Global Individual Ancestry Using Principal Components for Family Data.

Area of Science:

• Human genetics
• Population genetics
• Statistical genomics

Background:

• Complex human diseases and traits studies are susceptible to bias from population stratification and inbreeding, particularly in admixed populations.
• Principal Components (PCs) analysis in Genome-Wide Association Studies (GWAS) corrects for population stratification but assumes unrelated individuals, potentially yielding artifactual PCs if family structures are present and ignored.
• Existing extensions of PCs methods account for sibling relatedness or large pedigrees with high-dimensional phenotypes.

Purpose of the Study:

• To extend existing methods for estimating global individual ancestry coefficients.
• To evaluate the impact of family structure on ancestry estimation using different variance component matrix estimators.
• To assess the performance of these extended methods on simulated and real genetic datasets.

Main Methods:

• Utilized Single Nucleotide Polymorphisms (SNPs) from simulated and real datasets (GENOA sibship data, Baependi Heart Study).
• Applied extended Principal Components (PCs) methods incorporating variance component matrix estimators to account for family structures.
• Estimated global individual ancestry coefficients and compared results between sibships and extended pedigrees.

Main Results:

• Family structure significantly influences the estimation of global individual ancestry values in extended pedigrees.
• The impact of family structure on global individual ancestry estimation was not significant for sibships.
• The study demonstrated the utility of extended PCs methods across diverse genetic datasets.

Conclusions:

• Family structure is a critical factor to consider when estimating global individual ancestry, especially in large, complex pedigrees.
• The developed methods provide more accurate ancestry estimations by accounting for familial relatedness.
• These findings have implications for reducing bias in genetic association studies of complex diseases.