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Salinity is one of the most common abiotic stresses that limit the production of rice. Since salinity stress tolerance is controlled by many genes, identification of these stress responsive genes as well as to understand the underlying mechanisms is of importance from breeding point of view. In this direction, the reverse engineering of gene regulatory networks has proven to be successful. In this study, we construct the gene regulatory network using Kendall's tau correlation coefficient, in order to identify the stress responsive genes. The proposed approach was tested on a rice microarray dataset and 18 key genes were identified. Most of these key genes were found to be involved directly or indirectly in salinity stress, as evidenced from the published literature. Gene ontology analysis further confirmed the involvement of the selected genes in ion binding, oxidation-reduction and phosphorylation activities. These identified genes can be targeted for breeding salt-tolerant varieties of rice.

Keywords

Correlation Coefficient, Gene Regulatory Networks, Rice, Salinity.

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References

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Identification of cis- and trans-expression quantitative trait loci using Bayesian framework

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Authors

Himadri Shekhar Roy ¹, Amrit Kumar Paul ¹, Ranjit Kumar Paul ¹

Affiliations
1 ICAR-Indian Agricultural Statistics Research Institute, New Delhi 110 012, IN

Source

Current Science, Vol 122, No 10 (2022), Pagination: 1214-1219

Abstract

The detection and identification of expression quantitative trait loci (eQTLs) for biological characteristics like gene expression is an important focus of genomics. The existence of cis- and trans-eQTLs is crucial for establishing their cumulative significance to the desired traits. A crucial aspect of genomics is identifying the cis- and trans-eQTLs that capture substantial changes in the expression of distant genes. The goal of the present study was to use an integrated hierarchical Bayesian model to identify the cis- and trans-eQTLS. Molecular approaches are utilized to categorize just the candidate genes when quantitative trait loci or eQTLs are identified. Variations inside or near the gene are hypothesized to determine the genetic variances that reflect transcript levels. The identification of eQTLs has helped us better understand gene regulation and complex trait analysis. The present study focused on barley crops, and only cis-eQTLs were identified; no additional eQTL hotspots were determined. Mouse gene expressions were used to study trans-eQTLs and substantial cis- and trans-eQTLs, as well as four eQTL hotspots were identified

Keywords

Barley, gene expression, hotspots, integrated hierarchical model, quantitative trait loci.

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