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Fit population genetic models using a diffusion approximation to the joint frequency spectrum
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Diffusion Approximation for Demographic Inference

Developed by Ryan Gutenkunst in the labs of Scott Williamson and Carlos Bustamante in Cornell's Department of Biological Statistics and Computational Biology.

Ryan is now a postdoc at Los Alamos National Lab, in the Theoretical Biology and Biophysics group and the Center for Nonlinear Studies.


  • Paper accepted!; September 7, 2009
The paper on dadi has been accepted for publication in PLoS Genetics. It has also been posted on the arXiv.
The paper describes the method and infers demographic models for human expansion out of Africa and the settlement of the New World. The out of Africa model is then combined with a previously estimated distribution of selection coefficients to accurately predict the distribution of segregating synonymous variation between populations.
  • Version 1.2.3 release; May 21, 2009
Primarily, this version fixes several small bugs. It also adds an extension of the IM model to Demographics2D, support for pulling specific populations out of an ms file, and a non-log-parameters optimization method.

  • Version 1.2.2 release; March 23, 2009
Added method to import frequency spectra from SFS_CODE output. Also added the classic Isolation-Migration model to Demographics2D. Finally, some small bugfixes and additional technical features were added.


The above plot summarizes the result of fitting a model the joint frequency spectrum of genetic variation between the Yoruba (YRI) and CEPH European (CEU) populations. The data is derived from the Environmental Genome Project SNPs database. The upper left panel is the data, and the upper right is the result of a demographic model whose parameters have been optimized using dadi. The lower left panel is the residuals between model and data (red means the model predicts too many SNPs in that bin) and the lower right is a histogram of the residuals. The model involves population growth in the ancestral population, followed by divergence of the CEU population with a bottleneck and exponential growth. It contains 7 free parameters and took a few minutes to fit using dadi.

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