ArticleStatistics in biosciences2015
A regularized multivariate regression approach for eQTL analysis.
Article in Statistics in biosciences, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Beyond the single-outcome approach: A comparison of outcome-wide analysis methods for exposome research.Environment international · 2023Article
- Statistical Inference for High-Dimensional Pathway Analysis with Multiple Responses.Computational statistics & data analysis · 2022Article
- Integrative Proteo-genomic Analysis to Construct CNA-protein Regulatory Map in Breast and Ovarian Tumors.Molecular & cellular proteomics : MCP · 2019Article
- Genetic correlations reveal the shared genetic architecture of transcription in human peripheral blood.Nature communications · 2017Article
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Authors and funding
6 authors.
Funding
Abstract
Expression quantitative trait loci (eQTLs) are genomic loci that regulate expression levels of mRNAs or proteins. Understanding these regulatory provides important clues to biological pathways that underlie diseases. In this paper, we propose a new statistical method, GroupRemMap, for identifying eQTLs. We model the relationship between gene expression and single nucleotide variants (SNVs) through multivariate linear regression models, in which gene expression levels are responses and SNV genotypes are predictors. To handle the high-dimensionality as well as to incorporate the intrinsic group structure of SNVs, we introduce a new regularization scheme to (1) control the overall sparsity of the model; (2) encourage the group selection of SNVs from the same gene; and (3) facilitate the detection of trans-hub-eQTLs. We apply the proposed method to the colorectal and breast cancer data sets from The Cancer Genome Atlas (TCGA), and identify several biologically interesting eQTLs. These findings may provide insight into biological processes associated with cancers and generate hypotheses for future studies.
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