Interaction molecular QTL mapping discovers cellular and environmental modifiers of genetic regulatory effectsDepartment of Pathology, Stanford University, Stanford, CA, USA.
Pathology and Laboratory Medicine, The University of Vermont, Larner College of Medicine, Burlington, VT, USA.
Pathology and Laboratory Medicine, The University of Vermont, Larner College of Medicine, Burlington, VT, USA.
Department of Medicine, Duke University, Durham, NC, USA.
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.
Department of Biostatistics, University of Washington, Seattle, WA, USA.
Department of Population and Public Health Sciences, University of Southern California, Los Angeles, CA, USA.
Broad Institute of MIT and Harvard, Cambridge, MA, USA.
Broad Institute of MIT and Harvard, Cambridge, MA, USA.
Northwest Genomics Center, University of Washington, Seattle, WA, USA.
Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI, USA.
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.
Broad Institute of MIT and Harvard, Cambridge, MA, USA.
Center for Public Health Genomics, University of Virginia, Charlottesville, VA, USA.
Center for Public Health Genomics, University of Virginia, Charlottesville, VA, USA.
Departments of Medicine and Epidemiology, Columbia University Medical Center, New York, NY, USA.
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2024 (English)In: American Journal of Human Genetics, ISSN 0002-9297, E-ISSN 1537-6605, Vol. 111, no 1, p. 133-149Article in journal (Refereed) Published
Abstract [en]
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.
Place, publisher, year, edition, pages
Elsevier BV , 2024. Vol. 111, no 1, p. 133-149
Keywords [en]
cell-type composition, DNA methylation, gene expression, gene-environment interaction, interaction QTL
National Category
Medical Genetics and Genomics
Identifiers
URN: urn:nbn:se:kth:diva-342191DOI: 10.1016/j.ajhg.2023.11.013ISI: 001154145300001PubMedID: 38181730Scopus ID: 2-s2.0-85181088634OAI: oai:DiVA.org:kth-342191DiVA, id: diva2:1827889
Note
QC 20240115
2024-01-152024-01-152025-12-05Bibliographically approved