PANOMICS meets germplasm

Abstract

Summary 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.

Identification and Characterization of Contrasting Genotypes/Cultivars for Developing Heat Tolerance in Agricultural Crops: Current Status and Prospects

“…Advances in sequencing technologies especially, next generation sequencing (NGS), genotyping by sequencing (GBS), and other high throughput genotyping platforms have facilitated narrowing down of the heat tolerance QTL regions for analysis of candidate genes (Xu et al, 2017;Kilasi et al, 2018;Inghelandt et al, 2019;Tadesse et al, 2019). Given the huge number of novel SNPs developed recently and GWAS in large set of global crop germplasm, it became possible to identify novel haplotypes/genomic regions controlling heat tolerance (Paul et al, 2018;Varshney et al, 2019;Khan et al, 2020;Weckwerth et al, 2020) and allowed for the assessment of genetic diversity at nucleotide-scale. High throughput phenotyping coupled with advanced imaging devices, unmanned vehicles and machine learning, deep learning approaches and molecular genetics tools can further enhance the accuracy of selection of genomic regions associated with heat tolerance.…”

Systematic Multi-Omics Integration (MOI) Approach in Plant Systems Biology

“…Even when sampling of both is done at the same time, the translational and post-translational degradation and modification rates may differ among proteins. However, this is often unpredictable from the genome sequences alone (Weckwerth, 2019;Weckwerth et al, 2020), convoluting meaningful and direct interpretation between expression data.…”

“…Furthermore, this integration may have varied identifiers between databases, for instance metabolite nomenclature, further complicates integration process, especially in plants which possess high variability of secondary metabolites (Cavill et al, 2016). As sequencing may no longer be an issue due to affordable sequencing technologies with better throughput, such as SMRT (Single Molecule, Real-Time) Pacific Bioscience and Oxford Nanopore (Weckwerth, 2011;Weckwerth et al, 2020) specific databases for various non-model species can be generated to enrich and supplement the gene annotation. Hence this may provide thorough metabolic pathways for various organisms to allow MOI in pathway mapping to be performed holistically.…”

“…Protein regulation and post-translational modifications, such as phosphorylation are also known to impact the coordination of various cellular and biochemical processes (Nukarinen et al, 2016). Yet, they are unpredictable from any metabolic reconstruction (Weckwerth et al, 2020), complicating comprehensive and functional GSM. Metabolic and phenotypic dynamism are also affected by environmental factors, which thus far are not deductively informed by the genomes alone (Weckwerth et al, 2020).…”

“…In comparison, MOI in plants has been more difficult due to their metabolic diversity, poorly annotated large genomes (particularly for non-model species), and the presence of numerous symbionts with complex interaction networks. Several comprehensive reviews are available specifically on plant MOI (Fukushima et al, 2009;Fukushima et al, 2014;Rajasundaram and Selbig, 2016;Rai et al, 2017;Rai et al, 2019) and its practical usage in green systems biology, precision plant breeding, and other biotechnological applications (Weckwerth, 2011;Weckwerth, 2019;Weckwerth et al, 2020). However, the advancement of high-throughput technologies and large omics data sets leading to big data biology can be overwhelming, and perhaps an "Achilles' heel" for inexperience researchers.…”