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Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while...
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Updated: Jun 16, 2025

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CADET: Enhanced transcriptome-wide association analyses in admixed samples using eQTL summary data.

S Taylor Head1, Qile Dai1, Joellen Schildkraut2

  • 1Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, GA 30322, USA.

American Journal of Human Genetics
|June 14, 2025
PubMed
Summary
This summary is machine-generated.

We developed CADET, a new method for transcriptome-wide association studies (TWAS) in admixed populations. CADET improves gene-trait association testing by using local-ancestry information, outperforming existing methods.

Keywords:
GReXTWASadmixturecomplex traitscross-populationeQTLexpression quantitative trait locilocal ancestrypolygenic scoretranscriptome

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Area of Science:

  • Genetics
  • Statistical Genetics
  • Bioinformatics

Background:

  • Transcriptome-wide association studies (TWAS) identify genes linked to traits using genetically regulated expression (GReX).
  • Standard TWAS methods assume ancestral homogeneity between training and target datasets, limiting their application in admixed populations.
  • Admixed individuals' genomes, a mosaic of ancestral segments, pose challenges for TWAS accuracy and power.

Purpose of the Study:

  • To develop a novel method, CADET, for powerful TWAS in admixed cohorts.
  • To leverage local-ancestry (LA) information and diverse ancestral reference panels for improved GReX prediction.
  • To address the limitations of existing TWAS methods in ancestrally diverse populations.

Main Methods:

  • CADET integrates local-ancestry (LA) information from admixed cohorts with summary-level expression quantitative trait locus (eQTL) data from multiple ancestral reference panels.
  • It employs multiple polygenic risk score models to predict LA-aware GReX components in admixed individuals.
  • Performance was evaluated using simulated data, comparing imputation accuracy, power, and type I error rates against LA-unaware methods.

Main Results:

  • CADET demonstrated optimal performance across various settings, irrespective of the genetic architecture's dependence on ancestry.
  • Simulations confirmed CADET's superior imputation accuracy, power, and type I error control compared to LA-unaware approaches.
  • Application to UK Biobank data identified 18 novel gene-trait associations for blood biochemistry phenotypes, unique to the LA-aware strategy.

Conclusions:

  • CADET provides a powerful and robust framework for conducting TWAS in admixed populations.
  • The method effectively utilizes local-ancestry information to enhance gene-trait association discovery.
  • CADET's findings in the UK Biobank cohort highlight its utility in uncovering biologically relevant genetic associations in diverse populations.