Bottleneck - UMEcolGenetics/PawPawPulation-Genetics GitHub Wiki
This tutorial covers the use of the BOTTLENECK program. BOTTLENECK utilizes three mutation models, three statistical analyses, and a "mode-shift" function to identify recently bottlenecked populations1. Of the three mutation models, only the stepwise mutation model (SMM) and the two-phase mutation model (TPM) were run in this tutorial as they are best used with microsatellite data, while the infinite alleles model (IAM) is recommended for use with allozyme data1,2,3. Additionally, the sign test and the Wilcoxon sign rank test were the only two statistical analyses performed in this tutorial as they are better suited for analyzing fewer than 10 loci1. The other statistical analysis, the standardized differences test, was not performed as it is not recommended for use with fewer than 20 loci1,4. The "mode-shift" function indicates if there is deviation from the mutation-drift equilibrium, indicating a recently bottlenecked population5. Lastly, the software could not analyze all of the populations as populations with under 10 individuals can lead to errors in the program. As such, any population that contained less than 5 individuals, in addition to the POC population, were removed from the analysis.
The data in this analysis is comprised of microsatellite loci from 82 pawpaw (Asimina triloba) populations in the United States. It was produced by Wyatt et al. (2021)6 and accessed from Dryad (https://doi.org/10.5061/dryad.5x69p8d3g).
The data was opened in the GenAlEx (Genetic Analysis in Excel) add-in for Microsoft Excel7 and exported in the GenePop file format. Once exported in the appropriate format, the data should then be opened in Notepad to remove populations containing fewer than 5 individuals, as well as the POC population.
The BOTTLENECK software is available for download here.
On the left side of the interface, there are several different options to choose from. Since we are working with microsatellite data, we need to deseect the I.A.M. mutation model and selection the T.P.M. mutation model. Keep the S.M.M. mutation model selected. We also need to set the variance for TPM to 12 and set the proportion of SMM in TPM (%) to 95. Keep the iterations at 1000. Deselect the standardized differences test because we are working with only 9 loci, and keep the signs test, the Wilcoxon sign rank test, and the mode-shift selected.
Lastly, upload the data file that was exported from GenAlEx (we have provided an example file - see Pawpaw_Data.xlsx, and click GO!.
When the software has finished running, you can save the results as a text file or you can view a summary table of the results by selecting summary.
The results of our analyses are summarized in the BOTTLENECK_Results_Table.xlsx file.
The sign test revealed significant values (p < 0.05) for the CKW, MWR, PWP, and WES populations under the SMM mutation model and revealed significant values for the CKW, MWR, and SHO populations under the TPM mutation model, which indicates a heterozygosity excess in these populations.
The one-tailed Wilcoxon sign rank test (Wilcoxon-1T) revealed significant values for the BLU and LL2 populations under both the SMM and TPM mutation models, indicating heterozygosity excess. The two-tailed Wilcoxon sign rank test (Wilcoxon-2T) revealed significant values for the BLU, CKW, and MWR populations under both the SMM and the TPM mutation models.
Finally, the software identified a mode-shift in the BCB, BLU, CCC, DRP, KAN, LL2, RL, SUS, WSL, FHM, LM1, and MC1 populations. These mode-shifts are deviation from mutation-drift equilibrium and indicate recent bottlenecks.
[1]: Piry, S., Luikart, G., and Cornuet, J.-M. (1999). BOTTLENECK: a computer program for detecting recent reductions in the effective population size using allele frequency data. Journal of Heredity, 90(4): 502-503. https://doi.org/10.1093/jhered/90.4.502.
[2]: Luikart, G., & Cornuet, J.-M. (1998). Empirical evaluation of a test for identifying recently bottlenecked populations from allele frequency data. Conservation Biology, 12(1): 228-237. https://www.jstor.org/stable/2387479.
[3]: Putman, A. I., & Carbone, I. (2014). Challenges in analysis and interpretation of microsatellite data for population genetic studies. Ecology and Evolution, 4(22): 4399-4428. https://doi.org/10.1002/ece3.1305.
[4]: Cornuet, J.-M, & Luikart, G. (1996). Description and power analysis of two tests for detecting recent population bottlenecks from allele frequency data. Genetics, 144(4): 2001-2014. https://doi.org/10.1093/genetics/144.4.2001.
[5]: Luikart, G., Allendorf, F. W., Cornuet, J.-M., and Sherwin, W. B. (1998). Distortion of allele frequency distributions provides a test for recent population bottlenecks. Journal of Heredity, 89(3): 238-247. https://doi.org/10.1093/jhered/89.3.238.
[6]: Wyatt, G. E., Hamrick, J. L., and Trapnell, D. W. (2021). The role of anthropogenic dispersal in shaping the distribution and genetic composition of a widespread North American tree species. Ecology and Evolution, 11(16): 11515-11532. https://doi.org/10.1002/ece3.7944.
[7]: Peakall, R. & Smouse, P.E. (2012). GenAlEx 6.5: genetic analysis in Excel. Population genetic software for teaching and research--an update. Bioinformatics (Oxford, England), 28(19), 2537–2539. https://doi.org/10.1093/bioinformatics/bts460.