Integrating transcriptomic network reconstruction and eQTL analyses reveals mechanistic connections between genomic architecture and Brassica rapa development

A spectral framework to map QTLs affecting joint differential networks of gene co-expression

Studying the mechanisms underlying the genotype-phenotype association is crucial in genetics. Gene expression studies have deepened our understanding of the genotype → expression → phenotype mechanisms. However, traditional expression quantitative trait loci (eQTL) methods often overlook the critical role of gene co-expression networks in translating genotype into phenotype. This gap highlights the need for more powerful statistical methods to analyze genotype → network → phenotype mechanism. Here, we develop a network-based method, called snQTL, to map quantitative trait loci affecting gene co-expression networks. Our approach tests the association between genotypes and joint differential networks of gene co-expression via a tensor-based spectral statistics, thereby overcoming the ubiquitous multiple testing challenges in existing methods. We demonstrate the effectiveness of snQTL in the analysis of three-spined stickleback (Gasterosteus aculeatus) data. Compared to conventional methods, our method snQTL uncovers chromosomal regions affecting gene co-expression networks, including one strong candidate gene that would have been missed by traditional eQTL analyses. Our framework suggests the limitation of current approaches and offers a powerful network-based tool for functional loci discoveries.

A low-cost high-throughput phenotyping system for automatically quantifying foliar area and greenness

PREMISE

With modern advances in genetic sequencing technology, plant phenotyping has become a substantial bottleneck in crop improvement programs. Traditionally, researchers have manually measured phenotypic traits to help determine genotype-phenotype relationships, but manual measurements can be time consuming and expensive. Recently, automated phenotyping systems have increased the spatial and temporal density of measurements, but most of these systems are extremely expensive and require specialized expertise. In the present paper, we develop and validate a low-cost, scalable, high-throughput phenotyping (HTP) system for automating the measurement of foliar area and greenness.

METHODS

During a greenhouse experiment on the effects of abiotic stress on Brassica rapa, we collected images of hundreds of plants every hour for over a month with a system that cost approximately US$1000.

RESULTS

In comparison with manually acquired images, this HTP system was able to produce similar estimates of foliar area and greenness, developmental trends, and treatment effects. Foliar area was correlated between the two image sets, but greenness was not.

DISCUSSION

These findings highlight the potential of HTP systems built from low-cost hardware and freely available software. Future work can use this system to investigate genotype-environment interactions and the genetic loci underlying morphological changes resulting from abiotic stress.

Integration of genome-wide association studies and gene coexpression networks unveils promising soybean resistance genes against five common fungal pathogens

Soybean is one of the most important legume crops worldwide. However, soybean yield is dramatically affected by fungal diseases, leading to economic losses of billions of dollars yearly. Here, we integrated publicly available genome-wide association studies and transcriptomic data to prioritize candidate genes associated with resistance to Cadophora gregata, Fusarium graminearum, Fusarium virguliforme, Macrophomina phaseolina, and Phakopsora pachyrhizi. We identified 188, 56, 11, 8, and 3 high-confidence candidates for resistance to F. virguliforme, F. graminearum, C. gregata, M. phaseolina and P. pachyrhizi, respectively. The prioritized candidate genes are highly conserved in the pangenome of cultivated soybeans and are heavily biased towards fungal species-specific defense responses. The vast majority of the prioritized candidate resistance genes are related to plant immunity processes, such as recognition, signaling, oxidative stress, systemic acquired resistance, and physical defense. Based on the number of resistance alleles, we selected the five most resistant accessions against each fungal species in the soybean USDA germplasm. Interestingly, the most resistant accessions do not reach the maximum theoretical resistance potential. Hence, they can be further improved to increase resistance in breeding programs or through genetic engineering. Finally, the coexpression network generated here is available in a user-friendly web application ( https://soyfungigcn.venanciogroup.uenf.br/ ) and an R/Shiny package ( https://github.com/almeidasilvaf/SoyFungiGCN ) that serve as a public resource to explore soybean-pathogenic fungi interactions at the transcriptional level.

Integrated Physiological and Transcriptomic Analyses Reveal a Regulatory Network of Anthocyanin Metabolism Contributing to the Ornamental Value in a Novel Hybrid Cultivar of Camellia Japonica

Camellia japonica is a plant species with great ornamental and gardening values. A novel hybrid cultivar Chunjiang Hongxia (Camellia japonica cv. Chunjiang Hongxia, CH) possesses vivid red leaves from an early growth stage to a prolonged period and is, therefore, commercially valuable. The molecular mechanism underlying this red-leaf phenotype in C. japonica cv. CH is largely unknown. Here, we investigated the leaf coloration process, photosynthetic pigments contents, and different types of anthocyanin compounds in three growth stages of the hybrid cultivar CH and its parental cultivars. The gene co-expression network and differential expression analysis from the transcriptome data indicated that the changes of leaf color were strongly correlated to the anthocyanin metabolic processes in different leaf growth stages. Genes with expression patterns associated with leaf color changes were also discussed. Together, physiological and transcriptomic analyses uncovered the regulatory network of metabolism processes involved in the modulation of the ornamentally valuable red-leaf phenotype and provided the potential candidate genes for future molecular breeding of ornamental plants such as Camellia japonica.

eQTL mapping of the 12S globulin cruciferin gene PGCRURSE5 as a novel candidate associated with starch content in potato tubers

Tuber starch content (TSC) is a very important trait in potato (Solanum tuberosum L.). This study is the first to use expression quantitative trait loci (eQTL) mapping of transcript-derived markers for TSC in potato. Thirty-four differentially expressed genes were selected by comparing the RNA-seq data of contrasting bulked segregants. For the 11 candidate genes, we determined their relative expression levels across the segregating diploid potato population using RT-qPCR. We detected 36 eQTL as candidate genes distributed on all twelve potato chromosomes, and nine of them overlapped with QTL for TSC. Peaks for two eQTL, eAGPaseS-a and ePGRCRURSE5, were close to the corresponding loci of the large subunit of ADP-glucose pyrophosphorylase (AGPaseS-a) and the 12S globulin cruciferin gene (PGCRURSE5), respectively. The eQTL peaks for AGPaseS-a and PGRCRURSE5 explained 41.0 and 28.3% of the phenotypic variation at the transcript level. We showed the association of the DNA markers for AGPaseS-a and PGRCRURSE5 with QTL for TSC, and significant correlation between the expression level of PGRCRURSE5 and TSC. We did not observe a significant correlation between the expression level of AGPaseS-a and TSC. We concluded that the cruciferin gene PGRCRURSE5 is a novel candidate involved in the regulation of starch content in potato tubers.

Integrating High-Throughput Phenotyping and Statistical Genomic Methods to Genetically Improve Longitudinal Traits in Crops

The rapid development of remote sensing in agronomic research allows the dynamic nature of longitudinal traits to be adequately described, which may enhance the genetic improvement of crop efficiency. For traits such as light interception, biomass accumulation, and responses to stressors, the data generated by the various high-throughput phenotyping (HTP) methods requires adequate statistical techniques to evaluate phenotypic records throughout time. As a consequence, information about plant functioning and activation of genes, as well as the interaction of gene networks at different stages of plant development and in response to environmental stimulus can be exploited. In this review, we outline the current analytical approaches in quantitative genetics that are applied to longitudinal traits in crops throughout development, describe the advantages and pitfalls of each approach, and indicate future research directions and opportunities.

Using networks to identify and interpret natural variation

Studies on natural variation and network biology inherently work to summarize vast amounts of information and data. The combination of these two areas of study while creating datasets of immense complexity is critical to their mutual progress. Networks are necessary as a way to work to reduce the dimensionality inherent in natural variation with 100 s to 1000 s of genotypes. Correspondingly natural variation is essential for testing how networks may or may not be shared across individuals or species. Advances in this area of cross-fertilization including using networks directly as phenotypes and the use of networks to help in prioritizing candidate gene validation efforts. Interesting new observations on frequent presence-absence variation in gene content and adaptation is beginning to highlight the potential for natural variation in network presence-absence. This review attempts to delve into these new insights.