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- Mähler, Niklas, et al.
(author)
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Leaf shape in Populus tremula is a complex, omnigenic trait
- 2020
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In: Ecology and Evolution. - : John Wiley & Sons. - 2045-7758. ; 10:21, s. 11922-11940
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Journal article (peer-reviewed)abstract
- Leaf shape is a defining feature of how we recognize and classify plant species. Although there is extensive variation in leaf shape within many species, few studies have disentangled the underlying genetic architecture. We characterized the genetic architecture of leaf shape variation in Eurasian aspen (Populus tremula L.) by performing genome‐wide association study (GWAS) for physiognomy traits. To ascertain the roles of identified GWAS candidate genes within the leaf development transcriptional program, we generated RNA‐Seq data that we used to perform gene co‐expression network analyses from a developmental series, which is publicly available within the PlantGenIE resource. We additionally used existing gene expression measurements across the population to analyze GWAS candidate genes in the context of a population‐wide co‐expression network and to identify genes that were differentially expressed between groups of individuals with contrasting leaf shapes. These data were integrated with expression GWAS (eQTL) results to define a set of candidate genes associated with leaf shape variation. Our results identified no clear adaptive link to leaf shape variation and indicate that leaf shape traits are genetically complex, likely determined by numerous small‐effect variations in gene expression. Genes associated with shape variation were peripheral within the population‐wide co‐expression network, were not highly connected within the leaf development co‐expression network, and exhibited signatures of relaxed selection. As such, our results are consistent with the omnigenic model.
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| 3. |
- Schiffthaler, Bastian, 1990-, et al.
(author)
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Seiðr: Efficient calculation of robust ensemble gene networks
- 2023
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In: Heliyon. - : Elsevier. - 2405-8440. ; 9:6
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Journal article (peer-reviewed)abstract
- Gene regulatory and gene co-expression networks are powerful research tools for identifying biological signal within high-dimensional gene expression data. In recent years, research has focused on addressing shortcomings of these techniques with regard to the low signal-to-noise ratio, non-linear interactions and dataset dependent biases of published methods. Furthermore, it has been shown that aggregating networks from multiple methods provides improved results. Despite this, few useable and scalable software tools have been implemented to perform such best-practice analyses. Here, we present Seidr (stylized Seiðr), a software toolkit designed to assist scientists in gene regulatory and gene co-expression network inference. Seidr creates community networks to reduce algorithmic bias and utilizes noise corrected network backboning to prune noisy edges in the networks.Using benchmarks in real-world conditions across three eukaryotic model organisms, Saccharomyces cerevisiae, Drosophila melanogaster, and Arabidopsis thaliana, we show that individual algorithms are biased toward functional evidence for certain gene-gene interactions. We further demonstrate that the community network is less biased, providing robust performance across different standards and comparisons for the model organisms.Finally, we apply Seidr to a network of drought stress in Norway spruce (Picea abies (L.) H. Krast) as an example application in a non-model species. We demonstrate the use of a network inferred using Seidr for identifying key components, communities and suggesting gene function for non-annotated genes.
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