-depth sequencing data: Application to biofuel traits in reed canarygrass
- G. P. Ramstein, A. E. Lipka, F. Lu, D. E. Costich, J. H. Cherney, E. S. Buckler, M. D. Casler
- G3 2015 v.5 no.5 pp. 891-909
- Brachypodium distachyon, Phalaris arundinacea, biofuels, cool season grasses, energy crops, genome, genome-wide association study, genotype, genotyping, guidelines, sequence analysis, single nucleotide polymorphism, uncertainty
- 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.