Population Genomics and Haplotype Analysis in Bread Wheat Identify a Gene Regulating Glume Pubescence

Genome-wide identification and mining elite allele variation of the Monoacylglycerol lipase (MAGL) gene family in upland cotton (Gossypium hirsutum L.)

BACKGROUND

Monoacylglycerol lipase (MAGL) genes belong to the alpha/beta hydrolase superfamily, catalyze the terminal step of triglyceride (TAG) hydrolysis, converting monoacylglycerol (MAG) into free fatty acids and glycerol.

RESULTS

In this study, 30 MAGL genes in upland cotton have been identified, which have been classified into eight subgroups. The duplication of GhMAGL genes in upland cotton was predominantly influenced by segmental duplication events, as revealed through synteny analysis. Furthermore, all GhMAGL genes were found to contain light-responsive elements. Through comprehensive association and haplotype analyses using resequencing data from 355 cotton accessions, GhMAGL3 and GhMAGL6 were detected as key genes related to lipid hydrolysis processes, suggesting a negative regulatory effect.

CONCLUSIONS

In summary, MAGL has never been studied in upland cotton previously. This study provides the genetic mechanism foundation for the discover of new genes involved in lipid metabolism to improve cottonseed oil content, which will provide a strategic avenue for marker-assisted breeding aimed at incorporating desirable traits into cultivated cotton varieties.

Genome-wide association study for seedling heat tolerance under two temperature conditions in bread wheat (Triticum aestivum L.)

BACKGROUND

As the greenhouse effect intensifies, global temperatures are steadily increasing, posing a challenge to bread wheat (Triticum aestivum L.) production. It is imperative to comprehend the mechanism of high temperature tolerance in wheat and implement breeding programs to identify and develop heat-tolerant wheat germplasm and cultivars.

RESULTS

To identify quantitative trait loci (QTL) related to heat stress tolerance (HST) at seedling stage in wheat, a panel of 253 wheat accessions which were re-sequenced used to conduct genome-wide association studies (GWAS) using the factored spectrally transformed linear mixed models (FaST-LMM). For most accessions, the growth of seedlings was found to be inhibited under heat stress. Analysis of the phenotypic data revealed that under heat stress conditions, the main root length, total root length, and shoot length of seedlings decreased by 47.46%, 49.29%, and 15.19%, respectively, compared to those in normal conditions. However, 17 varieties were identified as heat stress tolerant germplasm. Through GWAS analysis, a total of 115 QTLs were detected under both heat stress and normal conditions. Furthermore, 15 stable QTL-clusters associated with heat response were identified. By combining gene expression, haplotype analysis, and gene annotation information within the physical intervals of the 15 QTL-clusters, two novel candidate genes, TraesCS4B03G0152700/TaWRKY74-B and TraesCS4B03G0501400/TaSnRK3.15-B, were responsive to temperature and identified as potential regulators of HST in wheat at the seedling stage.

CONCLUSIONS

This study conducted a detailed genetic analysis and successfully identified two genes potentially associated with HST in wheat at the seedling stage, laying a foundation to further dissect the regulatory mechanism underlying HST in wheat under high temperature conditions. Our finding could serve as genomic landmarks for wheat breeding aimed at improving adaptation to heat stress in the face of climate change.

Image-based classification of wheat spikes by glume pubescence using convolutional neural networks

Introduction

Pubescence is an important phenotypic trait observed in both vegetative and generative plant organs. Pubescent plants demonstrate increased resistance to various environmental stresses such as drought, low temperatures, and pests. It serves as a significant morphological marker and aids in selecting stress-resistant cultivars, particularly in wheat. In wheat, pubescence is visible on leaves, leaf sheath, glumes and nodes. Regarding glumes, the presence of pubescence plays a pivotal role in its classification. It supplements other spike characteristics, aiding in distinguishing between different varieties within the wheat species. The determination of pubescence typically involves visual analysis by an expert. However, methods without the use of binocular loupe tend to be subjective, while employing additional equipment is labor-intensive. This paper proposes an integrated approach to determine glume pubescence presence in spike images captured under laboratory conditions using a digital camera and convolutional neural networks.

Methods

Initially, image segmentation is conducted to extract the contour of the spike body, followed by cropping of the spike images to an equal size. These images are then classified based on glume pubescence (pubescent/glabrous) using various convolutional neural network architectures (Resnet-18, EfficientNet-B0, and EfficientNet-B1). The networks were trained and tested on a dataset comprising 9,719 spike images.

Results

For segmentation, the U-Net model with EfficientNet-B1 encoder was chosen, achieving the segmentation accuracy IoU = 0.947 for the spike body and 0.777 for awns. The classification model for glume pubescence with the highest performance utilized the EfficientNet-B1 architecture. On the test sample, the model exhibited prediction accuracy parameters of F1 = 0.85 and AUC = 0.96, while on the holdout sample it showed F1 = 0.84 and AUC = 0.89. Additionally, the study investigated the relationship between image scale, artificial distortions, and model prediction performance, revealing that higher magnification and smaller distortions yielded a more accurate prediction of glume pubescence.