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 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 datamore »
- Authors:
-
- Univ. of Wisconsin, Madison, WI (United States). Dept. of Agronomy
- 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)
- Publication Date:
- Research Org.:
- US Dept. of Agriculture (USDA), Washington, DC (United States). Agricultural Research Service (ARS)
- Sponsoring Org.:
- USDOE Office of Science (SC)
- OSTI Identifier:
- 1627953
- Grant/Contract Number:
- AI02-07ER64454
- Resource Type:
- Accepted Manuscript
- Journal Name:
- G3
- Additional Journal Information:
- Journal Volume: 5; Journal Issue: 5; Journal ID: ISSN 2160-1836
- Publisher:
- Genetics Society of America
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 59 BASIC BIOLOGICAL SCIENCES; Genetics & Heredity; genome-wide association study; multiple imputation; genotyping by sequencing; bioenergy; Phalaris spp
Citation Formats
Ramstein, Guillaume P., Lipka, Alexander E., Lu, Fei, Costich, Denise E., Cherney, Jerome H., Buckler, Edward S., and Casler, Michael D. Genome-Wide Association Study Based on Multiple Imputation with Low-Depth Sequencing Data: Application to Biofuel Traits in Reed Canarygrass. United States: N. p., 2015.
Web. doi:10.1534/g3.115.017533.
Ramstein, Guillaume P., Lipka, Alexander E., Lu, Fei, Costich, Denise E., Cherney, Jerome H., Buckler, Edward S., & Casler, Michael D. Genome-Wide Association Study Based on Multiple Imputation with Low-Depth Sequencing Data: Application to Biofuel Traits in Reed Canarygrass. United States. https://doi.org/10.1534/g3.115.017533
Ramstein, Guillaume P., Lipka, Alexander E., Lu, Fei, Costich, Denise E., Cherney, Jerome H., Buckler, Edward S., and Casler, Michael D. Thu .
"Genome-Wide Association Study Based on Multiple Imputation with Low-Depth Sequencing Data: Application to Biofuel Traits in Reed Canarygrass". United States. https://doi.org/10.1534/g3.115.017533. https://www.osti.gov/servlets/purl/1627953.
@article{osti_1627953,
title = {Genome-Wide Association Study Based on Multiple Imputation with Low-Depth Sequencing Data: Application to Biofuel Traits in Reed Canarygrass},
author = {Ramstein, Guillaume P. and Lipka, Alexander E. and Lu, Fei and Costich, Denise E. and Cherney, Jerome H. and Buckler, Edward S. and Casler, Michael D.},
abstractNote = {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.},
doi = {10.1534/g3.115.017533},
journal = {G3},
number = 5,
volume = 5,
place = {United States},
year = {Thu Mar 12 00:00:00 EDT 2015},
month = {Thu Mar 12 00:00:00 EDT 2015}
}
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