Identification of rare disease genes as drivers of common diseases through tissue-specific gene regulatory networks

Genetic variants identified through genome-wide association studies (GWAS) are typically non-coding, exerting small regulatory effects on downstream genes. However, which downstream genes are ultimately impacted and how they confer risk remains mostly unclear. By contrast, variants that cause rare Mendelian diseases are often coding and have a more direct impact on disease development. Here we demonstrate that common and rare genetic diseases can be linked by studying how common disease-associated variants influence gene co-expression in 57 different tissues and cell types. We implemented this method in a framework called Downstreamer and applied it to 88 GWAS traits. We find that predicted downstream "genes" are enriched with Mendelian disease genes, e.g. key genes for height are enriched for genes that cause skeletal abnormalities and Ehlers-Danlos syndromes. 78% of these key genes are located outside of GWAS loci, suggesting that they result from complex trans regulation rather than being impacted by disease-associated variants in cis. Based on our findings, we discuss the challenges in reconstructing gene regulatory networks and provide a roadmap to improve the identification of these highly connected genes linked to common traits and diseases.

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Publication Details

Journal
Scientific Reports
Published
2024-12-04
DOI
https://doi.org/10.1038/s41598-024-80670-1
Citations
2
Primary Topic
Genetic Associations and Epidemiology
Type
article
Field-Weighted Citation Impact
1.10

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article

Identification of rare disease genes as drivers of common diseases through tissue-specific gene regulatory networks

Marie C. Sadler, Iris H. Jonkers, Patrick Deelen, Sophie Mulcahy Symmons et al.
2 citations
Scientific Reports
Genetic Associations and Epidemiology
1.10
article

Identification of rare disease genes as drivers of common diseases through tissue-specific gene regulatory networks

Marie C. Sadler, Iris H. Jonkers, Patrick Deelen, Sophie Mulcahy Symmons, Henry H. Wiersma, Annique Claringbould, Harm-Jan Westra, Floranne Boulogne, Urmo Võsa, Olivier B. Bakker, Zoltán Kutalik, Lude Franke, Carlos G. Urzúa-Traslaviña, Mahmoud Zidan
article en
2 citations

Abstract

Genetic variants identified through genome-wide association studies (GWAS) are typically non-coding, exerting small regulatory effects on downstream genes. However, which downstream genes are ultimately impacted and how they confer risk remains mostly unclear. By contrast, variants that cause rare Mendelian diseases are often coding and have a more direct impact on disease development. Here we demonstrate that common and rare genetic diseases can be linked by studying how common disease-associated variants influence gene co-expression in 57 different tissues and cell types. We implemented this method in a framework called Downstreamer and applied it to 88 GWAS traits. We find that predicted downstream "genes" are enriched with Mendelian disease genes, e.g. key genes for height are enriched for genes that cause skeletal abnormalities and Ehlers-Danlos syndromes. 78% of these key genes are located outside of GWAS loci, suggesting that they result from complex trans regulation rather than being impacted by disease-associated variants in cis. Based on our findings, we discuss the challenges in reconstructing gene regulatory networks and provide a roadmap to improve the identification of these highly connected genes linked to common traits and diseases.

Scientific ReportsVol. 14(1)
SIB Swiss Institute of Bioinformatics (CH), University Medical Center Groningen (NL), University of Groningen (NL), Oncode Institute (NL), University of Tartu (EE), Erasmus University Rotterdam (NL), University of Lausanne (CH)
Rijksuniversiteit Groningen, ZonMw, Nierstichting, Nederlandse Organisatie voor Wetenschappelijk Onderzoek, Universitair Medisch Centrum Groningen, Oncode Institute, European Research Council
Openalex Percentile: Top 18%
Genetic Associations and Epidemiology
1.10
DOIOpen Access PDFOpenAlex
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