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rvTWAS: identifying gene-trait association using sequences by utilizing transcriptome-directed feature selection.

Jingni He1, Qing Li1, Qingrun Zhang1,2,3,4

  • 1Department of Biochemistry and Molecular Biology, University of Calgary, Calgary T2N 1N4, Canada.

Genetics
|November 24, 2023
PubMed
Summary
This summary is machine-generated.

We developed rare variant TWAS (rvTWAS), a novel method to identify the genetic basis of complex traits by including rare variants. rvTWAS outperforms existing methods and reveals new genes for psychiatric disorders.

Keywords:
Bayesian feature selectiongene–trait association mappingkernel-based feature aggregationrare genetic variantstranscriptome-wide association study

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Area of Science:

  • Genetics
  • Bioinformatics
  • Computational Biology

Background:

  • Transcriptome-wide association study (TWAS) integrates gene expression data for complex trait genetics.
  • Current TWAS methods exclude rare variants due to limitations in predicting gene expression.

Purpose of the Study:

  • To develop a novel method, rare variant TWAS (rvTWAS), that incorporates rare variants into association mapping using gene expression data.
  • To overcome the limitations of existing TWAS protocols that exclude rare variants.

Main Methods:

  • rvTWAS disentangles TWAS into feature selection and aggregation steps, removing the need for expression prediction.
  • Employs a Bayesian model for expression-directed feature selection and a kernel machine for feature aggregation.
  • Validates performance through simulations and real-world data analysis in schizophrenia, bipolar disorder, and autism spectrum disorder.

Main Results:

  • rvTWAS effectively integrates rare variants for association mapping, outperforming existing TWAS protocols.
  • Identified additional genes associated with psychiatric disorders.
  • Proposed a mechanism involving zinc finger genes impacting schizophrenia, bipolar disorder, and autism spectrum disorder through transcriptional regulation.

Conclusions:

  • rvTWAS expands the scope of sequence-based association studies by enabling the integration of gene expression data with rare variants.
  • Provides a powerful new tool for dissecting the genetic architecture of complex traits and diseases.