Cargando…
PANOMICS meets germplasm
Genotyping‐by‐sequencing has enabled approaches for genomic selection to improve yield, stress resistance and nutritional value. More and more resource studies are emerging providing 1000 and more genotypes and millions of SNPs for one species covering a hitherto inaccessible intraspecific genetic v...
Autores principales: | , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley and Sons Inc.
2020
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7292548/ https://www.ncbi.nlm.nih.gov/pubmed/32163658 http://dx.doi.org/10.1111/pbi.13372 |
_version_ | 1783546133013856256 |
---|---|
author | Weckwerth, Wolfram Ghatak, Arindam Bellaire, Anke Chaturvedi, Palak Varshney, Rajeev K. |
author_facet | Weckwerth, Wolfram Ghatak, Arindam Bellaire, Anke Chaturvedi, Palak Varshney, Rajeev K. |
author_sort | Weckwerth, Wolfram |
collection | PubMed |
description | Genotyping‐by‐sequencing has enabled approaches for genomic selection to improve yield, stress resistance and nutritional value. More and more resource studies are emerging providing 1000 and more genotypes and millions of SNPs for one species covering a hitherto inaccessible intraspecific genetic variation. The larger the databases are growing, the better statistical approaches for genomic selection will be available. However, there are clear limitations on the statistical but also on the biological part. Intraspecific genetic variation is able to explain a high proportion of the phenotypes, but a large part of phenotypic plasticity also stems from environmentally driven transcriptional, post‐transcriptional, translational, post‐translational, epigenetic and metabolic regulation. Moreover, regulation of the same gene can have different phenotypic outputs in different environments. Consequently, to explain and understand environment‐dependent phenotypic plasticity based on the available genotype variation we have to integrate the analysis of further molecular levels reflecting the complete information flow from the gene to metabolism to phenotype. Interestingly, metabolomics platforms are already more cost‐effective than NGS platforms and are decisive for the prediction of nutritional value or stress resistance. Here, we propose three fundamental pillars for future breeding strategies in the framework of Green Systems Biology: (i) combining genome selection with environment‐dependent PANOMICS analysis and deep learning to improve prediction accuracy for marker‐dependent trait performance; (ii) PANOMICS resolution at subtissue, cellular and subcellular level provides information about fundamental functions of selected markers; (iii) combining PANOMICS with genome editing and speed breeding tools to accelerate and enhance large‐scale functional validation of trait‐specific precision breeding. |
format | Online Article Text |
id | pubmed-7292548 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | John Wiley and Sons Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-72925482020-06-15 PANOMICS meets germplasm Weckwerth, Wolfram Ghatak, Arindam Bellaire, Anke Chaturvedi, Palak Varshney, Rajeev K. Plant Biotechnol J Review Genotyping‐by‐sequencing has enabled approaches for genomic selection to improve yield, stress resistance and nutritional value. More and more resource studies are emerging providing 1000 and more genotypes and millions of SNPs for one species covering a hitherto inaccessible intraspecific genetic variation. The larger the databases are growing, the better statistical approaches for genomic selection will be available. However, there are clear limitations on the statistical but also on the biological part. Intraspecific genetic variation is able to explain a high proportion of the phenotypes, but a large part of phenotypic plasticity also stems from environmentally driven transcriptional, post‐transcriptional, translational, post‐translational, epigenetic and metabolic regulation. Moreover, regulation of the same gene can have different phenotypic outputs in different environments. Consequently, to explain and understand environment‐dependent phenotypic plasticity based on the available genotype variation we have to integrate the analysis of further molecular levels reflecting the complete information flow from the gene to metabolism to phenotype. Interestingly, metabolomics platforms are already more cost‐effective than NGS platforms and are decisive for the prediction of nutritional value or stress resistance. Here, we propose three fundamental pillars for future breeding strategies in the framework of Green Systems Biology: (i) combining genome selection with environment‐dependent PANOMICS analysis and deep learning to improve prediction accuracy for marker‐dependent trait performance; (ii) PANOMICS resolution at subtissue, cellular and subcellular level provides information about fundamental functions of selected markers; (iii) combining PANOMICS with genome editing and speed breeding tools to accelerate and enhance large‐scale functional validation of trait‐specific precision breeding. John Wiley and Sons Inc. 2020-05-19 2020-07 /pmc/articles/PMC7292548/ /pubmed/32163658 http://dx.doi.org/10.1111/pbi.13372 Text en © 2020 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 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 Weckwerth, Wolfram Ghatak, Arindam Bellaire, Anke Chaturvedi, Palak Varshney, Rajeev K. PANOMICS meets germplasm |
title | PANOMICS meets germplasm |
title_full | PANOMICS meets germplasm |
title_fullStr | PANOMICS meets germplasm |
title_full_unstemmed | PANOMICS meets germplasm |
title_short | PANOMICS meets germplasm |
title_sort | panomics meets germplasm |
topic | Review |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7292548/ https://www.ncbi.nlm.nih.gov/pubmed/32163658 http://dx.doi.org/10.1111/pbi.13372 |
work_keys_str_mv | AT weckwerthwolfram panomicsmeetsgermplasm AT ghatakarindam panomicsmeetsgermplasm AT bellaireanke panomicsmeetsgermplasm AT chaturvedipalak panomicsmeetsgermplasm AT varshneyrajeevk panomicsmeetsgermplasm |