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TiSAn: estimating tissue-specific effects of coding and non-coding variants.

Kévin Vervier1, Jacob J Michaelson1

  • 1Department of Psychiatry, Carver College of Medicine, University of Iowa, Iowa City, IA, USA.

Bioinformatics (Oxford, England)
|June 19, 2018
PubMed
Summary
This summary is machine-generated.

TiSAn is a new tool that uses machine learning to identify tissue-specific genetic variants. It improves upon existing methods by integrating multiple data sources, enhancing the interpretation of genetic variations in diseases like autism and coronary artery disease.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • General deleteriousness prediction tools (e.g., CADD, DANN, PolyPhen) are vital for genetic variant interpretation but lack tissue specificity.
  • Existing tissue-specific annotations, derived from epigenomic data, show promise but the benefit of integrating additional tissue-specific genome-scale data remains unclear.

Purpose of the Study:

  • To introduce TiSAn, a novel tool for tissue-specific functional annotation of genetic variants.
  • To evaluate if integrating multiple tissue-specific genome-scale data sources improves variant functional predictions.

Main Methods:

  • TiSAn integrates multiple expert-defined, genome-scale data sources.
  • It employs machine learning to distinguish tissue-relevant variants from non-relevant ones.
  • Predictive models were developed for human heart and brain tissues.

Main Results:

  • TiSAn accurately predicts tissue-specific genetic variations.
  • The tool was applied to large cohorts for autism spectrum disorder (TiSAn-brain) and coronary artery disease (TiSAn-heart).
  • The multiomics TiSAn model demonstrated superior prioritization of tissue-specific genetic variants compared to GenoSkyLine.

Conclusions:

  • TiSAn offers improved accuracy and flexibility for tissue-specific variant annotation.
  • The tool can contextualize and filter variants in whole genome sequencing and GWAS.
  • TiSAn enhances the interpretation of genetic contributions to complex traits in a tissue-specific manner.