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Background Inference of complex demographic histories is a source of information about events that happened in the past of studied populations. Existing methods for demographic inference typically require input from the researcher in the form of a parameterized model. With an increased variety of methods and tools, each with its own interface, the model specification becomes tedious and error-prone. Moreover, optimization algorithms used to find model parameters sometimes turn out to be inefficient, for instance, by being not properly tuned or highly dependent on a user-provided initialization. The open-source software GADMA addresses these problems, providing automatic demographic inference. It proposes a common interface for several likelihood engines and provides global parameters optimization based on a genetic algorithm. Results Here, we introduce the new GADMA2 software and provide a detailed description of the added and expanded features. It has a renovated core code base, new likelihood engines, an updated optimization algorithm, and a flexible setup for automatic model construction. We provide a full overview of GADMA2 enhancements, compare the performance of supported likelihood engines on simulated data, and demonstrate an example of GADMA2 usage on 2 empirical datasets. Conclusions We demonstrate the better performance of a genetic algorithm in GADMA2 by comparing it to the initial version and other existing optimization approaches. Our experiments on simulated data indicate that GADMA2's likelihood engines are able to provide accurate estimations of demographic parameters even for misspecified models. We improve model parameters for 2 empirical datasets of inbred species. [ABSTRACT FROM AUTHOR]

Background 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 (AFS), the distribution of allele frequencies in populations. The joint AFS 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. Results 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 AFS data. Our method is implemented in the software GADMA (Genetic Algorithm for Demographic Model Analysis, https://github.com/ctlab/GADMA). Conclusions 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 multiple demographic models at different local optima close to the global one, providing a larger set of possible scenarios to further explore demographic history. [ABSTRACT FROM AUTHOR]

Species is the fundamental taxonomic unit in biology and its delimitation has implications for conservation. In giraffe (Giraffa spp.), multiple taxonomic classifications have been proposed since the early 1900s. 1 However, one species with nine subspecies has been generally accepted, 2 likely due to limited in-depth assessments, subspecies hybridizing in captivity, 3,4 and anecdotal reports of hybrids in the wild. 5 Giraffe taxonomy received new attention after population genetic studies using traditional genetic markers suggested at least four species. 6,7 This view has been met with controversy, 8 setting the stage for debate. 9,10 Genomics is significantly enhancing our understanding of biodiversity and speciation relative to traditional genetic approaches and thus has important implications for species delineation and conservation. 11 We present a high-quality de novo genome assembly of the critically endangered Kordofan giraffe (G. camelopardalis antiquorum) 12 and a comprehensive whole-genome analysis of 50 giraffe representing all traditionally recognized subspecies. Population structure and phylogenomic analyses support four separately evolving giraffe lineages, which diverged 230–370 ka ago. These lineages underwent distinct demographic histories and show different levels of heterozygosity and inbreeding. Our results strengthen previous findings of limited gene flow and admixture among putative giraffe species 6,7,9 and establish a genomic foundation for recognizing four species and seven subspecies, the latter of which should be considered as evolutionary significant units. Achieving a consensus over the number of species and subspecies in giraffe is essential for adequately assessing their threat level and will improve conservation efforts for these iconic taxa. [Display omitted] • Comprehensive whole-genome analysis of 50 individuals from all giraffe subspecies • Results show four separate giraffe lineages with distinct demographic histories • Luangwa giraffe should be treated as its own separate subspecies of Masai giraffe • Chromium de novo genome assembly of the critically endangered Kordofan giraffe Coimbra et al.'s assessment on giraffe taxonomy uses genome data from all traditionally recognized subspecies. Population genomic and phylogenomic analyses support four separately evolving giraffe lineages that underwent distinct demographic histories. Their current genomic diversity will inform targeted conservation efforts for these iconic taxa. [ABSTRACT FROM AUTHOR]