MULTIVARIATE POLYGENIC MIXED MODEL IN ADMIXED POPULATION (pp.200-209)

Resumo

In genome-wide association studies (GWAS) the Principal Component based Analysis (PCAs) provides a global ancestry value per subject, allowing corrections for population stratification. These coefficients are typically 
estimated assuming unrelated individuals and making use of dual-space properties to prevent high dimensional and sparse matrix problems. However, if family structure is present and is ignored, such sub-structure may induce artifactual 
PCAs. Considering the variable-space in high dimensional data set, extensions of the PCA have been proposed by Konishi and Rao (1992) taking into account only sibship relatedness and by Oualkacha et al. (2012) which can be applied to 
general pedigrees. Further, considering the subject-space, Blangero et al. (2013) obtained an Eigen simplification of the likelihood function from the univariate polygenic mixed model. In this work we propose to apply these methods to 
estimate the global individual ancestry using PCs extracted from different variance components matrix estimators and dual-space properties for subjects and variables. We use the GENOA sibship data consisting of European and African 
American subjects and the Baependi Heart Study consisting of 80 extended families collected from the highly admixture Brazilian population, both with SNPs data from Affymetrix 6.0 chip as applications. All the implementation are done 
using R package.