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Interaction molecular QTL mapping discovers cellular and environmental modifiers of genetic regulatory effects.
Kasela, Silva; Aguet, François; Kim-Hellmuth, Sarah; Brown, Brielin C; Nachun, Daniel C; Tracy, Russell P; Durda, Peter; Liu, Yongmei; Taylor, Kent D; Johnson, W Craig; Van Den Berg, David; Gabriel, Stacey; Gupta, Namrata; Smith, Joshua D; Blackwell, Thomas W; Rotter, Jerome I; Ardlie, Kristin G; Manichaikul, Ani; Rich, Stephen S; Barr, R Graham; Lappalainen, Tuuli.
Afiliación
  • Kasela S; New York Genome Center, New York, NY, USA; Department of Systems Biology, Columbia University, New York, NY, USA. Electronic address: skasela@nygenome.org.
  • Aguet F; Broad Institute of MIT and Harvard, Cambridge, MA, USA.
  • Kim-Hellmuth S; 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.
  • Brown BC; New York Genome Center, New York, NY, USA; Data Science Institute, Columbia University, New York, NY, USA.
  • Nachun DC; Department of Pathology, Stanford University, Stanford, CA, USA.
  • Tracy RP; Pathology and Laboratory Medicine, The University of Vermont, Larner College of Medicine, Burlington, VT, USA.
  • Durda P; Pathology and Laboratory Medicine, The University of Vermont, Larner College of Medicine, Burlington, VT, USA.
  • Liu Y; Department of Medicine, Duke University, Durham, NC, USA.
  • Taylor KD; 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.
  • Johnson WC; Department of Biostatistics, University of Washington, Seattle, WA, USA.
  • Van Den Berg D; Department of Population and Public Health Sciences, University of Southern California, Los Angeles, CA, USA.
  • Gabriel S; Broad Institute of MIT and Harvard, Cambridge, MA, USA.
  • Gupta N; Broad Institute of MIT and Harvard, Cambridge, MA, USA.
  • Smith JD; Northwest Genomics Center, University of Washington, Seattle, WA, USA.
  • Blackwell TW; Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI, USA.
  • Rotter JI; 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.
  • Ardlie KG; Broad Institute of MIT and Harvard, Cambridge, MA, USA.
  • Manichaikul A; Center for Public Health Genomics, University of Virginia, Charlottesville, VA, USA.
  • Rich SS; Center for Public Health Genomics, University of Virginia, Charlottesville, VA, USA.
  • Barr RG; Departments of Medicine and Epidemiology, Columbia University Medical Center, New York, NY, USA.
  • Lappalainen T; 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.
Am J Hum Genet ; 111(1): 133-149, 2024 01 04.
Article en En | MEDLINE | ID: mdl-38181730
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.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Regulación de la Expresión Génica / Sitios de Carácter Cuantitativo Tipo de estudio: Prognostic_studies Límite: Humans Idioma: En Revista: Am J Hum Genet Año: 2024 Tipo del documento: Article
Texto completo
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Regulación de la Expresión Génica / Sitios de Carácter Cuantitativo Tipo de estudio: Prognostic_studies Límite: Humans Idioma: En Revista: Am J Hum Genet Año: 2024 Tipo del documento: Article