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GenoExp: a web tool for predicting gene expression levels from single nucleotide polymorphisms.

Ohad Manor1, Eran Segal2

  • 1Department of Computer Science and Applied Mathematics and Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot 76100, Israel.

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Predicting gene expression from single nucleotide polymorphisms (SNPs) is now possible for 232 genes across 14 cell types. A new tool, GenoExp, allows users to explore how their SNPs influence gene expression.

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Area of Science:

  • Genomics
  • Bioinformatics

Background:

  • Understanding the impact of genetic variations like single nucleotide polymorphisms (SNPs) on gene expression is crucial.
  • Previous work established multi-SNP predictive models for gene expression in Lymphoblastoid cell lines (LCL).

Purpose of the Study:

  • To validate and generalize multi-SNP predictive models for gene expression across diverse cell types.
  • To identify a larger set of genes whose expression variability can be explained by SNPs.
  • To develop a user-friendly tool for exploring individual genetic effects on gene expression.

Main Methods:

  • Developed and validated multi-SNP predictive models for gene expression.
  • Extended gene expression prediction to 232 genes across 14 cell types, explaining over 25% of expression variability.
  • Created GenoExp, a web-based tool for users to upload SNP data and predict gene expression levels.

Main Results:

  • Successfully predicted gene expression profiles in independent LCL studies.
  • Expanded the number of predictable genes to 232 across 14 cell types.
  • GenoExp enables personalized prediction of cell-specific gene expression based on individual SNP data.

Conclusions:

  • Multi-SNP models are generalizable and can predict gene expression across various cell types.
  • GenoExp provides a valuable resource for individuals to investigate the functional consequences of their genetic variations on gene expression.
  • The tool facilitates the study of genotype-phenotype relationships at the gene expression level.