This population does not exist: learning the distribution of evolutionary histories with generative adversarial networks

Booker, William W; Ray, Dylan D.; Schrider, Daniel R.

doi:10.1101/2022.09.17.508145

Cited by 8 publications

(13 citation statements)

References 78 publications

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“…Generative models trained with similar-sized genomic data have been reported in the literature but the main goal of these studies was characterization of population structure via dimensionality reduction and the generated genomes did not possess good haplotypic integrity [26,30]. There have been other studies focusing on demographic parameter estimation [31] and data generation [7,8,9,10] for population genetics but these only included training with smaller genomic segments.…”

Section: Discussionmentioning

confidence: 99%

“…There have been other studies focusing on demographic parameter estimation [41] and data generation [8,9,10,11] for population genetics but these only included training with smaller genomic segments.…”

Section: Discussionmentioning

confidence: 99%

“…There have been numerous applications in demographic inference [1], detecting natural selection [2] , genome-wide association studies [3] and functional genomics [4], many of which became state of the art [5,6]. In the recent few years, generative machine learning approaches for the genomics field have also begun to gain research interest thanks to algorithmic advances and widespread availability of computational resources [7,8,9,10,11]. Broadly speaking, generative machine learning involves the utilisation of generative models which are trained to model the distribution of a given dataset so that new data instances with similar characteristics to the real data can be sampled from this learned distribution.…”

Section: Introductionmentioning

confidence: 99%

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Deep convolutional and conditional neural networks for large-scale genomic data generation

Yelmen

Decelle

Boulos

et al. 2023

Preprint

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Applications of generative models for genomic data have gained significant momentum in the past few years, with scopes ranging from data characterization to generation of genomic segments and functional sequences. In our previous study, we demonstrated that generative adversarial networks (GANs) and restricted Boltzmann machines (RBMs) can be used to create novel high-quality artificial genomes (AGs) which can preserve the complex characteristics of real genomes such as population structure, linkage disequilibrium and selection signals. However, a major drawback of these models is scalability, since the large feature space of genome-wide data increases computational complexity vastly. To address this issue, we implemented a novel convolutional Wasserstein GAN (WGAN) model along with a novel conditional RBM (CRBM) framework for generating AGs with high SNP number. These networks implicitly learn the varying landscape of haplotypic structure in order to capture complex correlation patterns along the genome and generate a wide diversity of plausible haplotypes. We performed comparative analyses to assess both the quality of these generated haplotypes and the amount of possible privacy leakage from the training data.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Deep convolutional and conditional neural networks for large-scale genomic data generation

Yelmen

Decelle

Boulos

et al. 2023

Preprint

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“…However, this initial attempt is not capable of recovering rare variant patterns, but advanced architectures designed to deal with mode collapse may solve this issue ( Ghosh et al 2017 ). Despite current limitations, GANs appear to be a promising deep learning framework to infer complex population genetic parameters in face of an uncertain or unknown demographic model ( Booker et al 2022 ).…”

Section: Deep Learning Algorithmsmentioning

confidence: 99%

Deep Learning in Population Genetics

Korfmann

Gaggiotti

Fumagalli

2023

Genome Biology and Evolution

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Population genetics is transitioning into a data-driven discipline thanks to the availability of large-scale genomic data and the need to study increasingly complex evolutionary scenarios. With likelihood and Bayesian approaches becoming either intractable or computationally unfeasible, machine learning, and in particular deep learning, algorithms are emerging as popular techniques for population genetic inferences. These approaches rely on algorithms that learn non-linear relationships between the input data and the model parameters being estimated through representation learning from training data sets. Deep learning algorithms currently employed in the field comprise discriminative and generative models with fully connected, con volutional, or recurrent layers. Additionally, a wide range of powerful simulators to generate training data under complex scenarios are now available. The application of deep learning to empirical data sets mostly replicates previous findings of demography reconstruction and signals of natural selection in model organisms. To showcase the feasibility of deep learning to tackle new challenges, we designed a branched architecture to detect signals of recent balancing selection from temporal haplotypic data, which exhibited good predictive performance on simulated data. Investigations on the interpretability of neural networks, their robustness to uncertain training data, and creative representation of population genetic data, will provide further opportunities for technological advancements in the field.

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“…GANs have previously been applied in population genetics to simulate artificial genomes (Yelmen et al, 2021; Booker et al, 2022) and to estimate population genetic parameters (Wang et al, 2021). GANs consist of two networks: a generator and a discriminator.…”

Section: Introductionmentioning

confidence: 99%

Phylogenetic inference using Generative Adversarial Networks

Smith

Hahn

2022

Preprint

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Motivation:The application of machine learning approaches in phylogenetics has been impeded by the vast model space associated with inference. In particular, supervised machine learning approaches require data from across this space to train models. Because of this requirement, previous approaches have typically been limited to inferring relationships among unrooted quartets of taxa, where there are only three possible topologies. Here, we explore the potential of generative adversarial networks (GANs) to address this limitation. GANs consist of a generator and a discriminator: at each step, the generator aims to create data that is similar to real data, while the discriminator attempts to distinguish generated and real data. By using an evolutionary model as the generator, we use GANs to make evolutionary inferences, and, since a new model can be considered at each iteration, heuristic searches of complex model spaces are possible. Thus, GANs offer a potential solution to the challenges of applying machine learning to inferring phylogenetic relationships.Results:We developed phyloGAN, a GAN that infers phylogenetic relationships among species. phyloGAN takes as input a concatenated alignment, or a set of gene alignments, and then infers a phylogenetic tree either considering or ignoring gene tree heterogeneity. phyloGAN performs well for up to ten taxa in the concatenation case, and performs well for six taxa when considering gene tree heterogeneity. Future developments to the network should aim to make the exploration of this more complex model space more efficient to reduce run times and to enable the consideration of larger trees.Availability:phyloGAN is available on github: https://github.com/meganlsmith/phyloGAN/.

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This population does not exist: learning the distribution of evolutionary histories with generative adversarial networks

Cited by 8 publications

References 78 publications

Deep convolutional and conditional neural networks for large-scale genomic data generation

Deep convolutional and conditional neural networks for large-scale genomic data generation

Deep Learning in Population Genetics

Phylogenetic inference using Generative Adversarial Networks

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