Automatic inference of demographic parameters using Generative Adversarial Networks

Wang, Zhanpeng; Wang, Jiaping; Kourakos, Michael; Hoang, Nhung; Lee, Hyong Hark; Mathieson, Iain; Mathieson, Sara

doi:10.1101/2020.08.05.237834

Cited by 9 publications

(9 citation statements)

References 51 publications

(50 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Various SML methods have been recently developed (e.g., Schrider & Kern, 2018 ; Wang et al, 2021 ). In particular, neural networks are machine learning methods which are used increasingly in population genetics, often under the term “deep learning” (Sheehan & Song, 2016 ), and sometimes using an ABC framework (Mondal et al, 2019 ).…”

Section: Discussionmentioning

confidence: 99%

Extending approximate Bayesian computation with supervised machine learning to infer demographic history from genetic polymorphisms using DIYABC Random Forest

Collin

Durif

Raynal

et al. 2021

Molecular Ecology Resources

View full text Add to dashboard Cite

Simulation-based methods such as approximate Bayesian computation (ABC) are well-adapted to the analysis of complex scenarios of populations and species genetic history. In this context, supervised machine learning (SML) methods provide attractive statistical solutions to conduct efficient inferences about scenario choice and parameter estimation. The Random Forest methodology (RF) is a powerful ensemble of SML algorithms used for classification or regression problems. Random Forest allows conducting inferences at a low computational cost, without preliminary selection of the relevant components of the ABC summary statistics, and bypassing the derivation of ABC tolerance levels. We have implemented a set of RF algorithms to process inferences using simulated data sets generated from an extended version of the population genetic simulator implemented in DIYABC v2.1.0. The resulting computer package, named DIYABC Random Forest v1.0, integrates two functionalities into a user-friendly interface: the simulation under custom evolutionary scenarios of different types of molecular data (microsatellites, DNA sequences or SNPs) and RF treatments including statistical tools to evaluate the power and accuracy of inferences. We illustrate the functionalities of DIYABC Random Forest v1.0 for both scenario choice and parameter estimation through the analysis of pseudo-observed and real data sets corresponding to pool-sequencing and individual-sequencing SNP data sets. Because of the properties inherent to the implemented RF methods and the large feature vector (including various summary statistics and their linear combinations) available for SNP data, DIYABC Random Forest v1.0 can efficiently contribute to the analysis of large SNP data sets to make inferences about complex population genetic histories.

show abstract

Section: Discussionmentioning

confidence: 99%

Extending approximate Bayesian computation with supervised machine learning to infer demographic history from genetic polymorphisms using DIYABC Random Forest

Collin

Durif

Raynal

et al. 2021

Molecular Ecology Resources

View full text Add to dashboard Cite

show abstract

“…Progress in this regard could involve the use of generative adversarial networks (GANs), which appears to be a fruitful way to address this. Indeed, recent work suggests that one can train a GAN to learn to generate realistic population genomic data for any population (Wang et al ., 2020).…”

Section: Discussionmentioning

confidence: 99%

Detecting adaptive introgression in human evolution using convolutional neural networks

Gower

Picazo

Fumagalli

et al. 2020

Preprint

View full text Add to dashboard Cite

Studies in a variety of species have shown evidence for positively selected variants introduced into one population via introgression from another, distantly related population—a process known as adaptive introgression. However, there are few explicit frameworks for jointly modelling introgression and positive selection, in order to detect these variants using genomic sequence data. Here, we develop an approach based on convolutional neural networks (CNNs). CNNs do not require the specification of an analytical model of allele frequency dynamics, and have outperformed alternative methods for classification and parameter estimation tasks in various areas of population genetics. Thus, they are potentially well suited to the identification of adaptive introgression. Using simulations, we trained CNNs on genotype matrices derived from genomes sampled from the donor population, the recipient population and a related non-introgressed population, in order to distinguish regions of the genome evolving under adaptive introgression from those evolving neutrally or experiencing selective sweeps. Our CNN architecture exhibits 95% accuracy on simulated data, even when the the genomes are unphased, and accuracy decreases only moderately in the presence of heterosis. As a proof of concept, we applied our trained CNNs to human genomic datasets—both phased and unphased—to detect candidates for adaptive introgression that shaped our evolutionary history.

show abstract

“…In the last decade, advancements in computing power and artificial intelligence methodologies have fueled a renaissance in the development of new machine learning frameworks [ 51 , 52 , 53 , 54 ] that detect and quantify diverse evolutionary processes. These powerful approaches have been developed to identify diverse adaptive events [ 51 , 54 , 55 ], which include different mechanisms of positive selection and the estimation of key underlying evolutionary genetic parameters, such as recombination rates, population sizes changes over time, strengths of selection, frequencies of beneficial variants when selection is initiated, and ages at which variants became adaptive [ 52 , 56 , 57 , 58 ]. These methods can be utilized with large genomic datasets and multiple populations, allowing for hypothesis testing between populations with different attributes.…”

Section: Methods For Detecting Positive Selectionmentioning

confidence: 99%

Understanding the Adaptive Evolutionary Histories of South American Ancient and Present-Day Populations via Genomics

Lindo

DeGiorgio

2021

Genes

View full text Add to dashboard Cite

The South American continent is remarkably diverse in its ecological zones, spanning the Amazon rainforest, the high-altitude Andes, and Tierra del Fuego. Yet the original human populations of the continent successfully inhabited all these zones, well before the buffering effects of modern technology. Therefore, it is likely that the various cultures were successful, in part, due to positive natural selection that allowed them to successfully establish populations for thousands of years. Detecting positive selection in these populations is still in its infancy, as the ongoing effects of European contact have decimated many of these populations and introduced gene flow from outside of the continent. In this review, we explore hypotheses of possible human biological adaptation, methods to identify positive selection, the utilization of ancient DNA, and the integration of modern genomes through the identification of genomic tracts that reflect the ancestry of the first populations of the Americas.

show abstract

Automatic inference of demographic parameters using Generative Adversarial Networks

Cited by 9 publications

References 51 publications

Extending approximate Bayesian computation with supervised machine learning to infer demographic history from genetic polymorphisms using DIYABC Random Forest

Extending approximate Bayesian computation with supervised machine learning to infer demographic history from genetic polymorphisms using DIYABC Random Forest

Detecting adaptive introgression in human evolution using convolutional neural networks

Understanding the Adaptive Evolutionary Histories of South American Ancient and Present-Day Populations via Genomics

Contact Info

Product

Resources

About