GADMA: Genetic algorithm for inferring demographic history of multiple populations from allele frequency spectrum data

Abstract

The demographic history of any population is imprinted in the genomes of the individuals that make up the population. One of the most popular and convenient representations of genetic information is the allele frequency spectrum or AFS, the distribution of allele frequencies in populations. The joint allele frequency spectrum is commonly used to reconstruct the demographic history of multiple populations and several methods based on diffusion approximation (e.g., ∂a∂i) and ordinary differential equations (e.g., moments) have been developed and applied for demographic inference. These methods provide an opportunity to simulate AFS under a variety of researcher-specified demographic models and to estimate the best model and associated parameters using likelihood-based local optimizations. However, there are no known algorithms to perform global searches of demographic models with a given AFS.Here, we introduce a new method that implements a global search using a genetic algorithm for the automatic and unsupervised inference of demographic history from joint allele frequency spectrum data.Our method is implemented in the software GADMA (Genetic Algorithm for Demographic Analysis, https://github.com/ctlab/GADMA). We demonstrate the performance of GADMA by applying it to sequence data from humans and non-model organisms and show that it is able to automatically infer a demographic model close to or even better than the one that was previously obtained manually. Moreover, GADMA is able to infer demographic models at different local optima close to the global one, making it is possible to detect more biology corrected model during further research.

Inferring number of populations and changes in connectivity under the n-island model

“…We also applied our method to genomic data simulated under the scenarios used to describe recent human evolutionary history by Gutenkunst et al 2009; Noskova et al (2019). Here, we thus ask the following two questions: if human evolution were indeed closer to such splitting models, would our method infer again an n-island model with similar parameters to those inferred from the humans PSMCs?…”

“…Additionally, in the case of human evolutionary history, the branches of the assumed tree may represent predefined continental populations. Such models thus assume panmixia over large geographic regions and long periods (Gutenkunst et al, 2009;Mazet et al, 2016;Noskova et al, 2019;Scerri et al, 2019). Panmictic and tree models are useful approximations, and in the last decades they have proven their utility in building stories of human expansions and population splits (Gutenkunst et al, 2009;Li and Durbin, 2011).…”

“…We limit ourselves to validating the method with data simulated under piecewise stationary n-island models due to the very large parameter space explored here. We then apply our approach to human genomic data using five published PSMC curves (Prado-Martinez et al, 2013), allowing in each case the number of components to vary between analyses, and compare the inferred histories and connectivity graphs between individuals and with previously inferred scenarios by Rodríguez et al (2018) and Noskova et al (2019).…”

“…12 and 13 show the results of applying our method to the IICR curves associated with the demographic model for human expansion published byNoskova et al (2019…”

Demographic history, cold adaptation, and recent NRAP recurrent convergent evolution at amino acid residue 100 in the world northernmost cattle from Russia

“…To calculate historical demography and separation time between pairs of populations we used two complementary approaches: SMC++ (35) and GADMA (36), which use different population inference models. To remove spurious variants and regions with bad mapping quality from further consideration, we first computed the mappability score of the bovine reference genome using the GEM mappability program (37) with kmer size =100 bp allowing one mismatch.…”