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Genotype Imputation in Winter Wheat Using First-Generation Haplotype Map SNPs Improves Genome-Wide Association Mapping and Genomic Prediction of Traits

Genome-wide single nucleotide polymorphism (SNP) variation allows for the capture of haplotype structure in populations and prediction of unobserved genotypes based on inferred regions of identity-by-descent (IBD). Here we have used a first-generation wheat haplotype map created by targeted re-seque...

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Autores principales: Nyine, Moses, Wang, Shichen, Kiani, Kian, Jordan, Katherine, Liu, Shuyu, Byrne, Patrick, Haley, Scott, Baenziger, Stephen, Chao, Shiaoman, Bowden, Robert, Akhunov, Eduard
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Genetics Society of America 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6325902/
https://www.ncbi.nlm.nih.gov/pubmed/30420469
http://dx.doi.org/10.1534/g3.118.200664
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author Nyine, Moses
Wang, Shichen
Kiani, Kian
Jordan, Katherine
Liu, Shuyu
Byrne, Patrick
Haley, Scott
Baenziger, Stephen
Chao, Shiaoman
Bowden, Robert
Akhunov, Eduard
author_facet Nyine, Moses
Wang, Shichen
Kiani, Kian
Jordan, Katherine
Liu, Shuyu
Byrne, Patrick
Haley, Scott
Baenziger, Stephen
Chao, Shiaoman
Bowden, Robert
Akhunov, Eduard
author_sort Nyine, Moses
collection PubMed
description Genome-wide single nucleotide polymorphism (SNP) variation allows for the capture of haplotype structure in populations and prediction of unobserved genotypes based on inferred regions of identity-by-descent (IBD). Here we have used a first-generation wheat haplotype map created by targeted re-sequencing of low-copy genomic regions in the reference panel of 62 lines to impute marker genotypes in a diverse panel of winter wheat cultivars from the U.S. Great Plains. The IBD segments between the reference population and winter wheat cultivars were identified based on SNP genotyped using the 90K iSelect wheat array and genotyping by sequencing (GBS). A genome-wide association study and genomic prediction of resistance to stripe rust in winter wheat cultivars showed that an increase in marker density achieved by imputation improved both the power and precision of trait mapping and prediction. The majority of the most significant marker-trait associations belonged to imputed genotypes. With the vast amount of SNP variation data accumulated for wheat in recent years, the presented imputation framework will greatly improve prediction accuracy in breeding populations and increase resolution of trait mapping hence, facilitate cross-referencing of genotype datasets available across different wheat populations.
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spelling pubmed-63259022019-01-10 Genotype Imputation in Winter Wheat Using First-Generation Haplotype Map SNPs Improves Genome-Wide Association Mapping and Genomic Prediction of Traits Nyine, Moses Wang, Shichen Kiani, Kian Jordan, Katherine Liu, Shuyu Byrne, Patrick Haley, Scott Baenziger, Stephen Chao, Shiaoman Bowden, Robert Akhunov, Eduard G3 (Bethesda) Genomic Prediction Genome-wide single nucleotide polymorphism (SNP) variation allows for the capture of haplotype structure in populations and prediction of unobserved genotypes based on inferred regions of identity-by-descent (IBD). Here we have used a first-generation wheat haplotype map created by targeted re-sequencing of low-copy genomic regions in the reference panel of 62 lines to impute marker genotypes in a diverse panel of winter wheat cultivars from the U.S. Great Plains. The IBD segments between the reference population and winter wheat cultivars were identified based on SNP genotyped using the 90K iSelect wheat array and genotyping by sequencing (GBS). A genome-wide association study and genomic prediction of resistance to stripe rust in winter wheat cultivars showed that an increase in marker density achieved by imputation improved both the power and precision of trait mapping and prediction. The majority of the most significant marker-trait associations belonged to imputed genotypes. With the vast amount of SNP variation data accumulated for wheat in recent years, the presented imputation framework will greatly improve prediction accuracy in breeding populations and increase resolution of trait mapping hence, facilitate cross-referencing of genotype datasets available across different wheat populations. Genetics Society of America 2018-11-16 /pmc/articles/PMC6325902/ /pubmed/30420469 http://dx.doi.org/10.1534/g3.118.200664 Text en Copyright © 2019 Nyine 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
Nyine, Moses
Wang, Shichen
Kiani, Kian
Jordan, Katherine
Liu, Shuyu
Byrne, Patrick
Haley, Scott
Baenziger, Stephen
Chao, Shiaoman
Bowden, Robert
Akhunov, Eduard
Genotype Imputation in Winter Wheat Using First-Generation Haplotype Map SNPs Improves Genome-Wide Association Mapping and Genomic Prediction of Traits
title Genotype Imputation in Winter Wheat Using First-Generation Haplotype Map SNPs Improves Genome-Wide Association Mapping and Genomic Prediction of Traits
title_full Genotype Imputation in Winter Wheat Using First-Generation Haplotype Map SNPs Improves Genome-Wide Association Mapping and Genomic Prediction of Traits
title_fullStr Genotype Imputation in Winter Wheat Using First-Generation Haplotype Map SNPs Improves Genome-Wide Association Mapping and Genomic Prediction of Traits
title_full_unstemmed Genotype Imputation in Winter Wheat Using First-Generation Haplotype Map SNPs Improves Genome-Wide Association Mapping and Genomic Prediction of Traits
title_short Genotype Imputation in Winter Wheat Using First-Generation Haplotype Map SNPs Improves Genome-Wide Association Mapping and Genomic Prediction of Traits
title_sort genotype imputation in winter wheat using first-generation haplotype map snps improves genome-wide association mapping and genomic prediction of traits
topic Genomic Prediction
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6325902/
https://www.ncbi.nlm.nih.gov/pubmed/30420469
http://dx.doi.org/10.1534/g3.118.200664
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