<i>Amborella</i> gene presence/absence variation is associated with abiotic stress responses that may contribute to environmental adaptation

Hu, Haifei; Scheben, Armin; Verpaalen, Brent; Tirnaz, Soodeh; Bayer, Philipp E.; Hodel, Richard G. J.; Batley, Jacqueline; Soltis, Douglas E.; Soltis, Pamela S.; Edwards, David

doi:10.1111/nph.17658

Cited by 27 publications

(21 citation statements)

References 58 publications

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“…We identified 11,617 (28.9%) dispensable genes across the 413 rice accessions (Additional file 2: Table S6). Annotation suggests that these are enriched for functions associated with protein phosphorylation, telomere maintenance, DNA duplex unwinding, photosynthesis, defence response and pathogenesis (Additional file 2: Table S7), which is similar to the findings in other crop pangenome studies [25, 26]. We observed a significant difference in average gene numbers between Japonica, Indica and Aus (Fig.…”

Section: Resultssupporting

confidence: 86%

“…Additionally, introgression is potentially detected between Indica and Aus subpopulations based on the PAV data, consistent with previous studies showing that Indica accessions contain Aus introgressions [37] and Indica and Aus show closer genetic affinity [38]. The phylogeny variations between SNP and PAV analysis are consistent with observations in other plants such as Arabidopsis [39], Amborella trichopoda [25], green millet Setaria viridis [40] and Brassica oleracea [4], showing that PAV or SV can provide additional information to characterize population structure that might associate with transposon movement during genome evolution, highlighting the value of using PAVs or SVs in addition to SNPs in assessing species evolution.…”

Section: Pavs Provide Insights Into Rice Population Structuresupporting

confidence: 91%

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A pangenome analysis pipeline (PSVCP) provides insights into rice functional gene identification

Wang

Zhang

et al. 2022

Preprint

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Background: A pangenome aims to capture the complete genetic diversity within a species and reduce bias in genetic analysis inherent in using a single reference genome. However, the current linear format of most plant pangenomes limits the presentation of position information for novel sequences. Graph pangenomes have been developed to overcome this limitation. However, there is a lack of bioinformatics analysis tools for graph format genomes. Results: To overcome this problem, we have developed a novel pangenome construction strategy and a downstream pangenome analysis pipeline that captures position information while maintaining a linearized layout. We applied this strategy to construct a high-quality rice pangenome using 12 representative rice genomes and analyze an international rice panel with 413 diverse accessions using the pangenome reference. Our results provide insights into rice population structure and genomic diversity. Applying the pangenome for PAV-based GWAS analysis can identify causal structural variations for rice grain weight and plant height, while SNP-based GWAS can only identify approximate genomic locations. Additionally, a new locus (qPH8-1) was found to be associated with plant height on chromosome 8 that could not be detected using the SNP-based GWAS. Conclusions: Our results demonstrate that the pangenome constructed by our pipeline combined with PAV-based GWAS can provide additional power for genomic and genetic analysis. The pangenome constructed in this study and associated genome sequence data provide valuable genomic resources for future rice crop improvement.

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Section: Resultssupporting

confidence: 86%

Section: Pavs Provide Insights Into Rice Population Structuresupporting

confidence: 91%

A pangenome analysis pipeline (PSVCP) provides insights into rice functional gene identification

Wang

Zhang

et al. 2022

Preprint

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“…Genome sequencing has several applications in plant stress science. The structural analysis of DNA is not only fundamental for classifying organisms but also for identifying stress-driven mutations, which occur in plants under heat [ 46 ], drought [ 47 ], and other abiotic stresses [ 48 ]. Moreover, DNA structural variations occurring under low-dose stress can be linked to gene function using gene ontology analyses to reveal the genetic basis of hormesis [ 49 ].…”

Section: Data In Plant Hormesis Researchmentioning

confidence: 99%

Machine Learning for Plant Stress Modeling: A Perspective towards Hormesis Management

Rico-Chávez

Franco²,

Fernandez‐Jaramillo³

et al. 2022

Plants

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Plant stress is one of the most significant factors affecting plant fitness and, consequently, food production. However, plant stress may also be profitable since it behaves hormetically; at low doses, it stimulates positive traits in crops, such as the synthesis of specialized metabolites and additional stress tolerance. The controlled exposure of crops to low doses of stressors is therefore called hormesis management, and it is a promising method to increase crop productivity and quality. Nevertheless, hormesis management has severe limitations derived from the complexity of plant physiological responses to stress. Many technological advances assist plant stress science in overcoming such limitations, which results in extensive datasets originating from the multiple layers of the plant defensive response. For that reason, artificial intelligence tools, particularly Machine Learning (ML) and Deep Learning (DL), have become crucial for processing and interpreting data to accurately model plant stress responses such as genomic variation, gene and protein expression, and metabolite biosynthesis. In this review, we discuss the most recent ML and DL applications in plant stress science, focusing on their potential for improving the development of hormesis management protocols.

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“…Structural variation represented in pangenomes has been linked with pathogen resistance and tolerance to abiotic stress [ 32 , 113 , 114 ]. Identifying advantageous genes and alleles relies on associating pangenome SVs with phenotypic traits through genome-wide association studies (GWAS), quantitative trait loci (QTL) mapping or genomic selection [ 36 , 115 , 116 ].…”

Section: The Breeding Potential Of Under-utilised Crop Speciesmentioning

confidence: 99%

Pangenomes as a Resource to Accelerate Breeding of Under-Utilised Crop Species

Fernandez

Nestor

Danilevicz

et al. 2022

IJMS

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Pangenomes are a rich resource to examine the genomic variation observed within a species or genera, supporting population genetics studies, with applications for the improvement of crop traits. Major crop species such as maize (Zea mays), rice (Oryza sativa), Brassica (Brassica spp.), and soybean (Glycine max) have had pangenomes constructed and released, and this has led to the discovery of valuable genes associated with disease resistance and yield components. However, pangenome data are not available for many less prominent crop species that are currently under-utilised. Despite many under-utilised species being important food sources in regional populations, the scarcity of genomic data for these species hinders their improvement. Here, we assess several under-utilised crops and review the pangenome approaches that could be used to build resources for their improvement. Many of these under-utilised crops are cultivated in arid or semi-arid environments, suggesting that novel genes related to drought tolerance may be identified and used for introgression into related major crop species. In addition, we discuss how previously collected data could be used to enrich pangenome functional analysis in genome-wide association studies (GWAS) based on studies in major crops. Considering the technological advances in genome sequencing, pangenome references for under-utilised species are becoming more obtainable, offering the opportunity to identify novel genes related to agro-morphological traits in these species.

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Amborella gene presence/absence variation is associated with abiotic stress responses that may contribute to environmental adaptation

Cited by 27 publications

References 58 publications

A pangenome analysis pipeline (PSVCP) provides insights into rice functional gene identification

A pangenome analysis pipeline (PSVCP) provides insights into rice functional gene identification

Machine Learning for Plant Stress Modeling: A Perspective towards Hormesis Management

Pangenomes as a Resource to Accelerate Breeding of Under-Utilised Crop Species

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