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Related Experiment Videos

LungGENIE: the lung gene-expression and network imputation engine.

Auyon J Ghosh1, Liam P Coyne2, Sanchit Panda3

  • 1Division of Pulmonary, Critical Care, and Sleep Medicine, Department of Medicine, SUNY Upstate Medical University, 750 East Adams St, Syracuse, NY, 13210, USA. ghosha@upstate.edu.

BMC Genomics
|March 11, 2025
PubMed
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This summary is machine-generated.

Lung Gene Expression and Network Imputation Engine (LungGENIE) offers a novel, non-invasive method to predict lung gene expression from blood. This tool improves upon existing methods for genomic analysis in lung diseases.

Area of Science:

  • Genomics
  • Bioinformatics
  • Molecular Biology

Background:

  • Limited sample sizes hinder ex vivo lung tissue genomic analysis.
  • Transcriptome imputation presents a non-invasive alternative for molecular analysis.
  • Existing methods for gene expression prediction have limitations.

Purpose of the Study:

  • To introduce Lung Gene Expression and Network Imputation Engine (LungGENIE), a novel transcriptome imputation method.
  • To predict lung tissue-specific gene expression using blood gene-expression data.
  • To provide a tool for genomic analysis in lung disease research.

Main Methods:

  • Utilized paired blood and lung RNA sequencing data from the Genotype-Tissue Expression (GTEx) project for model training.
  • Validated LungGENIE performance using unique paired blood and lung samples from SUNY Upstate Biorepository (SUBR).
Keywords:
Chronic obstructive pulmonary diseaseGene-expressionImputation

Related Experiment Videos

  • Demonstrated proof-of-concept in an independent dataset from the Genetic Epidemiology of COPD (COPDGene) study.

    • Main Results:

    • LungGENIE achieved higher prediction accuracy (median Pearson's r = 0.25) compared to existing cis-expression quantitative trait loci (cis-eQTL) methods.
    • Approximately half of the reliably predicted transcripts were replicated in the testing dataset.
    • Showcased significant correlation between imputed and experimentally determined differential gene expression in COPD.

    Conclusions:

    • LungGENIE offers complementary and superior performance to existing cis-eQTL methods and direct blood-to-lung imputation.
    • The tool demonstrated robust performance across internal validation, external replication, and independent datasets.
    • LungGENIE is established as a valuable tool for studying lung diseases non-invasively.