ArticleScientific data2023
RNAseq of diverse spring wheat cultivars released during last 110 years.
Article in Scientific data, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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2 citing papers in PubMed, 4 citations in OpenAlex.
- Genome-wide association analysis reveals natural genetic variations controlling canopy architecture traits in bread wheat.Scientific reports · 2026Article
- Integrated meta-QTL and in silico transcriptome assessment pinpoint major genomic regions responsible for spike length in wheat (Triticum aestivum L.).The plant genome · 2024Article
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Authors and funding
14 authors at 5 institutions in 2 countries.
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No grant is acknowledged in the PubMed record.
Abstract
Here, we performed RNA-seq based expression analysis of root and leaf tissues of a set of 24 historical spring wheat cultivars representing 110 years of temporal genetic variations. This huge 130 tissues RNAseq dataset was initially used to study expression pattern of 97 genes regulating root growth and development in wheat. Root system architecture (RSA) is an important target for breeding stress-resilient and high-yielding wheat cultivars under climatic fluctuations. However, root transcriptome analysis is usually obscured due to challenges in root research due to their below ground presence. We also validated the dataset by performing correlation analysis between expression of RSA related genes in roots and leaves with 25 root traits analyzed under varying moisture conditions and 10 yield-related traits. The Pearson's correlation coefficients between root phenotypes and expression of root-specific genes varied from -0.72 to 0.78, and strong correlations with genes such as DRO1, TaMOR, ARF4, PIN1 was observed. The presented datasets have multiple uses such as a) studying the change in expression pattern of genes during time, b) differential expression of genes in two very important tissues of wheat i.e., leaf and roots, and c) studying customized expression of genes associated with important phenotypes in diverse wheat cultivars. The initial findings presented here provided key insights into understanding the transcriptomic basis of phenotypic variability of RSA in wheat cultivars.
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