Using Interactome Big Data to Crack Genetic Mysteries and Enhance Future Crop Breeding

Wu, Leiming; Han, Linqian; Li, Qing; Wang, Guoying; Zhang, Hongwei; Li, Lin

doi:10.1016/j.molp.2020.12.012

“…These TEs have already been described as being involved in gene expression, responses to external stimuli and plant development (Kashkush et al, 2003; Matsunaga et al, 2015; Traylor-Knowles et al, 2017; Tran and Choi, 2020). In rubber trees, TEs may be related to the differential expression observed in some commercial clones, affecting important processes such as rubber production (Wu et al, 2020). As pointed out by Wang et al (2020), the identification of TEs associated with functional genes related to important characteristics suggest that they can be used as molecular markers in MAS, contributing significantly to the genetic improvement of woody trees.…”

Section: Discussionmentioning

confidence: 99%

“…We are currently undergoing a major revolution in omics sciences (genomics, transcriptomics, proteomics and phenomics) with different methods for data integration enabling important advances in all phases of genetic improvement, ranging from the discovery of new variants to the understanding of important metabolic pathways (Scossa et al, 2021). The integration of data derived from multiomics can be combined to reveal, in a profound way, the relationships that represent the true biological meaning of the studied elements (Jamil et al, 2020; Wu et al, 2020). This approach has become increasingly common in humans (Wu et al, 2018), animals (Fonseca et al, 2018), microorganisms (Wang et al, 2019), and combinations of species (Pinu et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

“…This approach has become increasingly common in humans (Wu et al, 2018), animals (Fonseca et al, 2018), microorganisms (Wang et al, 2019), and combinations of species (Pinu et al, 2019). However, for plants, such integrated methodologies are still a great challenge, especially for nonmodel species with elevated genetic diversity and complex genomes (Jamil et al, 2020), which is the case for H. brasiliensis (Tang et al, 2016; Liu et al, 2020, Wu et al, 2020). Despite its economic importance, no study incorporating multiomics has been carried out on H. brasiliensis .…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Unravelling Rubber Tree Growth by Integrating GWAS and Biological Network-Based Approaches

Fr

¹

,

Aono

²

,

Silva

³

et al. 2021

Preprint

2

0

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Hevea brasiliensis (rubber tree) is a large tree species of the Euphorbiaceae family with inestimable economic importance. Rubber tree breeding programs currently aim to improve growth and production, and the use of early genotype selection technologies can accelerate such processes, mainly with the incorporation of genomic tools, such as marker-assisted selection (MAS). However, few quantitative trait loci (QTLs) have been used successfully in MAS for complex characteristics. Recent research shows the efficiency of genome-wide association studies (GWAS) for locating QTL regions in different populations. In this way, the integration of GWAS, RNA-sequencing (RNA-Seq) methodologies, coexpression networks and enzyme networks can provide a better understanding of the molecular relationships involved in the definition of the phenotypes of interest, supplying research support for the development of appropriate genomic based strategies for breeding. In this context, this work presents the potential of using combined multiomics to decipher the mechanisms of genotype and phenotype associations involved in the growth of rubber trees. Using GWAS from a genotyping-by-sequencing (GBS) Hevea population, we were able to identify molecular markers in QTL regions with a main effect on rubber tree plant growth under constant water stress. The underlying genes were evaluated and incorporated into a gene coexpression network modelled with an assembled RNA-Seq-based transcriptome of the species, where novel gene relationships were estimated and evaluated through in silico methodologies, including an estimated enzymatic network. From all these analyses, we were able to estimate not only the main genes involved in defining the phenotype but also the interactions between a core of genes related to rubber tree growth at the transcriptional and translational levels. This work was the first to integrate multiomics analysis into the in-depth investigation of rubber tree plant growth, producing useful data for future genetic studies in the species and enhancing the efficiency of the species improvement programs.

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“…In a closer perspective, other "omics" can determine non-genomic markers associated with desired traits, such as grain yield. Aside from the major "omics"-transcriptomics, proteomics, and metabolomics with lipidomics-there are more specialized branches, such as epigenomics, miRNAomics, interactomics [26], phospho-proteomics (global analysis of protein phosphorylation status) [27] and kinomics (activity of the complete set of kinases) [28], that can generate a wealth of information. If "phenotype" refers to the observable physical properties of an organism [29,30], then each of the "omics", besides genomics, describes the phenotype of an organism in its own manner.…”

Section: The Growing Need In Functional Knowledge For Crop Improvementmentioning

confidence: 99%

Metabolomics for Crop Breeding: General Considerations

Litvinov

¹

,

Карлов

²

,

Divashuk

³

2021

Genes

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The development of new, more productive varieties of agricultural crops is becoming an increasingly difficult task. Modern approaches for the identification of beneficial alleles and their use in elite cultivars, such as quantitative trait loci (QTL) mapping and marker-assisted selection (MAS), are effective but insufficient for keeping pace with the improvement of wheat or other crops. Metabolomics is a powerful but underutilized approach that can assist crop breeding. In this review, basic methodological information is summarized, and the current strategies of applications of metabolomics related to crop breeding are explored using recent examples. We briefly describe classes of plant metabolites, cellular localization of metabolic pathways, and the strengths and weaknesses of the main metabolomics technique. Among the commercialized genetically modified crops, about 50 different metabolically altered plants have been identified in the International Service for the Acquisition of Agri-biotech Applications (ISAAA) database. These plants are reviewed as encouraging examples of the application of knowledge of biochemical pathways. Based on the recent examples of metabolomic studies, we discuss the performance of metabolic markers, the integration of metabolic and genomic data in metabolic QTLs (mQTLs) and metabolic genome-wide association studies (mGWAS). The elucidation of metabolic pathways and involved genes will help in crop breeding and the introgression of alleles of wild relatives in a more targeted manner.

show abstract

“…We are currently undergoing a major revolution in omics sciences (genomics, transcriptomics, proteomics, and phenomics) with different methods for data integration enabling important advances in all phases of genetic improvement, ranging from the discovery of new variants to the understanding of important metabolic pathways (Scossa et al, 2021). The integration of data derived from multiomics can be combined to reveal, in a profound way, the relationships that represent the true biological meaning of the studied elements (Jamil et al, 2020;Wu et al, 2020b). This approach has become increasingly common in humans (Wu et al, 2018), animals (Fonseca et al, 2018), microorganisms (Wang et al, 2019), and combinations of species (Pinu et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Unravelling Rubber Tree Growth by Integrating GWAS and Biological Network-Based Approaches

Francisco

¹

,

Aono

²

,

Silva

³

et al. 2021

View full text Add to dashboard Cite

Hevea brasiliensis (rubber tree) is a large tree species of the Euphorbiaceae family with inestimable economic importance. Rubber tree breeding programs currently aim to improve growth and production, and the use of early genotype selection technologies can accelerate such processes, mainly with the incorporation of genomic tools, such as marker-assisted selection (MAS). However, few quantitative trait loci (QTLs) have been used successfully in MAS for complex characteristics. Recent research shows the efficiency of genome-wide association studies (GWAS) for locating QTL regions in different populations. In this way, the integration of GWAS, RNA-sequencing (RNA-Seq) methodologies, coexpression networks and enzyme networks can provide a better understanding of the molecular relationships involved in the definition of the phenotypes of interest, supplying research support for the development of appropriate genomic based strategies for breeding. In this context, this work presents the potential of using combined multiomics to decipher the mechanisms of genotype and phenotype associations involved in the growth of rubber trees. Using GWAS from a genotyping-by-sequencing (GBS) Hevea population, we were able to identify molecular markers in QTL regions with a main effect on rubber tree plant growth under constant water stress. The underlying genes were evaluated and incorporated into a gene coexpression network modelled with an assembled RNA-Seq-based transcriptome of the species, where novel gene relationships were estimated and evaluated through in silico methodologies, including an estimated enzymatic network. From all these analyses, we were able to estimate not only the main genes involved in defining the phenotype but also the interactions between a core of genes related to rubber tree growth at the transcriptional and translational levels. This work was the first to integrate multiomics analysis into the in-depth investigation of rubber tree plant growth, producing useful data for future genetic studies in the species and enhancing the efficiency of the species improvement programs.

show abstract

Using Interactome Big Data to Crack Genetic Mysteries and Enhance Future Crop Breeding

Cited by 38 publications

References 169 publications

Unravelling Rubber Tree Growth by Integrating GWAS and Biological Network-Based Approaches

Unravelling Rubber Tree Growth by Integrating GWAS and Biological Network-Based Approaches

Metabolomics for Crop Breeding: General Considerations

Unravelling Rubber Tree Growth by Integrating GWAS and Biological Network-Based Approaches

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