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Assessing exposure effects on gene expression.
Reifeis, Sarah A; Hudgens, Michael G; Civelek, Mete; Mohlke, Karen L; Love, Michael I.
Afiliación
  • Reifeis SA; Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina.
  • Hudgens MG; Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina.
  • Civelek M; Department of Biomedical Engineering, Center for Public Health Genomics, The University of Virginia, Charlottesville, Virginia.
  • Mohlke KL; Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina.
  • Love MI; Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina.
Genet Epidemiol ; 44(6): 601-610, 2020 09.
Article en En | MEDLINE | ID: mdl-32511796
ABSTRACT
In observational genomics data sets, there is often confounding of the effect of an exposure on gene expression. To adjust for confounding when estimating the exposure effect, a common approach involves including potential confounders as covariates with the exposure in a regression model of gene expression. However, when the exposure and confounders interact to influence gene expression, the fitted regression model does not necessarily estimate the overall effect of the exposure. Using inverse probability weighting (IPW) or the parametric g-formula in these instances is straightforward to apply and yields consistent effect estimates. IPW can readily be integrated into a genomics data analysis pipeline with upstream data processing and normalization, while the g-formula can be implemented by making simple alterations to the regression model. The regression, IPW, and g-formula approaches to exposure effect estimation are compared herein using simulations; advantages and disadvantages of each approach are explored. The methods are applied to a case study estimating the effect of current smoking on gene expression in adipose tissue.
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Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Regulación de la Expresión Génica Tipo de estudio: Diagnostic_studies / Etiology_studies / Incidence_studies / Observational_studies / Prognostic_studies / Risk_factors_studies Límite: Humans Idioma: En Revista: Genet Epidemiol Asunto de la revista: EPIDEMIOLOGIA / GENETICA MEDICA Año: 2020 Tipo del documento: Article

Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Regulación de la Expresión Génica Tipo de estudio: Diagnostic_studies / Etiology_studies / Incidence_studies / Observational_studies / Prognostic_studies / Risk_factors_studies Límite: Humans Idioma: En Revista: Genet Epidemiol Asunto de la revista: EPIDEMIOLOGIA / GENETICA MEDICA Año: 2020 Tipo del documento: Article