ArticleFrontiers in genetics2012
Correcting principal component maps for effects of spatial autocorrelation in population genetic data.
Article in Frontiers in genetics, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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16 citing papers in PubMed.
- When Clades Collide: Genomic Admixture in Blacklegged Ticks (Ixodes scapularis) From the Great Plains.Molecular ecology · 2026Article
- How Robust Are Genomic Offset Predictions to Methodological Choices? Insights From Perennial Ryegrass.Molecular ecology · 2026Article
- Performance of qpAdm-based screens for genetic admixture on graph-shaped histories and stepping stone landscapes.Genetics · 2025Article
- Spatial statistical machine learning models to assess the relationship between development vulnerabilities and educational factors in children in Queensland, Australia.BMC public health · 2022Article
- Genomic Analyses of Wild and Cultivated Bacanora Agave (Plants (Basel, Switzerland) · 2022Article
- Factor analysis of ancient population genomic samples.Nature communications · 2020Article
- Do different rates of gene flow underlie variation in phenotypic and phenological clines in a montane grasshopper community?Ecology and evolution · 2020Article
- Geospatial analysis of the patterns of chemical exposures among biota in the Canadian Oil Sands Region.PloS one · 2020Article
- Population genetic structure and connectivity of the seagrassEcology and evolution · 2019Article
- Seascape genetics and biophysical connectivity modelling support conservation of the seagrassEvolutionary applications · 2018Article
- Flow-directed PCA for monitoring networks.Environmetrics · 2017Article
- A Spatial Framework for Understanding Population Structure and Admixture.PLoS genetics · 2016Article
- Differential Evolution approach to detect recent admixture.BMC genomics · 2015Article
- Geographic population structure analysis of worldwide human populations infers their biogeographical origins.Nature communications · 2014Article
- Article
- Testing for associations between loci and environmental gradients using latent factor mixed models.Molecular biology and evolution · 2013Article
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4 authors.
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Abstract
In many species, spatial genetic variation displays patterns of "isolation-by-distance." Characterized by locally correlated allele frequencies, these patterns are known to create periodic shapes in geographic maps of principal components which confound signatures of specific migration events and influence interpretations of principal component analyses (PCA). In this study, we introduced models combining probabilistic PCA and kriging models to infer population genetic structure from genetic data while correcting for effects generated by spatial autocorrelation. The corresponding algorithms are based on singular value decomposition and low rank approximation of the genotypic data. As their complexity is close to that of PCA, these algorithms scale with the dimensions of the data. To illustrate the utility of these new models, we simulated isolation-by-distance patterns and broad-scale geographic variation using spatial coalescent models. Our methods remove the horseshoe patterns usually observed in PC maps and simplify interpretations of spatial genetic variation. We demonstrate our approach by analyzing single nucleotide polymorphism data from the Human Genome Diversity Panel, and provide comparisons with other recently introduced methods.
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