Browsing by Subject "Genome"

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Effects of using deep learning to predict the geographic origin of barley genebank accessions on genome–environment association studies
(2025) Chang, Che-Wei; Schmid, Karl
Genome–environment association (GEA) is an approach for identifying adaptive loci by combining genetic variation with environmental parameters, offering potential for improving crop resilience. However, its application to genebank accessions is limited by missing geographic origin data. To address this limitation, we explored the use of neural networks to predict the geographic origins of barley accessions and integrate imputed environmental data into GEA. Neural networks demonstrated high accuracy in cross-validation but occasionally produced ecologically implausible predictions as models solely considered geographical proximity. For example, some predicted origins were located within non-arable regions, such as the Mediterranean Sea. Using barley flowering time genes as benchmarks, GEA integrating imputed environmental data ( N=11,032) displayed partially concordant yet complementary detection of genomic regions near flowering time genes compared to regular GEA ( N=1,626), highlighting the potential of GEA with imputed data to complement regular GEA in uncovering novel adaptive loci. Also, contrary to our initial hypothesis anticipating a significant improvement in GEA performance by increasing sample size, our simulations yield unexpected insights. Our study suggests potential limitations in the sensitivity of GEA approaches to the considerable expansion in sample size achieved through predicting missing geographical data. Overall, our study provides insights into leveraging incomplete geographical origin data by integrating deep learning with GEA. Our findings indicate the need for further development of GEA approaches to optimize the use of imputed environmental data, such as incorporating regional GEA patterns instead of solely focusing on global associations between allele frequencies and environmental gradients across large-scale landscapes.
Genome-wide characterization of two-component system elements in barley enables the identification of grain-specific phosphorelay genes
(2025) Hertig, Christian W.; Devunuri, Pravinya; Rutten, Twan; Hensel, Götz; Schippers, Jos H. M.; Müller, Bruno; Thiel, Johannes
Background: The two-component system (TCS) serves as a common intracellular signal transduction pathway implicated in various processes of plant development and response to abiotic stress. With regard to the important cereal crop barley, only partial information about the occurrence of TCS signaling elements in the genome and putative functions is available. Results: In this study, we identified a total of 67 non-redundant TCS genes from all subgroups of the phosphorelay in the latest barley reference genome. Functional annotation and phylogenetic characterization was combined with a comprehensive gene expression analysis of the signaling components. Expression profiles hint at potential functions in vegetative and reproductive organs and tissue types as well as diverse stress responses. Apparently, a distinct subset of TCS genes revealed a stringent grain-specificity not being expressed elsewhere in the plant. By using laser capture microdissection (LCM)-based transcript analysis of barley grain tissues, we refined expression profiles of selected TCS genes and attributed them to individual cell types within the grain. Distinct TCS elements are exclusively expressed in the different maternal and filial cell types, particularly in the endosperm transfer cell (ETC) region. These genes are deemed to be selected in the domestication process of modern cultivars. Moreover, barley plants transformed with a synthetic sensor ( TCSn::GFP ) showed a high and specific activity in the ETC region of grains monitoring transcriptional output of the signaling system. Conclusions: The results provide comprehensive insights into the TCS gene family in the temperate cereal crop barley and indicate implications in various agronomic traits. The dataset is valuable for future research in different aspects of plant development and will be indispensable not only for barley, but also for other crops of the Poaceae.