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Sparse Regression Models for Unraveling Group and Individual Associations in eQTL Mapping

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eQTL Analysis

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

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Abstract

As a promising tool for dissecting the genetic basis of common diseases, expression quantitative trait loci (eQTL) study has attracted increasing research interest. Traditional eQTL methods focus on testing the associations between individual single-nucleotide polymorphisms (SNPs) and gene expression traits. A major drawback of this approach is that it cannot model the joint effect of a set of SNPs on a set of genes, which may correspond to biological pathways. To alleviate this limitation, in this chapter, we propose geQTL, a sparse regression method that can detect both group-wise and individual associations between SNPs and expression traits. geQTL can also correct the effects of potential confounders. Our method employs computationally efficient technique, thus it is able to fulfill large scale studies. Moreover, our method can automatically infer the proper number of group-wise associations. We perform extensive experiments on both simulated datasets and yeast datasets to demonstrate the effectiveness and efficiency of the proposed method. The results show that geQTL can effectively detect both individual and group-wise signals and outperform the state-of-the-arts by a large margin. This book chapter well illustrates that decoupling individual and group-wise associations for association mapping is able to improve eQTL mapping accuracy, and inferring individual and group-wise associations.

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Author information

Authors and Affiliations

  1. NEC Laboratories America, Inc., New York, NY, USA

    Wei Cheng

  2. College Information Sciences and Technology, The Pennsylvania State University, University Park, PA, USA

    Xiang Zhang

  3. Department of Computer Science, University of California, Los Angeles, Los Angeles, CA, USA

    Wei Wang

Authors
  1. Wei Cheng
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  2. Xiang Zhang
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  3. Wei Wang
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Corresponding author

Correspondence to Wei Cheng .

Editor information

Editors and Affiliations

  1. Temple University, Philadelphia, PA, USA

    Xinghua Mindy Shi

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Cheng, W., Zhang, X., Wang, W. (2020). Sparse Regression Models for Unraveling Group and Individual Associations in eQTL Mapping. 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_8

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  • DOI: https://doi.org/10.1007/978-1-0716-0026-9_8

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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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