Eigenanalysis of SNP data with an identity by descent interpretation

Area of Science:

• Population Genetics
• Statistical Genetics
• Bioinformatics

Background:

• Principal Component Analysis (PCA) is a common tool in Genome-Wide Association Studies (GWAS).
• Understanding PCA results is crucial for inferring demographic history.
• Existing methods often assume linkage equilibrium, which may not hold true for all population data.

Purpose of the Study:

• To provide a novel interpretation of PCA in GWAS using relatedness measures.
• To introduce EIGMIX, an efficient eigenanalysis method for estimating individual ancestral proportions (APs).
• To assess the accuracy of EIGMIX in detecting population structure and inferring genome-wide APs.

Main Methods:

• Interpreting PCA through identity-by-descent (IBD) relatedness measures.
• Developing EIGMIX, a computationally efficient method of moments for estimating APs.
• Applying EIGMIX to large-scale SNP data from HapMap Phase 3 and the Human Genome Diversity Panel.

Main Results:

• An approximately linear relationship was found between individual APs and their PCA projections.
• EIGMIX demonstrated computational efficiency suitable for millions of SNPs.
• Inferred APs using EIGMIX showed consistency with results from the ADMIXTURE program.

Conclusions:

• EIGMIX offers a robust method for inferring genome-wide ancestral proportions.
• The method accurately detects population structure without assuming linkage equilibrium.
• EIGMIX provides a valuable tool for geneticists studying population demographics and ancestry.