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Updated: Dec 24, 2025

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Published on: March 1, 2024

Blood-based multi-tissue gene expression inference with Bayesian ridge regression.

Wenjian Xu¹, Xuanshi Liu¹, Fei Leng¹

¹Beijing Key Laboratory for Genetics of Birth Defects, Beijing Pediatric Research Institute, MOE Key Laboratory of Major Diseases in Children, Genetics and Birth Defects Control Center, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing 100045, China.

Bioinformatics (Oxford, England)

|April 12, 2020

Summary

This summary is machine-generated.

Inferring gene expression in uncollected tissues is now possible using blood data. Our B-GEX method accurately predicts multi-tissue gene expression from a single blood profile, advancing research accessibility.

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Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Gene expression profiling is crucial for research but limited by difficult tissue collection.
Existing methods cannot infer multi-tissue gene expression from a single tissue's profile.
Ethical and practical challenges hinder the collection of certain tissue samples, like brain tissue.

Purpose of the Study:

To develop a computational method for inferring gene expression profiles of multiple unmeasured tissues using only a whole blood gene expression profile.
To overcome the limitations of traditional gene expression profiling methods that require direct tissue samples.

Main Methods:

A Bayesian ridge regression-based method (B-GEX) was developed.
Feature selection was used to extract low-dimensional feature vectors from whole blood gene expression profiles.
Inference models were trained using GTEx RNAseq data from 16 tissues to capture cross-tissue expression correlations.

Main Results:

B-GEX accurately infers gene expression profiles of multiple tissues from blood data.
The method outperforms least square regression, LASSO, and ridge regression in accuracy across most tissues.
B-GEX effectively infers both tissue-specific and non-tissue-specific gene expression levels.
Requires only whole blood expression profile, unlike previous methods needing multiple tissues or genomic features.

Conclusions:

B-GEX provides a novel and effective approach for computationally inferring gene expression in uncollected tissues.
This method enhances the feasibility of gene expression analysis in clinical applications by utilizing accessible blood samples.
B-GEX facilitates deeper insights into gene expression patterns across various tissues without direct sampling.