Editorial: Functional Genomics in Plant Breeding 2.0

Maghuly, Fatemeh; Sehr, Eva Maria; Saxena, Rachit K.; Konkin, David

doi:10.3390/ijms23136959

Cited by 2 publications

(1 citation statement)

References 18 publications

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“…Further, since the economically important crop traits are mostly quantitative in nature and get highly affected by their immediate environment, such GxE interactions add another level of complexity to breeding programs. The deployment of a multitude of advanced genomics tools in integration with traditional breeding pipelines, however, has made this task achievable in many important crop species ( Maghuly et al., 2022 ). These new genomic tools and technologies including molecular DNA markers, cutting-edge sequencing technologies, high-density genotyping and phenotyping platforms, genome mapping, genome dissection, genomic selection, predictions and editing methods have expedited the breeding of improved varieties ( Sihag et al., 2021 ; Kumar et al., 2021 ; Dhakate et al., 2022 ).…”

Section: Application Of Omics-technologies: Landscape Of Lentil Genom...mentioning

confidence: 99%

The Prospects of gene introgression from crop wild relatives into cultivated lentil for climate change mitigation

et al. 2023

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Crop wild relatives (CWRs), landraces and exotic germplasm are important sources of genetic variability, alien alleles, and useful crop traits that can help mitigate a plethora of abiotic and biotic stresses and crop yield reduction arising due to global climatic changes. In the pulse crop genus Lens, the cultivated varieties have a narrow genetic base due to recurrent selections, genetic bottleneck and linkage drag. The collection and characterization of wild Lens germplasm resources have offered new avenues for the genetic improvement and development of stress-tolerant, climate-resilient lentil varieties with sustainable yield gains to meet future food and nutritional requirements. Most of the lentil breeding traits such as high-yield, adaptation to abiotic stresses and resistance to diseases are quantitative and require the identification of quantitative trait loci (QTLs) for marker assisted selection and breeding. Advances in genetic diversity studies, genome mapping and advanced high-throughput sequencing technologies have helped identify many stress-responsive adaptive genes, quantitative trait loci (QTLs) and other useful crop traits in the CWRs. The recent integration of genomics technologies with plant breeding has resulted in the generation of dense genomic linkage maps, massive global genotyping, large transcriptomic datasets, single nucleotide polymorphisms (SNPs), expressed sequence tags (ESTs) that have advanced lentil genomic research substantially and allowed for the identification of QTLs for marker-assisted selection (MAS) and breeding. Assembly of lentil and its wild species genomes (~4Gbp) opens up newer possibilities for understanding genomic architecture and evolution of this important legume crop. This review highlights the recent strides in the characterization of wild genetic resources for useful alleles, development of high-density genetic maps, high-resolution QTL mapping, genome-wide studies, MAS, genomic selections, new databases and genome assemblies in traditionally bred genus Lens for future crop improvement amidst the impending global climate change.

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Section: Application Of Omics-technologies: Landscape Of Lentil Genom...mentioning

confidence: 99%

The Prospects of gene introgression from crop wild relatives into cultivated lentil for climate change mitigation

et al. 2023

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Phenotype Analysis of Arabidopsis thaliana Based on Optimized Multi-Task Learning

Yuan,

Xu,

Zhai

et al. 2023

Mathematics

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Deep learning techniques play an important role in plant phenotype research, due to their powerful data processing and modeling capabilities. Multi-task learning has been researched for plant phenotype analysis, which can combine different plant traits and allow for a consideration of correlations between multiple phenotypic features for more comprehensive analysis. In this paper, an intelligent and optimized multi-task learning method for the phenotypic analysis of Arabidopsis thaliana is proposed and studied. Based on the VGG16 network, hard parameter sharing and task-dependent uncertainty are used to weight the loss function of each task, allowing parameters associated with genotype classification, leaf number counting, and leaf area prediction tasks to be learned jointly. The experiments were conducted on the Arabidopsis thaliana dataset, and the proposed model achieved weighted classification accuracy, precision, and Fw scores of 96.88%, 97.50%, and 96.74%, respectively. Furthermore, the coefficient of determination R2 values in the leaf number and leaf area regression tasks reached 0.7944 and 0.9787, respectively.

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Editorial: Functional Genomics in Plant Breeding 2.0

Abstract: Scientists agree that the increased human impact on the environment since the 19th century has positioned our planet in a period of rapid and intense change, particularly to our natural ecosystems [...]

Cited by 2 publications

References 18 publications

The Prospects of gene introgression from crop wild relatives into cultivated lentil for climate change mitigation

The Prospects of gene introgression from crop wild relatives into cultivated lentil for climate change mitigation

Phenotype Analysis of Arabidopsis thaliana Based on Optimized Multi-Task Learning

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