Dataset Information

Deciphering Pleiotropic Signatures of Regulatory SNPs in Zea mays L. Using Multi-Omics Data and Machine Learning Algorithms.

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.

SUBMITTER: Haleem A

PROVIDER: S-EPMC9100765 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature

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Deciphering Pleiotropic Signatures of Regulatory SNPs in <i>Zea mays</i> L. Using Multi-Omics Data and Machine Learning Algorithms.

Haleem Ataul A Klees Selina S Schmitt Armin Otto AO Gültas Mehmet M

International journal of molecular sciences 20220504 9

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 like ...[more]

PMID: 35563516

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Project description:Background: Expression quantitative trait loci (eQTL) studies are a valuable approach for identifying genetic variants correlated with gene expression. However, identifying the causal variants is challenging due to linkage disequilibrium amongst variants in the same haplotype block. In this study, we aim to identify functional SNPs in key regulatory regions that alter transcriptional regulation and thus, potentially impact cellular function. The majority of disease-associated single-nucleotide polymorphisms (SNPs) are located in regulatory regions, which can result in allele-specific binding (ASB) of transcription factors and differential expression of the target gene alleles. Here, we present regSNPs-ASB, a generalized linear model-based approach to accurately identify regulatory SNPs that are located in transcription factor binding sites from ATAC-seq data. Results: Using regSNPs-ASB, we identified 53 regulatory SNPs in human MCF-7 breast cancer cells and 125 regulatory SNPs in human mesenchymal stem cells (MSC). By integrating the regSNPs-ASB output with RNA-seq experimental data and publicly available chromatin interaction data from MCF-7 cells, we found that these 53 regulatory SNPs were associated with 74 potential target genes and that 32 (43%) of these genes showed significant allele-specific expression (ASE). By comparing all of the MCF-7 and MSC regulatory SNPs to the eQTLs in the Genome-Tissue Expression (GTEx) Project database, we found that 30% (16/53) of the regulatory SNPs in MCF-7 and 43% (52/122) of the regulatory SNPs in MSC were also eQTLs. The enrichment of regulatory SNPs in eQTLs indicated that many of them are likely responsible for allelic differences in gene expression (chi-square test, p-value < 0.01). In sum, we conclude that regSNPs-ASB is a useful tool for identifying causal variants from ATAC-seq data. This new computational tool will enable efficient prioritization of genetic variants identified as eQTL for further studies to validate their causal regulatory function. Ultimately, identifying causal genetic variants will further our understanding of the underlying molecular mechanisms of disease and the eventual development of potential therapeutic targets.

Dataset Information

Deciphering Pleiotropic Signatures of Regulatory SNPs in Zea mays L. Using Multi-Omics Data and Machine Learning Algorithms.

Publications

Deciphering Pleiotropic Signatures of Regulatory SNPs in <i>Zea mays</i> L. Using Multi-Omics Data and Machine Learning Algorithms.

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