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Types of Genetic Transfer Between Organisms

Types of Genetic Transfer Between Organisms

Genetic transfer occurs when genetic information is passed from one organism to another. It occurs via two mechanisms: vertical gene transfer and horizontal gene transfer. Vertical gene transfer occurs when genetic information is transferred from one generation to the next, which happens much more frequently than horizontal gene transfer. Both sexual and asexual reproduction are forms of vertical gene transfer, where one or more organisms pass some or all of their genome onto their progeny.

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Transferability of Single- and Cross-Tissue Transcriptome Imputation Models Across Ancestry Groups.

Inti Pagnuco¹, Stephen Eyre¹, Magnus Rattray²

¹Centre for Genetics and Genomics Versus Arthritis, Centre for Musculoskeletal Research, Division of Musculoskeletal and Dermatological Sciences, The University of Manchester, Manchester, UK.

Genetic Epidemiology

|January 15, 2025

Summary

Transcriptome-wide association studies (TWAS) rely on accurate gene expression imputation. This study found that imputation models perform best within the same ancestry group, highlighting the need for diverse reference panels to improve TWAS accuracy.

Keywords:

ancestry expression quantitative trait loci imputation tissue transcriptome‐wide association study transferability

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Last Updated: Jul 15, 2026

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Area of Science:

Genetics
Bioinformatics
Population Genetics

Background:

Transcriptome-wide association studies (TWAS) link genetically regulated gene expression to complex traits.
TWAS accuracy depends on imputation models using expression quantitative trait loci (eQTLs).
Existing resources like GTEx are ancestry-biased, potentially limiting TWAS in diverse populations.

Purpose of the Study:

To evaluate the transferability of gene expression imputation models across different ancestry groups.
To compare the performance of tissue-specific (PrediXcan) and cross-tissue (UTMOST) imputation models.
To assess the impact of ancestry and tissue context on eQTL detection.

Main Methods:

Trained PrediXcan and UTMOST models on European ancestry data from GTEx.
Tested model performance on independent European ancestry and African American datasets.
Compared imputation accuracy between models and across ancestry groups.

Main Results:

Both imputation models performed best when trained and tested on the same ancestry group.
The cross-tissue UTMOST model generally outperformed the tissue-specific PrediXcan model.
eQTL detection accuracy is significantly influenced by both ancestry and tissue type.

Conclusions:

Gene expression imputation accuracy varies across ancestry groups.
Developing ancestry-specific reference panels is crucial for improving TWAS.
Addressing ancestry bias in genetic studies enhances understanding of complex trait biology.