ArticleG3 (Bethesda, Md.)2022
Quantitative evaluation of nonlinear methods for population structure visualization and inference.
Article in G3 (Bethesda, Md.), 2022. 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.
- Article
- Population history and admixture of the Fulani people from the Sahel.American journal of human genetics · 2025Article
- Quantitative evaluation of electrographic response to electroconvulsive therapy in super-refractory status epilepticus.Frontiers in neurology · 2024Article
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4 authors.
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Abstract
Population structure (also called genetic structure and population stratification) is the presence of a systematic difference in allele frequencies between subpopulations in a population as a result of nonrandom mating between individuals. It can be informative of genetic ancestry, and in the context of medical genetics, it is an important confounding variable in genome-wide association studies. Recently, many nonlinear dimensionality reduction techniques have been proposed for the population structure visualization task. However, an objective comparison of these techniques has so far been missing from the literature. In this article, we discuss the previously proposed nonlinear techniques and some of their potential weaknesses. We then propose a novel quantitative evaluation methodology for comparing these nonlinear techniques, based on populations for which pedigree is known a priori either through artificial selection or simulation. Based on this evaluation metric, we find graph-based algorithms such as t-SNE and UMAP to be superior to principal component analysis, while neural network-based methods fall behind.
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