Sparse regression models for unraveling group and individual associations in eQTL mapping

Wei Cheng, Yu Shi, Xiang Zhang, Wei Wang

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

Background: 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. Results: To alleviate this limitation, in this paper, 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 outperforms the state-of-the-arts by a large margin. Conclusions: This paper 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.

Original languageEnglish (US)
Article number136
JournalBMC Bioinformatics
Volume17
Issue number1
DOIs
StatePublished - Mar 22 2016

Fingerprint

Quantitative Trait Loci
Nucleotides
Polymorphism
Regression Model
Single nucleotide Polymorphism
Single Nucleotide Polymorphism
Gene expression
Yeast
Genes
Testing
Decoupling
Margin
Yeasts
Gene Expression
Pathway
Regression
Experiments
Gene
Research
Demonstrate

All Science Journal Classification (ASJC) codes

  • Structural Biology
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Applied Mathematics

Cite this

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abstract = "Background: 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. Results: To alleviate this limitation, in this paper, 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 outperforms the state-of-the-arts by a large margin. Conclusions: This paper 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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Sparse regression models for unraveling group and individual associations in eQTL mapping. / Cheng, Wei; Shi, Yu; Zhang, Xiang; Wang, Wei.

In: BMC Bioinformatics, Vol. 17, No. 1, 136, 22.03.2016.

Research output: Contribution to journalArticle

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