ArticleBioinformatics (Oxford, England)2013
Accounting for non-genetic factors by low-rank representation and sparse regression for eQTL mapping.
Article in Bioinformatics (Oxford, England), 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- Article
- Interpretable generative deep learning: an illustration with single cell gene expression data.Human genetics · 2022Review
- AC-PCoA: Adjustment for confounding factors using principal coordinate analysis.PLoS computational biology · 2022Article
- UNIFYING AND GENERALIZING METHODS FOR REMOVING UNWANTED VARIATION BASED ON NEGATIVE CONTROLS.Statistica Sinica · 2021Article
- LORSEN: Fast and Efficient eQTL Mapping With Low Rank Penalized Regression.Frontiers in genetics · 2021Article
- Effective SNP ranking improves the performance of eQTL mapping.Genetic epidemiology · 2020Article
- Data-based RNA-seq simulations by binomial thinning.BMC bioinformatics · 2020Article
- Controlling for Confounding Effects in Single Cell RNA Sequencing Studies Using both Control and Target Genes.Scientific reports · 2017Article
- An independent component analysis confounding factor correction framework for identifying broad impact expression quantitative trait loci.PLoS computational biology · 2017Article
- svt: Singular Value Thresholding in MATLAB.Journal of statistical software · 2017Article
- Simultaneous dimension reduction and adjustment for confounding variation.Proceedings of the National Academy of Sciences of the United States of America · 2016Article
- Sparse regression models for unraveling group and individual associations in eQTL mapping.BMC bioinformatics · 2016Article
- The role of regulatory variation in complex traits and disease.Nature reviews. Genetics · 2015Review
- Fast and robust group-wise eQTL mapping using sparse graphical models.BMC bioinformatics · 2015Article
- Graph-regularized dual Lasso for robust eQTL mapping.Bioinformatics (Oxford, England) · 2014Article
- HEFT: eQTL analysis of many thousands of expressed genes while simultaneously controlling for hidden factors.Bioinformatics (Oxford, England) · 2014Article
- Using gene expression to improve the power of genome-wide association analysis.Human heredity · 2014Article
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Authors and funding
4 authors.
Funding
Abstract
motivationExpression quantitative trait loci (eQTL) studies investigate how gene expression levels are affected by DNA variants. A major challenge in inferring eQTL is that a number of factors, such as unobserved covariates, experimental artifacts and unknown environmental perturbations, may confound the observed expression levels. This may both mask real associations and lead to spurious association findings.
resultsIn this article, we introduce a LOw-Rank representation to account for confounding factors and make use of Sparse regression for eQTL mapping (LORS). We integrate the low-rank representation and sparse regression into a unified framework, in which single-nucleotide polymorphisms and gene probes can be jointly analyzed. Given the two model parameters, our formulation is a convex optimization problem. We have developed an efficient algorithm to solve this problem and its convergence is guaranteed. We demonstrate its ability to account for non-genetic effects using simulation, and then apply it to two independent real datasets. Our results indicate that LORS is an effective tool to account for non-genetic effects. First, our detected associations show higher consistency between studies than recently proposed methods. Second, we have identified some new hotspots that can not be identified without accounting for non-genetic effects. AVAILABILITY: The software is available at: http://bioinformatics.med.yale.edu/software.aspx. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.