FastPop: a rapid principal component derived method to infer intercontinental ancestry using genetic data.

Area of Science:

• Genetics
• Bioinformatics
• Population Genetics

Background:

• Genome-wide association studies (GWAS) require accurate identification of subpopulations and intercontinental ancestry.
• Existing tools like Structure and Eigenstrat have limitations in computational efficiency and generalizability of results.

Purpose of the Study:

• To develop an efficient R package, FastPop, for inferring intercontinental ancestry and generating reproducible principal component analysis (PCA) scores.
• To address the computational and generalizability limitations of current ancestry inference software.

Main Methods:

• Developed FastPop, an R package utilizing PCA for ancestry inference.
• Applied FastPop to 2318 SNP markers across European, Asian, and African populations using 505 Hapmap samples and 19,661 additional samples.
• Validated FastPop results against Structure, comparing ancestry scores and computational time.

Main Results:

• FastPop demonstrated high consistency with Structure for ancestry scores (correlations of 0.99, 0.97, 0.99 for European, African, Asian ancestries).
• FastPop achieved significant computational efficiency, inferring ancestry for 19,661 samples in 16 minutes compared to Structure's 21-24 hours.
• Generated PCA scores applicable across multiple studies using SNP weights.

Conclusions:

• FastPop provides an efficient and generalizable method for inferring ancestry in large datasets with two or more intercontinental origins.
• The PCA-derived scores from FastPop ensure consistent ancestry analysis across diverse studies.
• FastPop is a valuable tool for population genetics and GWAS.