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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Vydáno v:Nature communications Ročník 12; číslo 1; s. 5647 - 9
Hlavní autoři: 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:angličtina
Vydáno: London Nature Publishing Group UK 24.09.2021
Nature Publishing Group
Nature Portfolio
Témata:
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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ContentType Journal Article
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2021. The Author(s).
The Author(s) 2021. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
Copyright_xml – notice: The Author(s) 2021
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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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StartPage 5647
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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