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Statistical Approaches for Identifying eQTLs (Expression Quantitative Trait Loci) in Plant and Human Genomes
Department of Mathematical Sciences, The University of Texas at El Paso.
Research Article
International Journal of Science and Research Archive, 2023, 10(02), 1429-1437.
Article DOI: 10.30574/ijsra.2023.10.2.0998
Publication history:
Received on 20 October 2023; revised on 21 December 2023; accepted on 29 December 2023
Abstract:
Expression quantitative trait loci, or eQTLs, are genetic regions that play a crucial role in influencing how genes are expressed, making them a vital tool for connecting genetic makeup to observable traits in both plants and humans. In this review, a thorough overview of the statistical methods used in eQTL mapping, covering everything from traditional linear regression to more sophisticated techniques like mixed linear models, Bayesian inference, hidden confounder correction, multivariate frameworks, and machine learning algorithms were provided. The unique biological and computational hurdles that eQTL studies face in plants, such as polyploidy and genotype-by-environment interactions compared to those in humans, which often grapple with issues like tissue specificity, cell-type diversity, and ethical considerations were pointed out. Emerging trends like integrative multi-omics (including mQTLs and chromQTLs), single-cell eQTL mapping, graph-based genome modeling, and causal inference methods (like Mendelian randomization), all of which are enhancing the resolution and interpretability of eQTL analysis were explored. Additionally, popular software tools such as Matrix eQTL, FastQTL, and TensorQTL, evaluating their scalability, power, and replicability were discussed. As eQTL studies evolve towards greater complexity and clinical relevance, strong statistical modeling will continue to be essential for unraveling regulatory variations across various genomic landscapes.
Keywords:
eQTL; Gene expression; Statistical genetics; Plant genomics; Human genomics; Machine learning; Pan-genome; Causal inference; Regulatory variants
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Copyright © 2023 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0
