Computation of ancestry scores with mixed families and unrelated individuals

TL;DR

This paper introduces new methods for computing genotype ancestry scores that are robust to family relationships, outperforming existing approaches, especially in datasets with related individuals, as demonstrated through simulations and real data analysis.

Contribution

The paper proposes novel strategies for ancestry score computation that effectively handle related individuals, improving upon the standard unrelated-only methods.

Findings

Proposed methods outperform existing approaches in simulations.

New techniques mitigate shrinkage issues in projected scores.

Methods demonstrate improved accuracy in real genetic data analysis.

Abstract

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 use a motivational example from the North American Cystic Fibrosis (CF) Consortium genetic association study with 3444 individuals and 898 family members to illustrate the challenge of computing ancestry scores when sets of both unrelated individuals and closely-related family members are included. We propose novel methods to obtain ancestry scores and demonstrate that the proposed methods outperform existing methods. The current standard is to compute loadings (left singular vectors) using unrelated individuals and to compute projected scores for remaining family members. However, projected ancestry scores from this approach suffer from shrinkage toward zero. We consider in turn alternate strategies: (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 the CF dataset.