Abstract
SummaryGenotyping‐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.
Highlights
Climate change and food security are the two major issues of the 21st century
Plant breeding methods were mainly based on the phenotypic selection, and it was very effective for the traits with simple genetic make-up, for example traits with higher heritability (Hallauer et al, 2010), but it was time-consuming and labour-intensive
The PANOMICS platform is expected to facilitate crop improvement by discovering target genes and pathways for physiological phenotypes that are controlled by complex genetic and epigenetic mechanisms with the ultimate goal of ‘precision breeding’ to produce elite lines (Figure 1). This comprehensive information about the molecular system can be integrated through powerful data mining techniques (Weckwerth, 2011; Weckwerth, 2019). Based on these high-throughput technologies, we propose three fundamental pillars for future breeding strategies: Strategy 1: combining genome selection with environment-dependent PANOMICS analysis and deep learning to improve prediction accuracy for marker-dependent trait performance (Figure 1); Strategy 2: PANOMICS resolution at subtissue, cellular and subcellular level provides information about fundamental functions of selected markers; Strategy 3: combining PANOMICS with genome editing and speed breeding tools to accelerate and enhance large-scale functional validation of trait-specific precision breeding
Summary
Genotyping-by-sequencing has enabled approaches for genomic selection to improve yield, stress resistance and nutritional value. The larger the databases are growing, the better statistical approaches for genomic selection will be available. Intraspecific genetic variation is able to explain a high proportion of the phenotypes, but a large part of phenotypic plasticity stems from environmentally driven transcriptional, post-transcriptional, translational, post-translational, epigenetic and metabolic regulation. 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. We propose three fundamental pillars for future breeding strategies in the framework of Green Systems Biology: (i) combining genome selection with environmentdependent PANOMICS analysis and deep learning to improve prediction accuracy for markerdependent 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
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