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Genome-Wide Association Study of Yield Component Traits in Intermediate Wheatgrass and Implications in Genomic Selection and Breeding

Intermediate wheatgrass (Thinopyrum intermedium, IWG) is a perennial grain crop with high biomass and grain yield, long seeds, and resistance to pests and diseases. It also reduces soil erosion, nitrate and mineral leaching into underground water tables, and sequesters carbon in its roots. The domes...

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Autores principales: Bajgain, Prabin, Zhang, Xiaofei, Anderson, James A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Genetics Society of America 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6686922/
https://www.ncbi.nlm.nih.gov/pubmed/31147390
http://dx.doi.org/10.1534/g3.119.400073
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author Bajgain, Prabin
Zhang, Xiaofei
Anderson, James A.
author_facet Bajgain, Prabin
Zhang, Xiaofei
Anderson, James A.
author_sort Bajgain, Prabin
collection PubMed
description Intermediate wheatgrass (Thinopyrum intermedium, IWG) is a perennial grain crop with high biomass and grain yield, long seeds, and resistance to pests and diseases. It also reduces soil erosion, nitrate and mineral leaching into underground water tables, and sequesters carbon in its roots. The domestication timeline of IWG as a grain crop spans only 3 decades, hence it lags annual grain crops in yield and seed characteristics. One approach to improve its agronomic traits is by using molecular markers to uncover marker-trait associations. In this study, we performed association mapping on IWG breeding germplasm from the third recurrent selection cycle at the University of Minnesota. The IWG population was phenotyped in St Paul, MN in 2017 and 2018, and in Crookston, MN in 2018 for grain yield, seed length, width and weight, spike length and weight, and number of spikelets per spike. Strong positive correlations were observed among most trait pairs, with correlations as high as 0.76. Genotyping using high throughput sequencing identified 8,899 high-quality genome-wide SNPs which were combined with phenotypic data in association mapping to discover regions associated with the yield component traits. We detected 154 genetic loci associated with these traits of which 19 were shared between at least two traits. Prediction of breeding values using significant loci as fixed effects in genomic selection model improved predictive abilities by up to 14%. Genetic mapping of agronomic traits followed by using genomic selection to predict breeding values can assist breeders in selecting superior genotypes to accelerate IWG domestication.
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spelling pubmed-66869222019-08-11 Genome-Wide Association Study of Yield Component Traits in Intermediate Wheatgrass and Implications in Genomic Selection and Breeding Bajgain, Prabin Zhang, Xiaofei Anderson, James A. G3 (Bethesda) Genomic Prediction Intermediate wheatgrass (Thinopyrum intermedium, IWG) is a perennial grain crop with high biomass and grain yield, long seeds, and resistance to pests and diseases. It also reduces soil erosion, nitrate and mineral leaching into underground water tables, and sequesters carbon in its roots. The domestication timeline of IWG as a grain crop spans only 3 decades, hence it lags annual grain crops in yield and seed characteristics. One approach to improve its agronomic traits is by using molecular markers to uncover marker-trait associations. In this study, we performed association mapping on IWG breeding germplasm from the third recurrent selection cycle at the University of Minnesota. The IWG population was phenotyped in St Paul, MN in 2017 and 2018, and in Crookston, MN in 2018 for grain yield, seed length, width and weight, spike length and weight, and number of spikelets per spike. Strong positive correlations were observed among most trait pairs, with correlations as high as 0.76. Genotyping using high throughput sequencing identified 8,899 high-quality genome-wide SNPs which were combined with phenotypic data in association mapping to discover regions associated with the yield component traits. We detected 154 genetic loci associated with these traits of which 19 were shared between at least two traits. Prediction of breeding values using significant loci as fixed effects in genomic selection model improved predictive abilities by up to 14%. Genetic mapping of agronomic traits followed by using genomic selection to predict breeding values can assist breeders in selecting superior genotypes to accelerate IWG domestication. Genetics Society of America 2019-05-30 /pmc/articles/PMC6686922/ /pubmed/31147390 http://dx.doi.org/10.1534/g3.119.400073 Text en Copyright © 2019 Bajgain et al. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Genomic Prediction
Bajgain, Prabin
Zhang, Xiaofei
Anderson, James A.
Genome-Wide Association Study of Yield Component Traits in Intermediate Wheatgrass and Implications in Genomic Selection and Breeding
title Genome-Wide Association Study of Yield Component Traits in Intermediate Wheatgrass and Implications in Genomic Selection and Breeding
title_full Genome-Wide Association Study of Yield Component Traits in Intermediate Wheatgrass and Implications in Genomic Selection and Breeding
title_fullStr Genome-Wide Association Study of Yield Component Traits in Intermediate Wheatgrass and Implications in Genomic Selection and Breeding
title_full_unstemmed Genome-Wide Association Study of Yield Component Traits in Intermediate Wheatgrass and Implications in Genomic Selection and Breeding
title_short Genome-Wide Association Study of Yield Component Traits in Intermediate Wheatgrass and Implications in Genomic Selection and Breeding
title_sort genome-wide association study of yield component traits in intermediate wheatgrass and implications in genomic selection and breeding
topic Genomic Prediction
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6686922/
https://www.ncbi.nlm.nih.gov/pubmed/31147390
http://dx.doi.org/10.1534/g3.119.400073
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