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Genotyping‐by‐sequencing approaches to characterize crop genomes: choosing the right tool for the right application
In the last decade, the revolution in sequencing technologies has deeply impacted crop genotyping practice. New methods allowing rapid, high‐throughput genotyping of entire crop populations have proliferated and opened the door to wider use of molecular tools in plant breeding. These new genotyping‐...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley and Sons Inc.
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5258866/ https://www.ncbi.nlm.nih.gov/pubmed/27696619 http://dx.doi.org/10.1111/pbi.12645 |
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author | Scheben, Armin Batley, Jacqueline Edwards, David |
author_facet | Scheben, Armin Batley, Jacqueline Edwards, David |
author_sort | Scheben, Armin |
collection | PubMed |
description | In the last decade, the revolution in sequencing technologies has deeply impacted crop genotyping practice. New methods allowing rapid, high‐throughput genotyping of entire crop populations have proliferated and opened the door to wider use of molecular tools in plant breeding. These new genotyping‐by‐sequencing (GBS) methods include over a dozen reduced‐representation sequencing (RRS) approaches and at least four whole‐genome resequencing (WGR) approaches. The diversity of methods available, each often producing different types of data at different cost, can make selection of the best‐suited method seem a daunting task. We review the most common genotyping methods used today and compare their suitability for linkage mapping, genomewide association studies (GWAS), marker‐assisted and genomic selection and genome assembly and improvement in crops with various genome sizes and complexity. Furthermore, we give an outline of bioinformatics tools for analysis of genotyping data. WGR is well suited to genotyping biparental cross populations with complex, small‐ to moderate‐sized genomes and provides the lowest cost per marker data point. RRS approaches differ in their suitability for various tasks, but demonstrate similar costs per marker data point. These approaches are generally better suited for de novo applications and more cost‐effective when genotyping populations with large genomes or high heterozygosity. We expect that although RRS approaches will remain the most cost‐effective for some time, WGR will become more widespread for crop genotyping as sequencing costs continue to decrease. |
format | Online Article Text |
id | pubmed-5258866 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | John Wiley and Sons Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-52588662017-02-03 Genotyping‐by‐sequencing approaches to characterize crop genomes: choosing the right tool for the right application Scheben, Armin Batley, Jacqueline Edwards, David Plant Biotechnol J Review In the last decade, the revolution in sequencing technologies has deeply impacted crop genotyping practice. New methods allowing rapid, high‐throughput genotyping of entire crop populations have proliferated and opened the door to wider use of molecular tools in plant breeding. These new genotyping‐by‐sequencing (GBS) methods include over a dozen reduced‐representation sequencing (RRS) approaches and at least four whole‐genome resequencing (WGR) approaches. The diversity of methods available, each often producing different types of data at different cost, can make selection of the best‐suited method seem a daunting task. We review the most common genotyping methods used today and compare their suitability for linkage mapping, genomewide association studies (GWAS), marker‐assisted and genomic selection and genome assembly and improvement in crops with various genome sizes and complexity. Furthermore, we give an outline of bioinformatics tools for analysis of genotyping data. WGR is well suited to genotyping biparental cross populations with complex, small‐ to moderate‐sized genomes and provides the lowest cost per marker data point. RRS approaches differ in their suitability for various tasks, but demonstrate similar costs per marker data point. These approaches are generally better suited for de novo applications and more cost‐effective when genotyping populations with large genomes or high heterozygosity. We expect that although RRS approaches will remain the most cost‐effective for some time, WGR will become more widespread for crop genotyping as sequencing costs continue to decrease. John Wiley and Sons Inc. 2017-01-24 2017-02 /pmc/articles/PMC5258866/ /pubmed/27696619 http://dx.doi.org/10.1111/pbi.12645 Text en © 2016 The Authors. Plant Biotechnology Journal published by Society for Experimental Biology and The Association of Applied Biologists and John Wiley & Sons Ltd. This is an open access article under the terms of the Creative Commons Attribution (http://creativecommons.org/licenses/by/4.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Review Scheben, Armin Batley, Jacqueline Edwards, David Genotyping‐by‐sequencing approaches to characterize crop genomes: choosing the right tool for the right application |
title | Genotyping‐by‐sequencing approaches to characterize crop genomes: choosing the right tool for the right application |
title_full | Genotyping‐by‐sequencing approaches to characterize crop genomes: choosing the right tool for the right application |
title_fullStr | Genotyping‐by‐sequencing approaches to characterize crop genomes: choosing the right tool for the right application |
title_full_unstemmed | Genotyping‐by‐sequencing approaches to characterize crop genomes: choosing the right tool for the right application |
title_short | Genotyping‐by‐sequencing approaches to characterize crop genomes: choosing the right tool for the right application |
title_sort | genotyping‐by‐sequencing approaches to characterize crop genomes: choosing the right tool for the right application |
topic | Review |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5258866/ https://www.ncbi.nlm.nih.gov/pubmed/27696619 http://dx.doi.org/10.1111/pbi.12645 |
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