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Abstract:
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The issue of robustness to family relationships in computing genotype ancestry scores such as eigenvector projections has received increased attention in genetic association, as the scores are widely used to control spurious association. We propose novel methods to obtain ancestry scores with mixed sets of related and unrelated individuals, and demonstrate that the proposed methods outperform existing methods. We consider in turn strategies as follows: (i) within-family data orthogonalization, (ii) matrix substitution based on decomposition of a target family-orthogonalized covariance matrix, (iii) covariance-preserving whitening, retaining covariances between unrelated pairs while orthogonalizing family members, and (iv) using family-averaged data to obtain loadings. Except for within-family orthogonalization, our proposed approaches offer similar performance and are superior to the standard approaches. We illustrate the performance via simulation and analysis of a cystic fibrosis dataset.
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