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Related Experiment Video

Updated: Aug 5, 2025

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
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huva: A human variation analysis framework to predict gene perturbation from population-scale multi-omics data.

Anna C Aschenbrenner1, Lorenzo Bonaguro2

  • 1Systems Medicine, Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE), 53127 Bonn, Germany.

STAR Protocols
|March 25, 2023
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Summary

This study introduces the human variation (huva) package to analyze gene expression variance in natural populations. It uses multi-omics data and a conditional quasi loss- and gain-of-function method to understand gene function and phenotype relationships.

Keywords:
BioinformaticsGene ExpressionImmunologyRNAseqSystems Biology

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Area of Science:

  • Genomics
  • Bioinformatics
  • Population Genetics

Background:

  • Gene expression variance is a fundamental aspect of natural populations.
  • Understanding this variance is crucial for inferring gene function and its relation to phenotypes.
  • Existing methods may not fully leverage population-scale multi-omics data for this analysis.

Purpose of the Study:

  • To present a novel protocol for analyzing gene expression variance.
  • To introduce the human variation (huva) package for this analysis.
  • To detail the steps for utilizing population-scale multi-omics data to infer gene function and phenotype-gene expression relationships.

Main Methods:

  • Conditional quasi loss- and gain-of-function approach.
  • Utilizing population-scale multi-omics data.
  • Development and application of the huva package.

Main Results:

  • The huva package facilitates the analysis of gene expression variance.
  • The protocol enables inference of gene function.
  • The relationship between phenotype and gene expression can be elucidated.

Conclusions:

  • The presented protocol and huva package offer a robust method for analyzing gene expression variance.
  • This approach enhances the understanding of gene function within natural populations.
  • It provides a framework for connecting genotype to phenotype through gene expression analysis.