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geste_bayescan [2011/06/10 11:35] heidigeste_bayescan [2011/07/07 11:32] (current) heidi
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-**BayeScan:** This program identifies candidate loci under natural selection. It's applicable to both, dominant and codominant data.+**BayeScan** (version 2.01): \\ 
 +This program identifies candidate loci under natural selection. It's applicable to both, dominant and codominant data.
  
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-**GESTE** (version 2.0): (GEnetic STructure inference based on genetic and Environmental data) is a Bayesian method to evaluate the effect that biotic and abiotic environmental factors (geographic distance, language, temperature, altitude, local population sizes, etc.) have on the genetic structure of populations. It can also be used to study spatial population processes, such as range expansions, by simply introducing longitude and latitude as the explanatory variables.\\+**GESTE** (version 2.0): \\ 
 +(GEnetic STructure inference based on genetic and Environmental data) is a Bayesian method to evaluate the effect that biotic and abiotic environmental factors (geographic distance, language, temperature, altitude, local population sizes, etc.) have on the genetic structure of populations. It can also be used to study spatial population processes, such as range expansions, by simply introducing longitude and latitude as the explanatory variables.\\
 GESTE estimates FST values for each local population and relates them to environmental factors using a generalized linear model. The method requires genetic data from codominant markers (e.g. allozymes, microsatellites, or SNPs) and environmental data specific to each local population. The software is written in C++ and integrates a tool to draw posterior density functions (histogram, running mean, traces, etc.) and to estimate parameters from them (mean, mode, variance, HPDI etc.).  GESTE estimates FST values for each local population and relates them to environmental factors using a generalized linear model. The method requires genetic data from codominant markers (e.g. allozymes, microsatellites, or SNPs) and environmental data specific to each local population. The software is written in C++ and integrates a tool to draw posterior density functions (histogram, running mean, traces, etc.) and to estimate parameters from them (mean, mode, variance, HPDI etc.). 
  
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 ===== How to cite ===== ===== How to cite =====
-  * **BayeScan:** Foll, M. and O. Gaggiotti (2008). "A Genome-Scan Method to Identify Selected Loci Appropriate for Both Dominant and Codominant Markers: A Bayesian Perspective." Genetics 180(2): 977-993.+  * **BayeScan:**  
 +    * Foll, M. and O. Gaggiotti (2008). "A Genome-Scan Method to Identify Selected Loci Appropriate for Both Dominant and Codominant Markers: A Bayesian Perspective." Genetics 180(2): 977-993
 +    * Foll M, Fischer MC, Heckel G and L Excoffier (2010) Estimating population structure from AFLP amplification intensity. Molecular Ecology 19: 4638-4647 
 +    * Fischer MC, Foll M, Excoffier L and G Heckel (2011) Enhanced AFLP genome scans detect local adaptation in high-altitude populations of a small rodent (Microtus arvalis). Molecular Ecology.
   * **GESTE:** Foll, M., and O.E. Gaggiotti, 2006. Identifying the environmental factors that determine the genetic structure of Populations. Genetics 174: 875-891.    * **GESTE:** Foll, M., and O.E. Gaggiotti, 2006. Identifying the environmental factors that determine the genetic structure of Populations. Genetics 174: 875-891. 
  
geste_bayescan.1307698510.txt.gz · Last modified: 2011/06/10 11:35 by heidi