Exome-wide variation in a diverse barley panel reveals genetic associations with ten agronomic traits in Eastern landraces.

Barley (Hordeum vulgare ssp. vulgare) is one of the first crops to be domesticated and is adapted to a wide range of environments. Worldwide barley germplasm collections possess valuable allelic variations that could further improve barley productivity. Although barley genomics has offered a global picture of allelic variation among varieties and its association with various agronomic traits, polymorphisms from East Asian varieties remain scarce. In this study, we analyze exome polymorphisms in a panel of 274 barley varieties collected worldwide, including 137 varieties from East Asian countries and Ethiopia. We reveal the underlying population structure and conduct genome-wide association studies for 10 agronomic traits. Moreover, we examin genome-wide associations for traits related to grain size such as awn length and glume length. Our results demonstrate the value of diverse barley germplasm panels containing Eastern varieties, highlighting their distinct genomic signatures relative to Western subpopulations.

Minimizing the expected value of the asymmetric loss function and an inequality for the variance of the loss.

The coefficients of regression are usually estimated for minimization problems with asymmetric loss functions. In this paper, we rather correct predictions so that the prediction error follows a generalized Gaussian distribution. In our method, we not only minimize the expected value of the asymmetric loss, but also lower the variance of the loss. Predictions usually have errors. Therefore, it is necessary to use predictions in consideration of these errors. Our approach takes into account prediction errors. Furthermore, even if we do not understand the prediction method, which is a possible circumstance in, e.g. deep learning, we can use our method if we know the prediction error distribution and asymmetric loss function. Our method can be applied to procurement of electricity from electricity markets.

Genetic Factors Associated with Heading Responses Revealed by Field Evaluation of 274 Barley Accessions for 20 Seasons.

Heading time is a key trait in cereals affecting the maturation period for optimal grain filling before harvest. Here, we aimed to understand the factors controlling heading time in barley (Hordeum vulgare). We characterized a set of 274 barley accessions collected worldwide by planting them for 20 seasons under different environmental conditions at the same location in Kurashiki, Japan. We examined interactions among accessions, known genetic factors, and an environmental factor to determine the factors controlling heading response. Locally adapted accessions have been selected for genetic factors that stabilize heading responses appropriate for barley cultivation, and these accessions show stable heading responses even under varying environmental conditions. We identified vernalization requirement and PPD-H1 haplotype as major stabilizing mechanisms of the heading response for regional adaptation in Kurashiki.

Decoding Plant-Environment Interactions That Influence Crop Agronomic Traits.

To ensure food security in the face of increasing global demand due to population growth and progressive urbanization, it will be crucial to integrate emerging technologies in multiple disciplines to accelerate overall throughput of gene discovery and crop breeding. Plant agronomic traits often appear during the plants' later growth stages due to the cumulative effects of their lifetime interactions with the environment. Therefore, decoding plant-environment interactions by elucidating plants' temporal physiological responses to environmental changes throughout their lifespans will facilitate the identification of genetic and environmental factors, timing and pathways that influence complex end-point agronomic traits, such as yield. Here, we discuss the expected role of the life-course approach to monitoring plant and crop health status in improving crop productivity by enhancing the understanding of plant-environment interactions. We review recent advances in analytical technologies for monitoring health status in plants based on multi-omics analyses and strategies for integrating heterogeneous datasets from multiple omics areas to identify informative factors associated with traits of interest. In addition, we showcase emerging phenomics techniques that enable the noninvasive and continuous monitoring of plant growth by various means, including three-dimensional phenotyping, plant root phenotyping, implantable/injectable sensors and affordable phenotyping devices. Finally, we present an integrated review of analytical technologies and applications for monitoring plant growth, developed across disciplines, such as plant science, data science and sensors and Internet-of-things technologies, to improve plant productivity.

Parental legacy and regulatory novelty in Brachypodium diurnal transcriptomes accompanying their polyploidy.

Polyploidy is a widespread phenomenon in eukaryotes that can lead to phenotypic novelty and has important implications for evolution and diversification. The modification of phenotypes in polyploids relative to their diploid progenitors may be associated with altered gene expression. However, it is largely unknown how interactions between duplicated genes affect their diurnal expression in allopolyploid species. In this study, we explored parental legacy and hybrid novelty in the transcriptomes of an allopolyploid species and its diploid progenitors. We compared the diurnal transcriptomes of representative Brachypodium cytotypes, including the allotetraploid Brachypodium hybridum and its diploid progenitors Brachypodium distachyon and Brachypodium stacei. We also artificially induced an autotetraploid B. distachyon. We identified patterns of homoeolog expression bias (HEB) across Brachypodium cytotypes and time-dependent gain and loss of HEB in B. hybridum. Furthermore, we established that many genes with diurnal expression experienced HEB, while their expression patterns and peak times were correlated between homoeologs in B. hybridum relative to B. distachyon and B. stacei, suggesting diurnal synchronization of homoeolog expression in B. hybridum. Our findings provide insight into the parental legacy and hybrid novelty associated with polyploidy in Brachypodium, and highlight the evolutionary consequences of diurnal transcriptional regulation that accompanied allopolyploidy.

Computer vision-based phenotyping for improvement of plant productivity: a machine learning perspective.

Employing computer vision to extract useful information from images and videos is becoming a key technique for identifying phenotypic changes in plants. Here, we review the emerging aspects of computer vision for automated plant phenotyping. Recent advances in image analysis empowered by machine learning-based techniques, including convolutional neural network-based modeling, have expanded their application to assist high-throughput plant phenotyping. Combinatorial use of multiple sensors to acquire various spectra has allowed us to noninvasively obtain a series of datasets, including those related to the development and physiological responses of plants throughout their life. Automated phenotyping platforms accelerate the elucidation of gene functions associated with traits in model plants under controlled conditions. Remote sensing techniques with image collection platforms, such as unmanned vehicles and tractors, are also emerging for large-scale field phenotyping for crop breeding and precision agriculture. Computer vision-based phenotyping will play significant roles in both the nowcasting and forecasting of plant traits through modeling of genotype/phenotype relationships.

Statistical and Machine Learning Approaches to Predict Gene Regulatory Networks From Transcriptome Datasets.

Statistical and machine learning (ML)-based methods have recently advanced in construction of gene regulatory network (GRNs) based on high-throughput biological datasets. GRNs underlie almost all cellular phenomena; hence, comprehensive GRN maps are essential tools to elucidate gene function, thereby facilitating the identification and prioritization of candidate genes for functional analysis. High-throughput gene expression datasets have yielded various statistical and ML-based algorithms to infer causal relationship between genes and decipher GRNs. This review summarizes the recent advancements in the computational inference of GRNs, based on large-scale transcriptome sequencing datasets of model plants and crops. We highlight strategies to select contextual genes for GRN inference, and statistical and ML-based methods for inferring GRNs based on transcriptome datasets from plants. Furthermore, we discuss the challenges and opportunities for the elucidation of GRNs based on large-scale datasets obtained from emerging transcriptomic applications, such as from population-scale, single-cell level, and life-course transcriptome analyses.

Diurnal Transcriptome and Gene Network Represented through Sparse Modeling in Brachypodium distachyon.

We report the comprehensive identification of periodic genes and their network inference, based on a gene co-expression analysis and an Auto-Regressive eXogenous (ARX) model with a group smoothly clipped absolute deviation (SCAD) method using a time-series transcriptome dataset in a model grass, Brachypodium distachyon. To reveal the diurnal changes in the transcriptome in B. distachyon, we performed RNA-seq analysis of its leaves sampled through a diurnal cycle of over 48 h at 4 h intervals using three biological replications, and identified 3,621 periodic genes through our wavelet analysis. The expression data are feasible to infer network sparsity based on ARX models. We found that genes involved in biological processes such as transcriptional regulation, protein degradation, and post-transcriptional modification and photosynthesis are significantly enriched in the periodic genes, suggesting that these processes might be regulated by circadian rhythm in B. distachyon. On the basis of the time-series expression patterns of the periodic genes, we constructed a chronological gene co-expression network and identified putative transcription factors encoding genes that might be involved in the time-specific regulatory transcriptional network. Moreover, we inferred a transcriptional network composed of the periodic genes in B. distachyon, aiming to identify genes associated with other genes through variable selection by grouping time points for each gene. Based on the ARX model with the group SCAD regularization using our time-series expression datasets of the periodic genes, we constructed gene networks and found that the networks represent typical scale-free structure. Our findings demonstrate that the diurnal changes in the transcriptome in B. distachyon leaves have a sparse network structure, demonstrating the spatiotemporal gene regulatory network over the cyclic phase transitions in B. distachyon diurnal growth.