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Reporter genes are a type of protein-coding gene that are often tagged to a gene of interest. Once inside a target cell, reporter genes usually produce visually identifiable characteristics like fluorescence and luminescence when expressed along with the gene of interest. Thus, reporter genes “report” the presence or absence of genes of interest in an organism, determine the gene expression pattern, or track the physical location of a DNA segment or protein in the cell.

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Cell Type-specific Gene Expression Profiling in the Mouse Liver

Cell Type-specific Gene Expression Profiling in the Mouse Liver

Published on: September 17, 2019

Imputing Gene Expression in Uncollected Tissues Within and Beyond GTEx.

Jiebiao Wang¹, Eric R Gamazon², Brandon L Pierce¹

¹Department of Public Health Sciences, University of Chicago, Chicago, IL 60637, USA.

American Journal of Human Genetics

|April 5, 2016

Summary

Researchers developed new methods to predict gene expression in human tissues not collected in studies like the Genotype-Tissue Expression (GTEx) project. This advances multi-tissue expression analysis and phenotype-expression correlation detection.

Keywords:

GTEx eQTL multi-tissue imputation tissue-tissue expression-level correlation transcriptome

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Published on: August 31, 2016

Area of Science:

Genomics
Bioinformatics
Systems Biology

Background:

Gene expression varies significantly across human tissue types.
The Genotype-Tissue Expression (GTEx) program collects transcriptome data, but not all tissues are accessible for every individual.
Limited accessibility of certain tissues hinders large-scale multi-tissue gene expression studies.

Purpose of the Study:

To develop and validate novel multi-tissue imputation methods for predicting gene expression in uncollected or inaccessible human tissues.
To enhance the power of detecting phenotype-expression correlations by incorporating imputed gene expression data.
To enable imputation of gene expression levels in inaccessible tissues for non-GTEx studies using GTEx data as a reference.

Main Methods:

Development of advanced multi-tissue imputation algorithms.
Performance evaluation through simulation studies comparing proposed methods with existing imputation techniques.
Analysis of gene expression quantitative trait loci (eQTLs) and tissue-tissue expression-level correlations within GTEx pilot project data.

Main Results:

The proposed multi-tissue imputation methods demonstrate superior performance compared to existing approaches.
Incorporating imputed expression data significantly improves the power to detect phenotype-expression correlations.
Expression levels in inaccessible tissues can be reliably imputed in non-GTEx studies by leveraging GTEx reference data.

Conclusions:

Novel imputation methods effectively predict gene expression in uncollected or inaccessible human tissues.
The approach enhances the utility of existing transcriptome datasets for broader multi-tissue analyses.
This work facilitates more comprehensive gene expression studies, particularly in tissues with limited accessibility.