Citations
GADMA citations
If you use GADMA in your research please cite:
Ekaterina Noskova, Vladimir Ulyantsev, Klaus-Peter Koepfli, Stephen J O’Brien, Pavel Dobrynin, GADMA: Genetic algorithm for inferring demographic history of multiple populations from allele frequency spectrum data, GigaScience, Volume 9, Issue 3, March 2020, giaa005, https://doi.org/10.1093/gigascience/giaa005
If you use GADMA2 please cite:
Ekaterina Noskova, Nikita Abramov, Stanislav Iliutkin, Anton Sidorin, Pavel Dobrynin, and Vladimir Ulyantsev, GADMA2: more efficient and flexible demographic inference from genetic data, GigaScience, Volume 12, August 2023, giad059, https://doi.org/10.1093/gigascience/giad059
If you use Bayesian optimization implemented GADMA please cite:
Ekaterina Noskova and Viacheslav Borovitskiy, Bayesian optimization for demographic inference, G3 Genes|Genomes|Genetics, Volume 13, Issue 7, July 2023, jkad080, https://doi.org/10.1093/g3journal/jkad080
Engine citations
If you use dadi
as engine in GADMA please cite:
RN Gutenkunst, RD Hernandez, SH Williamson, CD Bustamante “Inferring the joint demographic history of multiple populations from multidimensional SNP data” PLoS Genetics 5:e1000695 (2009).
If you use inbreeding inference with dadi
engine in GADMA please cite:
Paul D Blischak, Michael S Barker, Ryan N Gutenkunst, Inferring the Demographic History of Inbred Species from Genome-Wide SNP Frequency Data, Molecular Biology and Evolution, Volume 37, Issue 7, July 2020, Pages 2124–2136, https://doi.org/10.1093/molbev/msaa042
If you use moments
as engine in GADMA please cite:
Jouganous, J., Long, W., Ragsdale, A. P., & Gravel, S. (2017). Inferring the joint demographic history of multiple populations: beyond the diffusion approximation. Genetics, 206(3), 1549-1567.
If you use moments.LD
as engine in GADMA please cite:
Ragsdale, A. P., & Gravel, S. (2019) Models of archaic admixture and recent history from two-locus statistics. PLoS Genetics, 15(6), e1008204 (2019).
Ragsdale, A. P., & Gravel, S. (2020) Unbiased estimation of linkage disequilibrium from unphased data. Molecular Biology and Evolution 37.3 (2020): 923-932 (2020)
If you use momi
as engine in GADMA please cite:
Kamm, J., Terhorst, J., Durbin, R., and Song, Y.S. Efficiently inferring the demographic history of many populations with allele count data. Journal of the American Statistical Association, Vol. 115, No. 531, (2020) 1472-1487.
CLAIC citation
If you evaluate CLAIC in GADMA please cite:
Alec J. Coffman, Ping Hsun Hsieh, Simon Gravel, Ryan N. Gutenkunst, Computationally Efficient Composite Likelihood Statistics for Demographic Inference, Molecular Biology and Evolution, Volume 33, Issue 2, February 2016, Pages 591–593, https://doi.org/10.1093/molbev/msv255