ArticlePloS one2013
An information-theoretic machine learning approach to expression QTL analysis.
Article in PloS one, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed, 29 citations in OpenAlex.
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- Identification and Analysis of Glioblastoma Biomarkers Based on Single Cell Sequencing.Frontiers in bioengineering and biotechnology · 2020Article
- The Functional Effects of Key Driver KRAS Mutations on Gene Expression in Lung Cancer.Frontiers in genetics · 2020Article
- Multi-omics integrative analysis identified SNP-methylation-mRNA: Interaction in peripheral blood mononuclear cells.Journal of cellular and molecular medicine · 2019Article
- Network analysis of aging acceleration reveals systematic properties of 11 types of cancers.FEBS open bio · 2019Article
- The early detection of asthma based on blood gene expression.Molecular biology reports · 2019Article
- The Gene Expression Biomarkers for Chronic Obstructive Pulmonary Disease and Interstitial Lung Disease.Frontiers in genetics · 2019Article
- The transcriptome difference between colorectal tumor and normal tissues revealed by single-cell sequencing.Journal of Cancer · 2019Article
- Identification of the predictive genes for the response of colorectal cancer patients to FOLFOX therapy.OncoTargets and therapy · 2018Article
- Identification of tumor-educated platelet biomarkers of non-small-cell lung cancer.OncoTargets and therapy · 2018Article
- Identification and Analysis of Blood Gene Expression Signature for Osteoarthritis With Advanced Feature Selection Methods.Frontiers in genetics · 2018Article
- Integrative multi-omics analysis revealed SNP-lncRNA-mRNA (SLM) networks in human peripheral blood mononuclear cells.Human genetics · 2017Article
- Gene expression elucidates functional impact of polygenic risk for schizophrenia.Nature neuroscience · 2016Article
- Assessment of variation in immunosuppressive pathway genes reveals TGFBR2 to be associated with risk of clear cell ovarian cancer.Oncotarget · 2016Article
- Genetic differences among ethnic groups.BMC genomics · 2015Article
- Discriminating between deleterious and neutral non-frameshifting indels based on protein interaction networks and hybrid properties.Molecular genetics and genomics : MGG · 2015Article
- Prediction of MicroRNA-Disease Associations Based on Social Network Analysis Methods.BioMed research international · 2015Article
- Novel candidate key drivers in the integrative network of genes, microRNAs, methylations, and copy number variations in squamous cell lung carcinoma.BioMed research international · 2015Article
- Nonlinear quantitative radiation sensitivity prediction model based on NCI-60 cancer cell lines.TheScientificWorldJournal · 2014Article
Corrections and comments
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Authors and funding
2 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Expression Quantitative Trait Locus (eQTL) analysis is a powerful tool to study the biological mechanisms linking the genotype with gene expression. Such analyses can identify genomic locations where genotypic variants influence the expression of genes, both in close proximity to the variant (cis-eQTL), and on other chromosomes (trans-eQTL). Many traditional eQTL methods are based on a linear regression model. In this study, we propose a novel method by which to identify eQTL associations with information theory and machine learning approaches. Mutual Information (MI) is used to describe the association between genetic marker and gene expression. MI can detect both linear and non-linear associations. What's more, it can capture the heterogeneity of the population. Advanced feature selection methods, Maximum Relevance Minimum Redundancy (mRMR) and Incremental Feature Selection (IFS), were applied to optimize the selection of the affected genes by the genetic marker. When we applied our method to a study of apoE-deficient mice, it was found that the cis-acting eQTLs are stronger than trans-acting eQTLs but there are more trans-acting eQTLs than cis-acting eQTLs. We compared our results (mRMR.eQTL) with R/qtl, and MatrixEQTL (modelLINEAR and modelANOVA). In female mice, 67.9% of mRMR.eQTL results can be confirmed by at least two other methods while only 14.4% of R/qtl result can be confirmed by at least two other methods. In male mice, 74.1% of mRMR.eQTL results can be confirmed by at least two other methods while only 18.2% of R/qtl result can be confirmed by at least two other methods. Our methods provide a new way to identify the association between genetic markers and gene expression. Our software is available from supporting information.
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