ArticleInternational journal of molecular sciences2022
Deciphering Pleiotropic Signatures of Regulatory SNPs in
Article in International journal of molecular sciences, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Omics Technologies in Molecular Biology.International journal of molecular sciences · 2026Article
- Editorial: Utilizing machine learning with phenotypic and genotypic data to enhance effective breeding in agricultural and horticultural crops.Frontiers in plant science · 2026Article
- optRF: Optimising random forest stability by determining the optimal number of trees.BMC bioinformatics · 2025Article
- Genome-wide identification and functional roles relating to anthocyanin biosynthesis analysis in maize.BMC plant biology · 2025Article
- Computational Identification of Milk Trait Regulation Through Transcription Factor Cooperation in Murciano-Granadina Goats.Biology · 2024Article
- Deep learning the cis-regulatory code for gene expression in selected model plants.Nature communications · 2024Article
- Exploring the potential of incremental feature selection to improve genomic prediction accuracy.Genetics, selection, evolution : GSE · 2023Article
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Authors and funding
4 authors.
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Abstract
Maize is one of the most widely grown cereals in the world. However, to address the challenges in maize breeding arising from climatic anomalies, there is a need for developing novel strategies to harness the power of multi-omics technologies. In this regard, pleiotropy is an important genetic phenomenon that can be utilized to simultaneously enhance multiple agronomic phenotypes in maize. In addition to pleiotropy, another aspect is the consideration of the regulatory SNPs (rSNPs) that are likely to have causal effects in phenotypic development. By incorporating both aspects in our study, we performed a systematic analysis based on multi-omics data to reveal the novel pleiotropic signatures of rSNPs in a global maize population. For this purpose, we first applied Random Forests and then Markov clustering algorithms to decipher the pleiotropic signatures of rSNPs, based on which hierarchical network models are constructed to elucidate the complex interplay among transcription factors, rSNPs, and phenotypes. The results obtained in our study could help to understand the genetic programs orchestrating multiple phenotypes and thus could provide novel breeding targets for the simultaneous improvement of several agronomic traits.
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