Evaluating genetic ancestry inference from single-cell transcriptomic datasets.

Area of Science:

• Genomics
• Computational Biology
• Population Genetics

Background:

• Single-cell transcriptomic studies are essential for understanding cellular function and disease.
• Donor genetic ancestry is often missing in these datasets, limiting downstream analyses and introducing potential biases.
• Ensuring genetic homogeneity and diversity in datasets is critical for accurate and representative research.

Purpose of the Study:

• To evaluate computational methods for inferring genetic ancestry from single-cell sequencing data.
• To assess the accuracy of ancestry inference despite limitations in genetic polymorphism data and variant calling.
• To analyze the ancestry composition of existing large-scale single-cell datasets.

Main Methods:

• Framework development for evaluating genetic ancestry inference methods.
• Application of widely used tools (e.g., ADMIXTURE) to single-cell sequencing data.
• Analysis of genetic polymorphisms from single-cell RNA sequencing reads.

Main Results:

• Widely used tools accurately infer genetic ancestry and admixture proportions from single-cell data.
• Inference remains robust despite limited polymorphisms and imperfect variant calling.
• Analysis of ten Human Cell Atlas datasets revealed a high proportion of European ancestry donors.

Conclusions:

• Genetic ancestry inference is feasible and accurate using current computational tools on single-cell sequencing data.
• Existing large-scale datasets may lack diversity, with a predominance of European ancestry donors.
• Researchers should report donor ancestry and strive to generate more diverse datasets.