eprintid: 11557
rev_number: 15
eprint_status: archive
userid: 3170
dir: disk0/00/01/15/57
datestamp: 2020-08-14 11:39:10
lastmod: 2020-08-14 11:40:56
status_changed: 2020-08-14 11:40:56
type: article
metadata_visibility: show
creators_name: Weckwerth, W
creators_name: Ghatak, A
creators_name: Bellaire, A
creators_name: Chaturvedi, P
creators_name: Varshney, R K
icrisatcreators_name: Varshney, R K
affiliation: Molecular Systems Biology (MOSYS), Department of Functional and Evolutionary Ecology, Faculty of Life Sciences, University of Vienna, Vienna, Austria
affiliation: Vienna Metabolomics Center (VIME), University of Vienna, Vienna, Austria
affiliation: ICRISAT (Patancheru)
country: Austria
country: India
title: PANOMICS meets germplasm
ispublished: pub
subjects: s2.13
subjects: s3000
divisions: CRPS3
crps: CG1
full_text_status: public
keywords: Green systems biology, PANOMICS, Plant systems biology,
Multi-omics, Phenotyping, Crop improvement, Germplasm, Genome
editing, GWAS.
note: We thank Austrian Science Fund (FWF, DerWissenschaftsfonds),
Grant agreement number W1257-820 for the financial support of
Arindam Ghatak. Rajeev K Varshney is thankful to Science &
Engineering Research Board (SERB) of Department of Science &
Technology (DST), Government of India for providing the J.C.Bose
National Fellowship (SB/S9/Z-13/2019) and CGIAR Research
Program on Grain Legumes and Dryland Cereals (GLDC) for
Funding Research at Center of Excellence in Genomics & Systems
Biology (CEGSB), ICRISAT, Patancheru, Hyderabad, India. ICRISAT
is a member of CGIAR Consortium.
abstract: 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, ranslational, 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.
date: 2020-03
date_type: published
publication: Plant Biotechnology Journal (TSI)
volume: 18
number: 7
publisher: Wiley
pagerange: 1507-1525
id_number: doi:10.1111/pbi.13372
refereed: TRUE
issn: 1467-7644
official_url: https://doi.org/10.1111/pbi.13372
related_url_url: https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=10.1111%2Fpbi.13372&btnG=
related_url_type: pub
citation:   Weckwerth, W and Ghatak, A and Bellaire, A and Chaturvedi, P and Varshney, R K  (2020) PANOMICS meets germplasm.  Plant Biotechnology Journal (TSI), 18 (7).  pp. 1507-1525.  ISSN 1467-7644     
document_url: http://oar.icrisat.org/11557/1/pbi.13372.pdf