Inference of population structure using multilocus genotype data: dominant markers and null alleles

By Daniel Falush, Matthew Stephens, J. K. Pritchard. Molecular Ecology Notes, 2007

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Dominant markers such as amplified fragment length polymorphisms (AFLPs) provide an economical way of surveying variation at many loci. However, the uncertainty about the underlying genotypes presents a problem for statistical analysis. Similarly, the presence of null alleles and the limitations of genotype calling in polyploids mean that many conventional analysis methods are invalid for many organisms. Here we present a simple approach for accounting for genotypic ambiguity in studies of population structure and apply it to AFLP data from whitefish. The approach is implemented in the program structure version 2.2, which is available from http://pritch.bsd.uchicago.edu/structure.html.

Subfields: Genetics

View Paper: https://www.semanticscholar.org/paper/84ab9430390291b54961e75c698e833e88b87ac7

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Citations in the corpus

A state-space mixed membership blockmodel for dynamic network tomography (2008, background): “Variants of mixed membership models have appeared in population genetics (Pritchard et al., 2000), text modeling (Blei et al.”
Why sampling scheme matters: the effect of sampling scheme on landscape genetic results (2009, methodology): “…statistically cluster populations and define population substructure in the field of molecular ecology is program STRUCTURE (Pritchard et al. 2000, Falush et al. 2003, 2007), although other approaches and programs exist (e.g., PARTITION, Dawson and Belkhir 2001; BAPS2, Corander et al. 2004;…”
polysat: an R package for polyploid microsatellite analysis (2011, positive): “S TRUCTURE , SPAG E D I and POPDIST have data formats that indicate the ploidy of each individual, which is included in the import and export by POLYSAT .”
Phylogenetic relationships in Betula (Betulaceae) based on AFLP markers (2008, background): “Version 2.2 accommodates genotypic uncertainty in dominant marker data by sampling present bands as homozygotes or heterozygotes according to their posterior probabilities (Falush et al. 2007).”
Visualizing spatial population structure with estimated effective migration surfaces (2014, background): “, 2005), STRUCTURE (Pritchard et al., 2000) with a sampling location prior (Hubisz et al.”
Relative performance of Bayesian clustering software for inferring population substructure and individual assignment at low levels of population differentiation (2006, methodology): “STRUCTURE employs a somewhat ad hoc approach for inferring K, by deriving the posterior probability distribution of K from separate MCMC chains, each with a different fixed value of K (Pritchard et al. 2000; Falush et al. 2003).”
Inferring weak population structure with the assistance of sample group information (2009, positive): “STRUCTURE is a Bayesian, model-based algorithm that is widely used for clustering genetic data (Pritchard et al. 2000; Falush et al. 2003; Falush et al. 2007).”
Supervised Topic Models (2007, methodology): “Third, the technique we have used to incorporate a response can be applied in existing variants of LDA, such as author-topic models (Rosen-Zvi et al., 2004), population-genetics models (Pritchard et al., 2000), and survey-data models (Erosheva, 2002).”
Understanding the Limiting Factors of Topic Modeling via Posterior Contraction Analysis (2014, neutral): “We refer the reader to Blei et al. (2003) and Pritchard et al. (2000) for a background on the model.”
Phylogeography of a widespread Asian subtropical tree: genetic east–west differentiation and climate envelope modelling suggest multiple glacial refugia (2014, methodology): “…of genetic clusters from microsatellites To determine spatial genetic structure, a Bayesian clustering approach was employed using structure 2.3.3 (Pritchard et al., 2000). structure probabilistically assigns individual genotypes to K gene pools and estimates the posterior probability of the…”
Low connectivity and declining genetic variability along a depth gradient in Corallium rubrum populations (2011, methodology): “…the two locations suggested a quantification of the levels of sample admixtures by the Bayesian clustering procedures of STRUCTURE 2.2 (Pritchard et al. 2000; Falush et al. 2003, 2007) in two separate runs: one including the Portofino samples and the other including the Cap de Creus samples.”
Hybridization and the species problem in conservation (2015, methodology): “(2008) analyzed with the clustering program STRUCTURE (Falush et al., 2007), which estimates the proportion of each individual’s genetic ancestry derived from different ancestral groups (in this case, 2).”
Sphagnum divinum (sp. nov.) and S. medium Limpr. and their relationship to S. magellanicum Brid. (2018, positive): “3.4 (Pritchard et al., 2000; Falush et al., 2003, 2007; Hubisz et al., 2009).”
Break zones in the distributions of alleles and species in alpine plants (2011, methodology): “Briefly, we applied model-based Bayesian population clustering, designed to cluster population genetic data (structure 2.1; Pritchard et al., 2000) for K = 2–6 clusters, with 20 replicates at each level of K, and parameters set as described in Thiel-Egenter et al.”
Bayesian spatial modeling of genetic population structure (2008, methodology): “Originally, Cegelski et al. (2003) found evidence for three genetically distinct groups using the non-spatial model of Pritchard et al. (2000).”
How did this snail get here? Several dispersal vectors inferred for an aquatic invasive species (2013, background): “The assignment tests had limited power to identify individual dispersers, probably due to the strong pattern of isolation by distance present (which tends to give mixed memberships in several groups and reduces the biological meaning of the K-value (Pritchard et al., 2000)) and possibly due to small”