Network Analysis Prioritizes DEWAX and ICE1 as the Candidate Genes for Major eQTL Hotspots in Seed Germination of Arabidopsis thaliana

Hartanto, Margi; Joosen, Ronny V.L.; Snoek, L. Basten; Willems, Leo A.J.; Sterken, Mark G.; Ridder, Dick de; Hilhorst, Henk W. M.; Ligterink, Wilco; Nijveen, Harm

doi:10.1534/g3.120.401477

Cited by 7 publications

(19 citation statements)

References 76 publications

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“…Both the predicted isoforms of Serine threonine protein kinase encoding candidate gene ( Os06g0291500 ) had several SNPs that lead to missense mutations. Though the function of this gene is not defined in rice, its homolog in Arabidopsis (At1g53660) is an organic anion transmembrane transporter and has been implicated in seed germination, development and biomass accumulation (Keurentjes et al, 2007 ; Hartanto et al, 2020 ). It may have a potential to cause differential NO 3 − uptake in rice either directly or indirectly.…”

Section: Discussionmentioning

confidence: 99%

Integration of Dual Stress Transcriptomes and Major QTLs from a Pair of Genotypes Contrasting for Drought and Chronic Nitrogen Starvation Identifies Key Stress Responsive Genes in Rice

Sevanthi

Sinha

Sureshkumar

et al. 2021

Rice

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We report here the genome-wide changes resulting from low N (N-W+), low water (N+W-)) and dual stresses (N-W-) in root and shoot tissues of two rice genotypes, namely, IR 64 (IR64) and Nagina 22 (N22), and their association with the QTLs for nitrogen use efficiency. For all the root parameters, except for root length under N-W+, N22 performed better than IR64. Chlorophyll a, b and carotenoid content were higher in IR64 under N+W+ treatment and N-W+ and N+W- stresses; however, under dual stress, N22 had higher chlorophyll b content. While nitrite reductase, glutamate synthase (GS) and citrate synthase assays showed better specific activity in IR64, glutamate dehydrogenase showed better specific activity in N22 under dual stress (N-W-); the other N and C assimilating enzymes showed similar but low specific activities in both the genotypes. A total of 8926 differentially expressed genes (DEGs) were identified compared to optimal (N+W+) condition from across all treatments. While 1174, 698 and 903 DEGs in IR64 roots and 1197, 187 and 781 in N22 roots were identified, nearly double the number of DEGs were found in the shoot tissues; 3357, 1006 and 4005 in IR64 and 4004, 990 and 2143 in N22, under N-W+, N+W- and N-W- treatments, respectively. IR64 and N22 showed differential expression in 15 and 11 N-transporter genes respectively, under one or more stress treatments, out of which four showed differential expression also in N+W- condition. The negative regulators of N- stress, e.g., NIGT1, OsACTPK1 and OsBT were downregulated in IR64 while in N22, OsBT was not downregulated. Overall, N22 performed better under dual stress conditions owing to its better root architecture, chlorophyll and porphyrin synthesis and oxidative stress management. We identified 12 QTLs for seed and straw N content using 253 recombinant inbred lines derived from IR64 and N22 and a 5K SNP array. The QTL hotspot region on chromosome 6 comprised of 61 genes, of which, five were DEGs encoding for UDP-glucuronosyltransferase, serine threonine kinase, anthocyanidin 3-O-glucosyltransferase, and nitrate induced proteins. The DEGs, QTLs and candidate genes reported in this study can serve as a major resource for both rice improvement and functional biology.

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Section: Discussionmentioning

confidence: 99%

Integration of Dual Stress Transcriptomes and Major QTLs from a Pair of Genotypes Contrasting for Drought and Chronic Nitrogen Starvation Identifies Key Stress Responsive Genes in Rice

Sevanthi

Sinha

Sureshkumar

et al. 2021

Rice

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“…Some confirmed/strong-candidate eQTGs are used as positive in QTG-Finder2, and we remove these from the positive instances to be used as validation genes. Meanwhile, genes that were not experimentally validated but are predicted to play a role as eQTG through in silico analysis (e.g., network analysis) are categorized as hypothetical, for example, ICE1 and DEWAX (Hartanto et al 2020). If a gene's ortholog is considered an eQTG in another species, it is categorized as hypothetical-ortholog; for example, NF-YC4 is found as an eQTG in potatoes (van Muijen et al 2016).…”

Section: Selection Of Candidate Eqtl Genes and Independent Validation...mentioning

confidence: 99%

“…Experimental fine mapping to narrow down the region (e.g., in Eshed and Zamir 1995) is costly and laborious. As a result, only a few causal genes have been identified in the thousands of eQTLs that have been mapped for Arabidopsis thaliana, using different populations and experimental conditions (Keurentjes et al 2007;West et al 2007;Cubillos et al 2012;Snoek et al 2012;Lowry et al 2013;Hartanto et al 2020). As an in silico alternative, a prioritization method can help to limit the number of candidate eQTGs for further validation.…”

Section: Introductionmentioning

confidence: 99%

“…Candidates are then usually prioritized based on a network centrality measure, such as degree or closeness centrality (Serin et al 2016). Several candidate eQTGs have been identified in this way, for example, GIGANTEA (Keurentjes et al 2007), ELF3 (Jimenez-Gomez et al 2010), ICE1 , and DEWAX (Hartanto et al 2020). This approach, unfortunately, only works for eQTL hotspots, not for regions that only have a small number of eQTLs.…”

Section: Introductionmentioning

confidence: 99%

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Prioritizing Candidate eQTL Causal Genes in Arabidopsis using Random Forests

Hartanto

Sami

Ridder

et al. 2022

Preprint

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Expression quantitative trait locus (eQTL) mapping has been widely used to study the genetic regulation of gene expression in Arabidopsis thaliana. As a result, a large amount of eQTL data has been generated for this model plant; however, only a few causal eQTL genes have been identified, and experimental validation is costly and laborious. A prioritization method could help speed up the identification of causal eQTL genes. This study extends the machine-learning-based QTG-Finder2 method for prioritizing candidate causal genes in phenotype QTLs to be used for eQTLs by adding gene structure, protein interaction, and gene expression. Independent validation shows that the new algorithm can prioritize sixteen out of twenty-five potential eQTL causal genes within the 20% rank percentile. Several new features are important in prioritizing causal eQTL genes, including the number of protein-protein interactions, unique domains, and introns. Overall, this study provides a foundation for developing computational methods to prioritize candidate eQTL causal genes. The prediction of all genes is available in the AraQTL workbench (https://www.bioinformatics.nl/AraQTL/) to support the identification of gene expression regulators in Arabidopsis.

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“…in Eshed and Zamir 1995 ) is costly and laborious. As a result, only a few causal genes have been identified in the thousands of eQTLs that have been mapped for Arabidopsis thaliana , using different populations and experimental conditions ( Keurentjes et al 2007 ; West et al 2007 ; Cubillos et al 2012 ; Snoek et al 2012 ; Lowry et al 2013 ; Hartanto et al 2020 ). As an in silico alternative, a prioritization method can help to limit the number of candidate eQTGs for further validation.…”

Section: Introductionmentioning

confidence: 99%

Prioritizing candidate eQTL causal genes in Arabidopsis using RANDOM FORESTS

Hartanto

Sami

Ridder

et al. 2022

G3 Genes|Genomes|Genetics

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Expression quantitative trait locus (eQTL) mapping has been widely used to study the genetic regulation of gene expression in Arabidopsis thaliana. As a result, a large amount of eQTL data has been generated for this model plant; however, only a few causal eQTL genes have been identified, and experimental validation is costly and laborious. A prioritization method could help speed up the identification of causal eQTL genes. This study extends the machine-learning-based QTG-Finder2 method for prioritizing candidate causal genes in phenotype QTLs to be used for eQTLs by adding gene structure, protein interaction, and gene expression. Independent validation shows that the new algorithm can prioritize sixteen out of twenty-five potential eQTL causal genes within the top 20% rank. Several new features are important in prioritizing causal eQTL genes, including the number of protein-protein interactions, unique domains, and introns. Overall, this study provides a foundation for developing computational methods to prioritize candidate eQTL causal genes. The prediction of all genes is available in the AraQTL workbench (https://www.bioinformatics.nl/AraQTL/) to support the identification of gene expression regulators in Arabidopsis.

show abstract

Network Analysis Prioritizes DEWAX and ICE1 as the Candidate Genes for Major eQTL Hotspots in Seed Germination of Arabidopsis thaliana

Cited by 7 publications

References 76 publications

Integration of Dual Stress Transcriptomes and Major QTLs from a Pair of Genotypes Contrasting for Drought and Chronic Nitrogen Starvation Identifies Key Stress Responsive Genes in Rice

Integration of Dual Stress Transcriptomes and Major QTLs from a Pair of Genotypes Contrasting for Drought and Chronic Nitrogen Starvation Identifies Key Stress Responsive Genes in Rice

Prioritizing Candidate eQTL Causal Genes in Arabidopsis using Random Forests

Prioritizing candidate eQTL causal genes in Arabidopsis using RANDOM FORESTS

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