ArticleBiometrics2018
Computation of ancestry scores with mixed families and unrelated individuals.
Article in Biometrics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
6 citing papers in PubMed.
- HAP-SAMPLE2: data-based resampling for association studies with admixture.Bioinformatics (Oxford, England) · 2025Article
- Variants of IL6, IL10, FCN2, RNASE3, IL12B and IL17B loci are associated with Schistosoma mansoni worm burden in the Albert Nile region of Uganda.PLoS neglected tropical diseases · 2023Article
- Identifying suicidal subtypes and dynamic indicators of increasing and decreasing suicide risk in active duty military personnel: Study protocol.Contemporary clinical trials communications · 2021Article
- A Review and Tutorial of Machine Learning Methods for Microbiome Host Trait Prediction.Frontiers in genetics · 2019Review
- Eigenvalue significance testing for genetic association.Biometrics · 2018Article
- A survey of high dimension low sample size asymptotics.Australian & New Zealand journal of statistics · 2018Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
The issue of robustness to family relationships in computing genotype ancestry scores such as eigenvector projections has received increased attention in genetic association, and is particularly challenging when sets of both unrelated individuals and closely related family members are included. 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 two main novel strategies: (i) matrix substitution based on decomposition of a target family-orthogonalized covariance matrix, and (ii) using family-averaged data to obtain loadings. We illustrate the performance via simulations, including resampling from 1000 Genomes Project data, and analysis of a cystic fibrosis dataset. The matrix substitution approach has similar performance to the current standard, but is simple and uses only a genotype covariance matrix, while the family-average method shows superior performance. Our approaches are accompanied by novel ancillary approaches that provide considerable insight, including individual-specific eigenvalue scree plots.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.