The contribution of genetic determinants of blood gene expression and splicing to molecular phenotypes and health outcomes

Alex Tokolyi^#¹, Elodie Persyn^#^{2

3

4}, Artika P Nath^{4

5}, Katie L Burnham¹, Jonathan Marten², Thomas Vanderstichele¹, Manuel Tardaguila^{1

6}, David Stacey^{2

7

8}, Ben Farr¹, Vivek Iyer¹, Xilin Jiang^{2

3

9}, Samuel A Lambert^{2

3

4

10}, Guillaume Noell¹, Michael A Quail¹, Diana Rajan¹, Scott C Ritchie^{2

3

4

5

10

11}, Benjamin B Sun¹², Scott A J Thurston¹, Yu Xu^{2

3

4}, Christopher D Whelan¹³, Heiko Runz¹², Slavé Petrovski^{14

15}, Daniel J Gaffney^{1

16}, David J Roberts^{17

18}, Emanuele Di Angelantonio^{2

3

6

10

11

19}, James E Peters²⁰, Nicole Soranzo^{1

6}, John Danesh^{1

2

3

10

11

19}, Adam S Butterworth^{2

3

10

11

19}, Michael Inouye^#^{2

3

4

5

10

11}, Emma E Davenport^#¹, Dirk S Paul^#^{21

22

23}

Affiliations

¹ Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK.
² British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK.
³ Victor Phillip Dahdaleh Heart and Lung Research Institute, University of Cambridge, Cambridge, UK.
⁴ Cambridge Baker Systems Genomics Initiative, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK.
⁵ Cambridge Baker Systems Genomics Initiative, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia.
⁶ Human Technopole, Fondazione Human Technopole, Milan, Italy.
⁷ Australian Centre for Precision Health, Unit of Clinical Health Sciences, University of South Australia, Adelaide, South Australia, Australia.
⁸ South Australian Health and Medical Research Institute, Adelaide, South Australia, Australia.
⁹ Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
¹⁰ Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, UK.
¹¹ British Heart Foundation Centre of Research Excellence, University of Cambridge, Cambridge, UK.
¹² Translational Sciences, Research & Development, Biogen, Cambridge, MA, USA.
¹³ Neuroscience Data Science, Janssen Research & Development, Cambridge, MA, USA.
¹⁴ Centre for Genomics Research, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge, UK.
¹⁵ Department of Medicine, University of Melbourne, Austin Health, Melbourne, Victoria, Australia.
¹⁶ Genomics, BioMarin Pharmaceutical Inc., Novato, CA, USA.
¹⁷ Radcliffe Department of Medicine, John Radcliffe Hospital, Oxford, UK.
¹⁸ Clinical Services, NHS Blood and Transplant, Oxford Centre, John Radcliffe Hospital, Oxford, UK.
¹⁹ NIHR Blood and Transplant Research Unit in Donor Health and Behaviour, University of Cambridge, Cambridge, UK.
²⁰ Department of Immunology and Inflammation, Imperial College London, London, UK.
²¹ British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK. dirk.paul@astrazeneca.com.
²² Victor Phillip Dahdaleh Heart and Lung Research Institute, University of Cambridge, Cambridge, UK. dirk.paul@astrazeneca.com.
²³ Centre for Genomics Research, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge, UK. dirk.paul@astrazeneca.com.

^# Contributed equally.

PMID: 40038547
PMCID: PMC11906350
DOI: 10.1038/s41588-025-02096-3

The contribution of genetic determinants of blood gene expression and splicing to molecular phenotypes and health outcomes

Alex Tokolyi et al. Nat Genet. 2025 Mar.

. 2025 Mar;57(3):616-625.

doi: 10.1038/s41588-025-02096-3. Epub 2025 Mar 4.

Authors

Affiliations

¹ Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK.
² British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK.
³ Victor Phillip Dahdaleh Heart and Lung Research Institute, University of Cambridge, Cambridge, UK.
⁴ Cambridge Baker Systems Genomics Initiative, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK.
⁵ Cambridge Baker Systems Genomics Initiative, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia.
⁶ Human Technopole, Fondazione Human Technopole, Milan, Italy.
⁷ Australian Centre for Precision Health, Unit of Clinical Health Sciences, University of South Australia, Adelaide, South Australia, Australia.
⁸ South Australian Health and Medical Research Institute, Adelaide, South Australia, Australia.
⁹ Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
¹⁰ Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, UK.
¹¹ British Heart Foundation Centre of Research Excellence, University of Cambridge, Cambridge, UK.
¹² Translational Sciences, Research & Development, Biogen, Cambridge, MA, USA.
¹³ Neuroscience Data Science, Janssen Research & Development, Cambridge, MA, USA.
¹⁴ Centre for Genomics Research, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge, UK.
¹⁵ Department of Medicine, University of Melbourne, Austin Health, Melbourne, Victoria, Australia.
¹⁶ Genomics, BioMarin Pharmaceutical Inc., Novato, CA, USA.
¹⁷ Radcliffe Department of Medicine, John Radcliffe Hospital, Oxford, UK.
¹⁸ Clinical Services, NHS Blood and Transplant, Oxford Centre, John Radcliffe Hospital, Oxford, UK.
¹⁹ NIHR Blood and Transplant Research Unit in Donor Health and Behaviour, University of Cambridge, Cambridge, UK.
²⁰ Department of Immunology and Inflammation, Imperial College London, London, UK.
²¹ British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK. dirk.paul@astrazeneca.com.
²² Victor Phillip Dahdaleh Heart and Lung Research Institute, University of Cambridge, Cambridge, UK. dirk.paul@astrazeneca.com.
²³ Centre for Genomics Research, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge, UK. dirk.paul@astrazeneca.com.

^# Contributed equally.

PMID: 40038547
PMCID: PMC11906350
DOI: 10.1038/s41588-025-02096-3

Abstract

The biological mechanisms through which most nonprotein-coding genetic variants affect disease risk are unknown. To investigate gene-regulatory mechanisms, we mapped blood gene expression and splicing quantitative trait loci (QTLs) through bulk RNA sequencing in 4,732 participants and integrated protein, metabolite and lipid data from the same individuals. We identified cis-QTLs for the expression of 17,233 genes and 29,514 splicing events (in 6,853 genes). Colocalization analyses revealed 3,430 proteomic and metabolomic traits with a shared association signal with either gene expression or splicing. We quantified the relative contribution of the genetic effects at loci with shared etiology, observing 222 molecular phenotypes significantly mediated by gene expression or splicing. We uncovered gene-regulatory mechanisms at disease loci with therapeutic implications, such as WARS1 in hypertension, IL7R in dermatitis and IFNAR2 in COVID-19. Our study provides an open-access resource on the shared genetic etiology across transcriptional phenotypes, molecular traits and health outcomes in humans ( https://IntervalRNA.org.uk ).

PubMed Disclaimer

Conflict of interest statement

Competing interests: M.A.Q. is on the Key Opinion Leader panel for New England Biolabs. B.B.S. and H.R. are employees and stockholders of Biogen. C.D.W. is an employee and stockholder of Johnson & Johnson. S.P. and D.S.P. are employees and stockholders of AstraZeneca. D.J.G. is an employee and stockholder of BioMarin Pharmaceutical. D.J.R. is an employee of NHSBT. J.E.P. has received hospitality and travel expenses to speak at Olink-sponsored academic meetings (none within the past 5 years). A.S.B. has received grants outside of this work from AstraZeneca, Bayer, Biogen, BioMarin and Sanofi. M.I. is a trustee of the Public Health Genomics Foundation, a member of the Scientific Advisory Board of Open Targets and has a research collaboration with AstraZeneca that is unrelated to this study. The other authors declare no competing interests.

Figures

**Fig. 1. Overview of the multi-omic data available in the INTERVAL study and external cohorts, as well as the main analytical approaches.**
The images depicting ‘genotypes’ (the figure is created with NIAID NIH Bioart) and ‘proteins’ (Protein Data Bank (PDB) code 2F6W) were reproduced from public databases. COVID-19 HGI, COVID-19 host genetics initiative; MAF, minor allele frequency.

**Fig. 2. Genetic influences on gene expression and splicing.**
a, Distribution of lead variants at *cis*-eQTLs and *cis*-sQTLs around the TSS and gene body (normalized to the median gene length of 24 kb). b, Schematic representation of the *trans*-QTL mapping analysis approach and summary of the QTL discovery results. c, Circos plot of the *trans*-splicing of 18 sGenes by the *cis*-eQTL for *QKI*. TES, transcription end site.

**Fig. 3. Colocalization analyses of *cis*-eQTL and *cis*-sQTL with other molecular phenotypes.**
a, Barplot of the percentage of omics traits with a colocalized association signal with a *cis*-eQTL or/and a *cis*-sQTL. b, Network graph of all pairwise colocalization results. Highlighted examples on the right-hand side include *OAS1*, IL6R and *WARS1*.

**Fig. 4. Mediation analyses of molecular phenotypes with transcriptional QTLs.**
a, Schematic representation of the tested mediation model, for which eQTL and sQTL phenotypes mediate the relationship between genomic variants and levels of molecular phenotypes. The images depicting ‘independent genomic variants’ (the figure is created with NIAID NIH Bioart) and ‘molecular phenotypes’ (PDB code 2F6W) were reproduced from public databases. b, Total number of detected molecular phenotypes mediated by sQTLs and eQTLs. c, Colocalization of sQTLs excising the transmembrane domains of the interleukin receptors *IL6R* and *IL17RA* and mediation with plasma protein quantities (n = 3,024 for *IL17RA* and n = 3,072 for IL6R). The central point represents the mediation effect estimate. Error bars represent the upper and lower 95% confidence intervals of the estimated effects. d, Schematic representation of the splicing events excising transmembrane domains of the interleukin receptors IL6R and *IL17RA*.

**Fig. 5. Multitrait colocalization of *cis*-eQTLs and *cis*-sQTLs with molecular phenotypes and health outcomes.**
a, Putative pathways and directions of the effect of sQTL signals for IL7R and *WARS1* associated with plasma protein quantity, dermatitis and eczema, and hypertension, respectively. The image depicting ‘soluble protein’ was reproduced from a public database (PDB code 2F6W). b, Gene-level summary of colocalization of *cis*-eQTL and *cis*-sQTL with COVID-19 HGI summary statistics. The red dashed line represents genome-wide significance (P = 5 × 10⁻⁸) and the height toward the center represents the significance of the GWAS association. c, Example of a multitrait colocalization for COVID-19 in *OAS1*, with GWAS summary statistics, *cis*-pQTL, *cis*-eQTL and *cis*-sQTL.

See this image and copyright information in PMC

References

1. Loos, R. J. F. 15 years of genome-wide association studies and no signs of slowing down. Nat. Commun.11, 5900 (2020). - PMC - PubMed
1. Albert, F. W. & Kruglyak, L. The role of regulatory variation in complex traits and disease. Nat. Rev. Genet.16, 197–212 (2015). - PubMed
1. Neumeyer, S., Hemani, G. & Zeggini, E. Strengthening causal inference for complex disease using molecular quantitative trait loci. Trends Mol. Med.26, 232–241 (2020). - PubMed
1. Suhre, K. et al. Human metabolic individuality in biomedical and pharmaceutical research. Nature477, 54–60 (2011). - PMC - PubMed
1. Sun, B. B. et al. Genomic atlas of the human plasma proteome. Nature558, 73–79 (2018). - PMC - PubMed

MeSH terms

Actions
Actions
Actions
Actions
Actions
Actions
Actions
Actions
Actions
Actions

LinkOut - more resources

Full Text Sources
- Nature Publishing Group
- PubMed Central

Save citation to file

Email citation

Add to Collections

Add to My Bibliography

Your saved search

Create a file for external citation management software

Your RSS Feed

The contribution of genetic determinants of blood gene expression and splicing to molecular phenotypes and health outcomes

Affiliations

The contribution of genetic determinants of blood gene expression and splicing to molecular phenotypes and health outcomes

Authors

Affiliations

Abstract

Conflict of interest statement

Figures

References

MeSH terms

LinkOut - more resources

Full Text Sources