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Statistical and Machine Learning Methods for eQTL Analysis

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eQTL Analysis

Part of the book series: Methods in Molecular Biology ((MIMB,volume 2082))

Abstract

An immense amount of observable diversity exists for all traits and across global populations. In the post-genomic era, equipped with efficient sequencing capabilities and better genotyping methods, we are now able to more fully appreciate how regulation of gene expression is consequential to one’s genotypes in coding and non-coding DNA. The identification of genetic loci that contribute to quantifiable variation in genetic expression is critical in further improving our understanding of the biological regulation of complex traits. Expression quantitative traits loci (eQTLs) mapping studies have provided a powerful suite of techniques for genome wide analysis to detect these regulatory effects. However, a typical eQTL analysis relies on a large number of samples with many genetic variants to achieve robust power and significance for detection. With this in mind, eQTL analysis brings about distinct computational and statistical challenges that require advanced methodological development to overcome. In recent years, many statistical and machine learning methods for eQTL analysis have been developed with the ability to provide a more complex perspective towards the identification of relationships between genetic variation and genetic expression. In this chapter, we provide a comprehensive review of statistical and machine learning methods. We will present various machine learning methods based upon regularization terms and several other statistical analysis methods. Finally, we will discuss prior knowledge integration and hyperparameter optimization.

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Author information

Authors and Affiliations

  1. Department of Bioinformatics and Genomics, College of Computing and Informatics, University of North Carolina at Charlotte, Charlotte, NC, USA

    Junjie Chen & Conor Nodzak

Authors
  1. Junjie Chen
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  2. Conor Nodzak
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Editor information

Editors and Affiliations

  1. Temple University, Philadelphia, PA, USA

    Xinghua Mindy Shi

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Chen, J., Nodzak, C. (2020). Statistical and Machine Learning Methods for eQTL Analysis. In: Shi, X. (eds) eQTL Analysis. Methods in Molecular Biology, vol 2082. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-0026-9_7

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  • DOI: https://doi.org/10.1007/978-1-0716-0026-9_7

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  • Publisher Name: Humana, New York, NY

  • Print ISBN: 978-1-0716-0025-2

  • Online ISBN: 978-1-0716-0026-9

  • eBook Packages: Springer Protocols

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