Bayesian Analysis of Genetic Differentiation Between Populations

Abstract

We introduce a Bayesian method for estimating hidden population substructure using multilocus molecular markers and geographical information provided by the sampling design. The joint posterior distribution of the substructure and allele frequencies of the respective populations is available in an analytical form when the number of populations is small, whereas an approximation based on a Markov chain Monte Carlo simulation approach can be obtained for a moderate or large number of populations. Using the joint posterior distribution, posteriors can also be derived for any evolutionary population parameters, such as the traditional fixation indices. A major advantage compared to most earlier methods is that the number of populations is treated here as an unknown parameter. What is traditionally considered as two genetically distinct populations, either recently founded or connected by considerable gene flow, is here considered as one panmictic population with a certain probability based on marker data and prior information. Analyses of previously published data on the Moroccan argan tree (Argania spinosa) and of simulated data sets suggest that our method is capable of estimating a population substructure, while not artificially enforcing a substructure when it does not exist. The software (BAPS) used for the computations is freely available from http://www.rni.helsinki.fi/~mjs.

Population structure and adaptive variation of Helichrysum italicum (Roth) G. Don along eastern Adriatic temperature and precipitation gradient

“…The genetic structure of H. italicum populations was assessed using two Bayesian model-based clustering methods as implemented in STRUCTURE v. 2.3.4 125 and BAPS v. 6 126 , 127 . In STRUCTURE, the number of clusters (K) was set from 1 to 11 and 30 runs per K were performed on the Isabella computer cluster at the University of Zagreb, University Computing Centre (SRCE).…”

“…Another Bayesian model-based analysis was performed using BAPS 126 , 127 to verify data obtained from STRUCTURE. Mixture analysis was performed both without the geographic coordinates as an informative prior (‘ Clustering of individuals ’) and with this prior (‘ Spatial clustering of individuals ’ 131 .…”

Identifying and conserving sympatric diversity in trout of the genus Salmo, with particular reference to Lough Melvin, Ireland

“…Samples from different spawning locations can be analysed in a similar fashion. Other methods rely on treating all specimens from a water as a single sample with sympatric populations being detected by analysis for heterozygote deficiency (Wahlund effect) (Jorde et al, 2018), or by using population structuring methods such as BAPS (Corander & Marttinen, 2006;Corander et al, 2003Corander et al, , 2004, STRUCTURE (Pritchard et al, 2000) and/or discriminant analysis of principal components (DAPC; Jombart et al, 2010). BAPS and STRUCTURE do not rely on a priori information to infer population structuring.…”

Weak spatial-genetic structure in a native invasive, the southern pine beetle (Dendroctonus frontalis), across the eastern United States

“…To infer the number of natural genetic groups (K ) and their members, we analyzed microsatellite datasets using three approaches: STRUCTURE v2.3.4 (Pritchard, Stephens & Donnelly, 2000), BAPS v6.0 (Corander, Waldmann & Sillanpää, 2003), and Discriminant Analysis of Principal Components (DAPC; Jombart, Devillard & Balloux, 2010). The first two are Bayesian approaches and assume HWE and LE within ''true'' clusters.…”