Interaction molecular QTL mapping discovers cellular and environmental modifiers of genetic regulatory effects

Silva Kasela; François Aguet; Sarah Kim-Hellmuth; Brielin C Brown; Daniel C Nachun; Russell P Tracy; Peter Durda; Yongmei Liu; Kent D Taylor; W Craig Johnson; David Van Den Berg; Stacey Gabriel; Namrata Gupta; Joshua D Smith; Thomas W Blackwell; Jerome I Rotter; Kristin G Ardlie; Ani Manichaikul; Stephen S Rich; R Graham Barr; Tuuli Lappalainen

doi:10.1016/j.ajhg.2023.11.013

Interaction molecular QTL mapping discovers cellular and environmental modifiers of genetic regulatory effects

Am J Hum Genet. 2024 Jan 4;111(1):133-149. doi: 10.1016/j.ajhg.2023.11.013.

Authors

Silva Kasela¹, François Aguet², Sarah Kim-Hellmuth³, Brielin C Brown⁴, Daniel C Nachun⁵, Russell P Tracy⁶, Peter Durda⁶, Yongmei Liu⁷, Kent D Taylor⁸, W Craig Johnson⁹, David Van Den Berg¹⁰, Stacey Gabriel², Namrata Gupta², Joshua D Smith¹¹, Thomas W Blackwell¹², Jerome I Rotter⁸, Kristin G Ardlie², Ani Manichaikul¹³, Stephen S Rich¹³, R Graham Barr¹⁴, Tuuli Lappalainen¹⁵

Affiliations

¹ New York Genome Center, New York, NY, USA; Department of Systems Biology, Columbia University, New York, NY, USA. Electronic address: skasela@nygenome.org.
² Broad Institute of MIT and Harvard, Cambridge, MA, USA.
³ New York Genome Center, New York, NY, USA; Department of Pediatrics, Dr. von Hauner Children's Hospital, University Hospital LMU Munich, Munich, Germany; Computational Health Center, Institute of Translational Genomics, Helmholtz Munich, Neuherberg, Germany.
⁴ New York Genome Center, New York, NY, USA; Data Science Institute, Columbia University, New York, NY, USA.
⁵ Department of Pathology, Stanford University, Stanford, CA, USA.
⁶ Pathology and Laboratory Medicine, The University of Vermont, Larner College of Medicine, Burlington, VT, USA.
⁷ Department of Medicine, Duke University, Durham, NC, USA.
⁸ The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA.
⁹ Department of Biostatistics, University of Washington, Seattle, WA, USA.
¹⁰ Department of Population and Public Health Sciences, University of Southern California, Los Angeles, CA, USA.
¹¹ Northwest Genomics Center, University of Washington, Seattle, WA, USA.
¹² Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI, USA.
¹³ Center for Public Health Genomics, University of Virginia, Charlottesville, VA, USA.
¹⁴ Departments of Medicine and Epidemiology, Columbia University Medical Center, New York, NY, USA.
¹⁵ New York Genome Center, New York, NY, USA; Department of Systems Biology, Columbia University, New York, NY, USA; Science for Life Laboratory, Department of Gene Technology, KTH Royal Institute of Technology, Stockholm, Sweden. Electronic address: tlappalainen@nygenome.org.

PMID: 38181730
PMCID: PMC10806864 (available on 2024-07-04)
DOI: 10.1016/j.ajhg.2023.11.013

Abstract

Bulk-tissue molecular quantitative trait loci (QTLs) have been the starting point for interpreting disease-associated variants, and context-specific QTLs show particular relevance for disease. Here, we present the results of mapping interaction QTLs (iQTLs) for cell type, age, and other phenotypic variables in multi-omic, longitudinal data from the blood of individuals of diverse ancestries. By modeling the interaction between genotype and estimated cell-type proportions, we demonstrate that cell-type iQTLs could be considered as proxies for cell-type-specific QTL effects, particularly for the most abundant cell type in the tissue. The interpretation of age iQTLs, however, warrants caution because the moderation effect of age on the genotype and molecular phenotype association could be mediated by changes in cell-type composition. Finally, we show that cell-type iQTLs contribute to cell-type-specific enrichment of diseases that, in combination with additional functional data, could guide future functional studies. Overall, this study highlights the use of iQTLs to gain insights into the context specificity of regulatory effects.

Keywords: DNA methylation; cell-type composition; gene expression; gene-environment interaction; interaction QTL.

MeSH terms

Gene Expression Regulation*
Genotype
Humans
Phenotype
Quantitative Trait Loci* / genetics

Abstract

MeSH terms

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