dadi.CUDA: Accelerating Population Genetics Inference with Graphics Processing Units

Computationally Efficient Demographic History Inference from Allele Frequencies with Supervised Machine Learning

Inferring past demographic history of natural populations from genomic data is of central concern in many studies across research fields. Previously, our group had developed dadi, a widely used demographic history inference method based on the allele frequency spectrum (AFS) and maximum composite-likelihood optimization. However, dadi's optimization procedure can be computationally expensive. Here, we present donni (demography optimization via neural network inference), a new inference method based on dadi that is more efficient while maintaining comparable inference accuracy. For each dadi-supported demographic model, donni simulates the expected AFS for a range of model parameters then trains a set of Mean Variance Estimation neural networks using the simulated AFS. Trained networks can then be used to instantaneously infer the model parameters from future genomic data summarized by an AFS. We demonstrate that for many demographic models, donni can infer some parameters, such as population size changes, very well and other parameters, such as migration rates and times of demographic events, fairly well. Importantly, donni provides both parameter and confidence interval estimates from input AFS with accuracy comparable to parameters inferred by dadi's likelihood optimization while bypassing its long and computationally intensive evaluation process. donni's performance demonstrates that supervised machine learning algorithms may be a promising avenue for developing more sustainable and computationally efficient demographic history inference methods.

Integrating cryptic diversity into coral evolution, symbiosis and conservation

Understanding how diversity evolves and is maintained is critical to predicting the future trajectories of ecosystems under climate change; however, our understanding of these processes is limited in marine systems. Corals, which engineer reef ecosystems, are critically threatened by climate change, and global efforts are underway to conserve and restore populations as attempts to mitigate ocean warming continue. Recently, sequencing efforts have uncovered widespread undescribed coral diversity, including 'cryptic lineages'-genetically distinct but morphologically similar coral taxa. Such cryptic lineages have been identified in at least 24 coral genera spanning the anthozoan phylogeny and across ocean basins. These cryptic lineages co-occur in many reef systems, but their distributions often differ among habitats. Research suggests that cryptic lineages are ecologically specialized and several examples demonstrate differences in thermal tolerance, highlighting the critical implications of this diversity for predicting coral responses to future warming. Here, we draw attention to recent discoveries, discuss how cryptic diversity affects the study of coral adaptation and acclimation to future environments, explore how it shapes symbiotic partnerships, and highlight challenges and opportunities for conservation and restoration efforts.

Computationally efficient demographic history inference from allele frequencies with supervised machine learning

Inferring past demographic history of natural populations from genomic data is of central concern in many studies across research fields. Previously, our group had developed dadi, a widely used demographic history inference method based on the allele frequency spectrum (AFS) and maximum composite likelihood optimization. However, dadi's optimization procedure can be computationally expensive. Here, we developed donni (demography optimization via neural network inference), a new inference method based on dadi that is more efficient while maintaining comparable inference accuracy. For each dadi-supported demographic model, donni simulates the expected AFS for a range of model parameters then trains a set of Mean Variance Estimation neural networks using the simulated AFS. Trained networks can then be used to instantaneously infer the model parameters from future input data AFS. We demonstrated that for many demographic models, donni can infer some parameters, such as population size changes, very well and other parameters, such as migration rates and times of demographic events, fairly well. Importantly, donni provides both parameter and confidence interval estimates from input AFS with accuracy comparable to parameters inferred by dadi's likelihood optimization while bypassing its long and computationally intensive evaluation process. donni's performance demonstrates that supervised machine learning algorithms may be a promising avenue for developing more sustainable and computationally efficient demographic history inference methods.

Forty Years of Inferential Methods in the Journals of the Society for Molecular Biology and Evolution

We are launching a series to celebrate the 40th anniversary of the first issue of Molecular Biology and Evolution. In 2024, we will publish virtual issues containing selected papers published in the Society for Molecular Biology and Evolution journals, Molecular Biology and Evolution and Genome Biology and Evolution. Each virtual issue will be accompanied by a perspective that highlights the historic and contemporary contributions of our journals to a specific topic in molecular evolution. This perspective, the first in the series, presents an account of the broad array of methods that have been published in the Society for Molecular Biology and Evolution journals, including methods to infer phylogenies, to test hypotheses in a phylogenetic framework, and to infer population genetic processes. We also mention many of the software implementations that make methods tractable for empiricists. In short, the Society for Molecular Biology and Evolution community has much to celebrate after four decades of publishing high-quality science including numerous important inferential methods.

Demographic history inference and the polyploid continuum

Polyploidy is an important generator of evolutionary novelty across diverse groups in the Tree of Life, including many crops. However, the impact of whole-genome duplication depends on the mode of formation: doubling within a single lineage (autopolyploidy) versus doubling after hybridization between two different lineages (allopolyploidy). Researchers have historically treated these two scenarios as completely separate cases based on patterns of chromosome pairing, but these cases represent ideals on a continuum of chromosomal interactions among duplicated genomes. Understanding the history of polyploid species thus demands quantitative inferences of demographic history and rates of exchange between subgenomes. To meet this need, we developed diffusion models for genetic variation in polyploids with subgenomes that cannot be bioinformatically separated and with potentially variable inheritance patterns, implementing them in the dadi software. We validated our models using forward SLiM simulations and found that our inference approach is able to accurately infer evolutionary parameters (timing, bottleneck size) involved with the formation of auto- and allotetraploids, as well as exchange rates in segmental allotetraploids. We then applied our models to empirical data for allotetraploid shepherd's purse (Capsella bursa-pastoris), finding evidence for allelic exchange between the subgenomes. Taken together, our model provides a foundation for demographic modeling in polyploids using diffusion equations, which will help increase our understanding of the impact of demography and selection in polyploid lineages.

Bayesian optimization for demographic inference

Inference of demographic histories of species and populations is one of the central problems in population genetics. It is usually stated as an optimization problem: find a model's parameters that maximize a certain log-likelihood. This log-likelihood is often expensive to evaluate in terms of time and hardware resources, critically more so for larger population counts. Although genetic algorithm-based solution has proven efficient for demographic inference in the past, it struggles to deal with log-likelihoods in the setting of more than three populations. Different tools are therefore needed to handle such scenarios. We introduce a new optimization pipeline for demographic inference with time consuming log-likelihood evaluations. It is based on Bayesian optimization, a prominent technique for optimizing expensive black box functions. Comparing to the existing widely used genetic algorithm solution, we demonstrate new pipeline's superiority in the limited time budget setting with four and five populations, when using the log-likelihoods provided by the moments tool.

dadi-cli: Automated and distributed population genetic model inference from allele frequency spectra

Summary

dadi is a popular software package for inferring models of demographic history and natural selection from population genomic data. But using dadi requires Python scripting and manual parallelization of optimization jobs. We developed dadi-cli to simplify dadi usage and also enable straighforward distributed computing.

Availability and Implementation

dadi-cli is implemented in Python and released under the Apache License 2.0. The source code is available at https://github.com/xin-huang/dadi-cli . dadi-cli can be installed via PyPI and conda, and is also available through Cacao on Jetstream2 https://cacao.jetstream-cloud.org/ .

GADMA2: more efficient and flexible demographic inference from genetic data

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.

Ten years of demographic modelling of divergence and speciation in the sea

Understanding population divergence that eventually leads to speciation is essential for evolutionary biology. High species diversity in the sea was regarded as a paradox when strict allopatry was considered necessary for most speciation events because geographical barriers seemed largely absent in the sea, and many marine species have high dispersal capacities. Combining genome-wide data with demographic modelling to infer the demographic history of divergence has introduced new ways to address this classical issue. These models assume an ancestral population that splits into two subpopulations diverging according to different scenarios that allow tests for periods of gene flow. Models can also test for heterogeneities in population sizes and migration rates along the genome to account, respectively, for background selection and selection against introgressed ancestry. To investigate how barriers to gene flow arise in the sea, we compiled studies modelling the demographic history of divergence in marine organisms and extracted preferred demographic scenarios together with estimates of demographic parameters. These studies show that geographical barriers to gene flow do exist in the sea but that divergence can also occur without strict isolation. Heterogeneity of gene flow was detected in most population pairs suggesting the predominance of semipermeable barriers during divergence. We found a weak positive relationship between the fraction of the genome experiencing reduced gene flow and levels of genome-wide differentiation. Furthermore, we found that the upper bound of the 'grey zone of speciation' for our dataset extended beyond that found before, implying that gene flow between diverging taxa is possible at higher levels of divergence than previously thought. Finally, we list recommendations for further strengthening the use of demographic modelling in speciation research. These include a more balanced representation of taxa, more consistent and comprehensive modelling, clear reporting of results and simulation studies to rule out nonbiological explanations for general results.

Genome analysis traces regional dispersal of rice in Taiwan and Southeast Asia

The dispersal of rice (Oryza sativa) following domestication influenced massive social and cultural changes across South, East, and Southeast Asia. The history of dispersal across islands of Southeast Asia, and the role of Taiwan and the Austronesian expansion in this process remain largely unresolved. Here, we reconstructed the routes of dispersal of O. sativa ssp. japonica rice through Taiwan and the northern Philippines using whole-genome re-sequencing of indigenous rice landraces coupled with archaeological and paleoclimate data. Our results indicate that japonica rice found in the northern Philippines diverged from Indonesian landraces as early as 3500 BP. In contrast, rice cultivated by the indigenous peoples of the Taiwanese mountains has complex origins. It comprises two distinct populations, each best explained as a result of admixture between temperate japonica that presumably came from northeast Asia, and tropical japonica from the northern Philippines and mainland Southeast Asia respectively. We find that the temperate japonica component of these indigenous Taiwan populations diverged from northeast Asia subpopulations at about 2600 BP, while gene flow from the northern Philippines occurred before ∼1300 years BP. This coincides with a period of intensified trade established across the South China Sea. Finally, we find evidence for positive selection acting on distinct genomic regions in different rice subpopulations, indicating local adaptation associated with the spread of japonica rice.

fastsimcoal2: demographic inference under complex evolutionary scenarios

Motivation

fastsimcoal2 extends fastsimcoal, a continuous time coalescent-based genetic simulation program, by enabling the estimation of demographic parameters under very complex scenarios from the site frequency spectrum under a maximum-likelihood framework.

Results

Other improvements include multi-threading, handling of population inbreeding, extended input file syntax facilitating the description of complex demographic scenarios, and more efficient simulations of sparsely structured populations and of large chromosomes.

Availability and implementation

fastsimcoal2 is freely available on http://cmpg.unibe.ch/software/fastsimcoal2/. It includes console versions for Linux, Windows and MacOS, additional scripts for the analysis and visualization of simulated and estimated scenarios, as well as a detailed documentation and ready-to-use examples.