PANOMICS meets germplasm

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, 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.

Keywords: GWAS; Green systems biology; PANOMICS; crop improvement; genome editing; germplasm; multi-omics; phenotyping; plant systems biology.

Figure 1

PANOMICS to germplasm. Data from PANOMICS subfields are integrated to guide breeding processes in order to provide elite lines to improve germplasm.

Figure 2

Plant morphological and physiological traits relevant for high yield.

Figure 3

Crop breeding pillars for precision breeding strategy. Currently, agronomically important genes are identified using marker‐assisted selection breeding (MAS) and genomic selection, which only provides ~40% of the phenotypic variance. In future, integration of PANOMICS platform will not only enhance the identification of remaining ~60% of phenotypic variance but also support the identification of agronomical trait correlated to the genes in the most rapid and effective manner to support precision breeding.