sn-spMF: matrix factorization informs tissue-specific genetic regulation of gene expression
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sn-spMF: matrix factorization informs tissue-specific genetic regulation of gene expression Yuan He1 , Surya B. Chhetri2,3 , Marios Arvanitis1,4 , Kaushik Srinivasan5 , François Aguet6 , Kristin G. Ardlie6 , Alvaro N. Barbeira7 , Rodrigo Bonazzola7 , Hae Kyung Im7 , GTEx Consortium, Christopher D. Brown8* and Alexis Battle1,5* *Correspondence: [email protected]; [email protected] Please find the full list of authors in GTEx in Additional file 3 8 Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA 1 Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, 21218, USA Full list of author information is available at the end of the article
Abstract Genetic regulation of gene expression, revealed by expression quantitative trait loci (eQTLs), exhibits complex patterns of tissue-specific effects. Characterization of these patterns may allow us to better understand mechanisms of gene regulation and disease etiology. We develop a constrained matrix factorization model, sn-spMF, to learn patterns of tissue-sharing and apply it to 49 human tissues from the Genotype-Tissue Expression (GTEx) project. The learned factors reflect tissues with known biological similarity and identify transcription factors that may mediate tissue-specific effects. sn-spMF, available at https://github.com/heyuan7676/ts_eQTLs, can be applied to learn biologically interpretable patterns of eQTL tissue-specificity and generate testable mechanistic hypotheses. Keywords: Matrix factorization, Ubiquitous eQTLs, Tissue-specific eQTLs, Transcription factors
Background Understanding the genetic effects on gene expression is essential to characterizing the gene regulatory landscape and provides insights into the molecular basis of phenotypes. Expression quantitative trait locus (eQTL) studies using genotype and gene expression data have demonstrated that the genetic regulation of gene expression is pervasive ([1–5], the GTEx Consortium 2020, in submission). Additionally, numerous studies have leveraged eQTLs to characterize the molecular basis of complex phenotypic variation [6–10]. Tissues in the human body carry out universal cellular processes in addition to performing highly specialized functions, driven in large part by patterns of gene expression in each cell type [11, 12]. Characterizing the tissue-sharing and tissue-specificity of genetic effects on gene expression is therefore critical to understanding how genetic variation
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