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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Vydané v:	Nature communications Ročník 12; číslo 1; s. 5647 - 9
Hlavní autori:	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
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	London Nature Publishing Group UK 24.09.2021 Nature Publishing Group Nature Portfolio
Predmet:	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
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Abstract	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 and phenotypes driven by confounders, forward- and reverse causal effects. The bi-directional causal effects between gene expression and complex traits are obtained by Mendelian Randomization integrating summary-level data from GWAS and whole-blood eQTLs. Applying this approach to complex traits reveals that forward effects have negligible contribution. For example, BMI- and triglycerides-gene expression correlation coefficients robustly correlate with trait-to-expression causal effects ( r BMI = 0.11, P BMI = 2.0 × 10 −51 and r TG = 0.13, P TG = 1.1 × 10 −68 ), but not detectably with expression-to-trait effects. Our results demonstrate that studies comparing the transcriptome of diseased and healthy subjects are more prone to reveal disease-induced gene expression changes rather than disease causing ones. Identification of gene expression changes between healthy and diseased individuals can reveal mechanistic insights and biomarkers. Here, the authors propose a bi-directional transcriptome-wide Mendelian Randomization approach to assess causal effects between gene expression and complex traits.
AbstractList	Identification of gene expression changes between healthy and diseased individuals can reveal mechanistic insights and biomarkers. Here, the authors propose a bi-directional transcriptome-wide Mendelian Randomization approach to assess causal effects between gene expression and complex traits. 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 and phenotypes driven by confounders, forward- and reverse causal effects. The bi-directional causal effects between gene expression and complex traits are obtained by Mendelian Randomization integrating summary-level data from GWAS and whole-blood eQTLs. Applying this approach to complex traits reveals that forward effects have negligible contribution. For example, BMI- and triglycerides-gene expression correlation coefficients robustly correlate with trait-to-expression causal effects ( r BMI = 0.11, P BMI = 2.0 × 10 −51 and r TG = 0.13, P TG = 1.1 × 10 −68 ), but not detectably with expression-to-trait effects. Our results demonstrate that studies comparing the transcriptome of diseased and healthy subjects are more prone to reveal disease-induced gene expression changes rather than disease causing ones. Identification of gene expression changes between healthy and diseased individuals can reveal mechanistic insights and biomarkers. Here, the authors propose a bi-directional transcriptome-wide Mendelian Randomization approach to assess causal effects between gene expression and complex traits. 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 and phenotypes driven by confounders, forward- and reverse causal effects. The bi-directional causal effects between gene expression and complex traits are obtained by Mendelian Randomization integrating summary-level data from GWAS and whole-blood eQTLs. Applying this approach to complex traits reveals that forward effects have negligible contribution. For example, BMI- and triglycerides-gene expression correlation coefficients robustly correlate with trait-to-expression causal effects (r = 0.11, P = 2.0 × 10 and r = 0.13, P = 1.1 × 10 ), but not detectably with expression-to-trait effects. Our results demonstrate that studies comparing the transcriptome of diseased and healthy subjects are more prone to reveal disease-induced gene expression changes rather than disease causing ones. 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 and phenotypes driven by confounders, forward- and reverse causal effects. The bi-directional causal effects between gene expression and complex traits are obtained by Mendelian Randomization integrating summary-level data from GWAS and whole-blood eQTLs. Applying this approach to complex traits reveals that forward effects have negligible contribution. For example, BMI- and triglycerides-gene expression correlation coefficients robustly correlate with trait-to-expression causal effects (rBMI = 0.11, PBMI = 2.0 × 10−51 and rTG = 0.13, PTG = 1.1 × 10−68), but not detectably with expression-to-trait effects. Our results demonstrate that studies comparing the transcriptome of diseased and healthy subjects are more prone to reveal disease-induced gene expression changes rather than disease causing ones.Identification of gene expression changes between healthy and diseased individuals can reveal mechanistic insights and biomarkers. Here, the authors propose a bi-directional transcriptome-wide Mendelian Randomization approach to assess causal effects between gene expression and complex traits. 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 and phenotypes driven by confounders, forward- and reverse causal effects. The bi-directional causal effects between gene expression and complex traits are obtained by Mendelian Randomization integrating summary-level data from GWAS and whole-blood eQTLs. Applying this approach to complex traits reveals that forward effects have negligible contribution. For example, BMI- and triglycerides-gene expression correlation coefficients robustly correlate with trait-to-expression causal effects (rBMI = 0.11, PBMI = 2.0 × 10-51 and rTG = 0.13, PTG = 1.1 × 10-68), but not detectably with expression-to-trait effects. Our results demonstrate that studies comparing the transcriptome of diseased and healthy subjects are more prone to reveal disease-induced gene expression changes rather than disease causing ones.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 and phenotypes driven by confounders, forward- and reverse causal effects. The bi-directional causal effects between gene expression and complex traits are obtained by Mendelian Randomization integrating summary-level data from GWAS and whole-blood eQTLs. Applying this approach to complex traits reveals that forward effects have negligible contribution. For example, BMI- and triglycerides-gene expression correlation coefficients robustly correlate with trait-to-expression causal effects (rBMI = 0.11, PBMI = 2.0 × 10-51 and rTG = 0.13, PTG = 1.1 × 10-68), but not detectably with expression-to-trait effects. Our results demonstrate that studies comparing the transcriptome of diseased and healthy subjects are more prone to reveal disease-induced gene expression changes rather than disease causing ones. 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 and phenotypes driven by confounders, forward- and reverse causal effects. The bi-directional causal effects between gene expression and complex traits are obtained by Mendelian Randomization integrating summary-level data from GWAS and whole-blood eQTLs. Applying this approach to complex traits reveals that forward effects have negligible contribution. For example, BMI- and triglycerides-gene expression correlation coefficients robustly correlate with trait-to-expression causal effects ( r BMI = 0.11, P BMI = 2.0 × 10 −51 and r TG = 0.13, P TG = 1.1 × 10 −68 ), but not detectably with expression-to-trait effects. Our results demonstrate that studies comparing the transcriptome of diseased and healthy subjects are more prone to reveal disease-induced gene expression changes rather than disease causing ones.
ArticleNumber	5647
Author	Sadler, Marie C. Reymond, Alexandre Tanaka, Toshiko Weihs, Antoine Bandinelli, Stefania Wood, Andrew R. Frayling, Timothy Auwerx, Chiara Porcu, Eleonora Sleiman, Maroun S. Bou Teumer, Alexander Kutalik, Zoltán Lepik, Kaido Santoni, Federico A. Delaneau, Olivier Ribeiro, Diogo M. Nauck, Matthias Völker, Uwe Metspalu, Andres
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BackLink	https://www.ncbi.nlm.nih.gov/pubmed/34561431$$D View this record in MEDLINE/PubMed
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Snippet	Comparing transcript levels between healthy and diseased individuals allows the identification of differentially expressed genes, which may be causes,... Identification of gene expression changes between healthy and diseased individuals can reveal mechanistic insights and biomarkers. Here, the authors propose a...
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SubjectTerms	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
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Title	Differentially expressed genes reflect disease-induced rather than disease-causing changes in the transcriptome
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