Differentially expressed genes reflect disease-induced rather than disease-causing changes in the transcriptome

Comparing transcript levels between healthy and diseased individuals allows the identification of differentially expressed genes, which may be causes, consequences or mere correlates of the disease under scrutiny. We propose a method to decompose the observational correlation between gene expression...

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Veröffentlicht in:	Nature communications Jg. 12; H. 1; S. 5647 - 9
Hauptverfasser:	Porcu, Eleonora, Sadler, Marie C., Lepik, Kaido, Auwerx, Chiara, Wood, Andrew R., Weihs, Antoine, Sleiman, Maroun S. Bou, Ribeiro, Diogo M., Bandinelli, Stefania, Tanaka, Toshiko, Nauck, Matthias, Völker, Uwe, Delaneau, Olivier, Metspalu, Andres, Teumer, Alexander, Frayling, Timothy, Santoni, Federico A., Reymond, Alexandre, Kutalik, Zoltán
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	London Nature Publishing Group UK 24.09.2021 Nature Publishing Group Nature Portfolio
Schlagworte:	38 38/43 45 631/114/2415 631/208/199 631/208/205 Algorithms Biomarkers Causality Correlation coefficient Correlation coefficients Gene expression Gene Expression Profiling - methods Genes Genetic Association Studies - methods Genetic Predisposition to Disease - genetics Genome-Wide Association Study - methods Humanities and Social Sciences Humans Mendelian Randomization Analysis - methods multidisciplinary Phenotype Phenotypes Polymorphism, Single Nucleotide Quantitative Trait Loci - genetics Randomization Science Science (multidisciplinary) Transcription Transcriptome - genetics Transcriptomes Triglycerides
ISSN:	2041-1723, 2041-1723
Online-Zugang:	Volltext
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