ArticleGenome research2026
A spectral component approach leveraging identity-by-descent graphs to address recent population structure in genomic analysis.
Article in Genome research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Multiple-testing corrections in case-control studies using identity-by-descent segments.American journal of human genetics · 2026Article
- Admixture mapping identifies complex trait associations with local ancestry in themedRxiv : the preprint server for health sciences · 2025Article
- Multiple-testing corrections in selection scans using identity-by-descent segments.American journal of human genetics · 2025Article
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7 authors.
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Abstract
Population structure is a well-known confounder in statistical genetics, particularly in genome-wide association studies (GWAS), in which it can lead to inflated test statistics and spurious associations. Traditional methods, such as principal components (PCs), commonly used to adjust for population structure, are limited in capturing fine-scale, nonlinear patterns that arise from recent demographic events, patterns that are crucial for understanding rare variant effects. To address this challenge, we propose a novel method called spectral components (SPCs), which leverages identity-by-descent (IBD) graphs to capture and transform local, nonlinear fine-scale population structure into continuous representations that can be seamlessly integrated into genetic analysis pipelines. Using both simulated data sets and empirical data from the UK Biobank (N ≈ 420,000), we demonstrate that SPCs outperform PCs in adjusting for fine-scale population structure. In simulations, SPCs explain >90% of the fine-scale population structure with fewer components, whereas PCs capture <5%. In the UK Biobank, SPCs reduce the inflation of
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