GESTE/ BayeScan

BayeScan
GESTE

BayeScan (version 2.01):
This program identifies candidate loci under natural selection. It's applicable to both, dominant and codominant data.

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.).

• AFLP
• SNP
• Microsatellites
• allozymes

• The program recognizes keywords in […].
• You have to indicate the number of loci and populations before the main data.
• For each population, there is one line per locus numbered from 1 to the number of loci.
• Population must be numbered from 1 to the number of populations.
• Then there is the number of alleles measured for this population at this locus (50 individuals make 100 alleles for diploids) and the number of possible alleles found at this locus (for all populations).
• After, there is the corresponding allele count. This part must sum to the number of alleles measured.
• You can write any comments between sections.
• There is no particular file extension needed.
• Number of individuals can be different at every locus (missing data).

[loci]=5

[populations]=2

[pop]=1
  1  100   7   34    0   4   0  13  20  29 
  2  100   7    3   26   2   8  56   2   3 
  3  100   7   46    0  17   1  10  25   1 
  4  100   7    4    7   2  52  23  12   0 
  5  100   7   23  28   0   1   2   38   8 

[pop]=2
  1  100   7   11   6  17   2   8  36  20 
  2  100   7    8   6   3  26  36   9  12 
  3  100   7   11   7  35  13  26   8   0 
  4  100   7   14   2   0  24  36  24   0 
  5  100   7   20   6  19  36   6  10   3 

• 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.