Deep Learning in Population Genetics

Abstract

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.

Domain-adaptive neural networks improve supervised machine learning based on simulated population genetic data

“…Standard approaches to supervised machine learning rest on the assumption that the data they are used to analyze follow essentially the same distribution as the data used for training. In applications in population genetics, the training data are typically generated by simulation, leading to concerns about potential biases from simulation misspecification when supervised machine-learning methods are used in place of more traditional summary-statistic-or model-based methods (Caldas et al 2022;Korfmann et al 2023). In this article, we have shown that techniques from the "domain adaptation" literature can effectively be used to address this problem.…”

“…This synthetic training data serves as the foundation of the new simulate-and-train paradigm of supervised machine learning for population genetics inference ( Fig. 1A, Schrider and Kern 2018; Korfmann et al 2023).…”

“…At the same time, this paradigm is highly dependent on well-specified models for simulation (Korfmann et al 2023). If the simulation assumptions do not match the underlying generative process of the real data—that is, in the presence of simulation mis-specification —the trained deep-learning model may reflect the biases in the simulated data and perform poorly on real data.…”

“…Given adequately rigorous and scalable computational tools for analysis, these rich catalogs of genetic variation provide opportunities for addressing many important questions in areas such as human evolution, plant genetics, and the ecology of non-model organisms. Deep-learning methods, already well-established in other application areas (LeCun et al 2015), have proven to be good matches for these analytical tasks and have recently been successfully applied to many problems in population genetics (Sheehan and Song 2016;Kern and Schrider 2018;Schrider and Kern 2018;Flagel et al 2019;Torada et al 2019;Adrion et al 2020;Caldas et al 2022;Hejase et al 2022;Korfmann et al 2023).…”

Inference of population genetics parameters using discriminator neural networks: an adversarial Monte Carlo approach

“…The task is generally framed as a classification problem, or sometimes a regression problem, where the network itself learns which aspects of the data are informative for the parameters in question. Since their introduction to the field, CNNs have been used to infer a variety of biological parameters and evolutionary processes, such as recombination rates, population sizes, migration rates and historical split times, as well as episodes of positive selection and adaptive introgression, to name a few (recently reviewed by Korfmann et al, 2023). Methods based on neural networks have produced very accurate results, as evaluated using simulations, where the ground truth is known.…”