Evaluating genetic-ancestry inference from single-cell RNA-seq data | JoVE Visualize

Area of Science:

Genomics
Bioinformatics
Population Genetics

Background:

Characterizing donor ancestry in single-cell RNA sequencing (scRNA-seq) is vital for dataset homogeneity, bias reduction, and identifying ancestry-specific regulatory mechanisms.
Missing donor ancestry information in scRNA-seq studies impedes comprehensive analysis and understanding of disease relevance.
Ensuring scRNA-seq datasets represent global human genetic diversity is essential for equitable research outcomes.

Purpose of the Study:

To propose and evaluate a framework for assessing methods to infer genetic ancestry from genetic polymorphisms detected in scRNA-seq data.
To demonstrate the accuracy of established tools like ADMIXTURE for ancestry inference using scRNA-seq data, even with limited polymorphisms and imperfect variant calling.
To infer genetic ancestry for donors in existing scRNA-seq datasets and highlight potential biases in population representation.

Main Methods:

Developed a framework to evaluate genetic ancestry inference methods using genetic polymorphisms from scRNA-seq reads.
Applied widely used bioinformatics tools, such as ADMIXTURE, to analyze scRNA-seq data for ancestry estimation.
Inferred genetic ancestry for 196 donors across four Human Cell Atlas scRNA-seq datasets.

Main Results:

Widely used tools accurately infer genetic ancestry and admixture proportions from scRNA-seq data.
Inference remains robust despite limited genetic polymorphisms and imperfect variant calling inherent in scRNA-seq.
Analysis of Human Cell Atlas datasets revealed a disproportionately high number of donors of European ancestry.

Conclusions:

Genetic ancestry inference from scRNA-seq data is feasible and accurate using existing computational tools.
Current scRNA-seq datasets, including those from the Human Cell Atlas, are heavily skewed towards European ancestry.
Researchers are urged to report genetic ancestry for all donors and prioritize generating more diverse scRNA-seq datasets to enhance representation and reduce bias.