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...

Descripción completa

Detalles Bibliográficos
Autores principales: Weckwerth, Wolfram, Ghatak, Arindam, Bellaire, Anke, Chaturvedi, Palak, Varshney, Rajeev K.
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