Genome-Wide Association Study Based on Multiple Imputation with Low-Depth Sequencing Data: Application to Biofuel Traits in Reed Canarygrass

Ramstein, Guillaume P.; Lipka, Alexander E.; Lu, Fei; Costich, Denise E.; Cherney, Jerome H.; Buckler, Edward S.; Casler, Michael D.

doi:10.1534/g3.115.017533

Genome-Wide Association Study Based on Multiple Imputation with Low-Depth Sequencing Data: Application to Biofuel Traits in Reed Canarygrass

Journal Article · Thu Mar 12 00:00:00 EDT 2015 · G3

DOI:https://doi.org/10.1534/g3.115.017533· OSTI ID:1627953

Ramstein, Guillaume P. ^[1]; Lipka, Alexander E. ^[2]; Lu, Fei ^[2]; Costich, Denise E. ^[3]; Cherney, Jerome H. ^[4]; Buckler, Edward S. ^[5]; Casler, Michael D. ^[6]

Univ. of Wisconsin, Madison, WI (United States). Dept. of Agronomy; DOE/OSTI
Univ. of Wisconsin, Madison, WI (United States). Inst. for Genomic Diversity
Cornell Univ., Ithaca, NY (United States). Dept. of Plant Breeding and Genetics
Cornell Univ., Ithaca, NY (United States). School of Integrative Plant Science. Soil and Crops Section
US Dept. of Agriculture (USDA), Ithaca, NY (United States). Agricultural Research Service (ARS); US Dept. of Agriculture (USDA), Madison, WI (United States). Agricultural Research Service (ARS)
Univ. of Wisconsin, Madison, WI (United States). Dept. of Agronomy

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 lowdepth 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.

View Accepted Manuscript (DOE)

Research Organization:: Agricultural Research Service

Sponsoring Organization:: USDOE Office of Science (SC)

Grant/Contract Number:: AI02-07ER64454

OSTI ID:: 1627953

Journal Information:: G3, Journal Name: G3 Journal Issue: 5 Vol. 5; ISSN 2160-1836

Publisher:: Genetics Society of AmericaCopyright Statement

Country of Publication:: United States

Language:: English

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59 BASIC BIOLOGICAL SCIENCES
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bioenergy
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Genome-Wide Association Study Based on Multiple Imputation with Low-Depth Sequencing Data: Application to Biofuel Traits in Reed Canarygrass

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