Global individual ancestry using principal components for family data

摘要

Studies of human complex diseases and traits associated with candidate genes are potentially vulnerable to bias (confounding) due to population stratification and inbreeding, especially in admixed population. In genome-wide association studies (GWAS) the Principal Components (PCs) method provides a global ancestry value per subject, allowing corrections for population stratification. However, these coefficients are typically estimated assuming unrelated individuals and if family structure is present and it is ignored, such sub-structure may induce artifactual PCs. Extensions of the PCs method have been proposed by Konishi and Rao (1992) taking into account only siblings relatedness and by Oualkacha et al. (2012) taking into account large pedigrees and high dimensional phenotype data. In this work we extended these methods to estimate the global individual ancestry coefficients from PCs derived from different variance components matrix estimators using single nucleotide polymorphisms (SNPs) from two simulated data sets and two real data sets, the GENOA sibship data consisting of European and African American subjects and the Baependi Heart Study consisting of 80 extended Brazilian families, both with genotyping data from Affymetrix 6.0 chip. Our results showed that the family structure plays an important role in the estimation of the global individual ancestry for extended pedigrees but not for sibships.