Cited by 458Genetic diversity and population structureGenomics and Phylogenetic StudiesForensic and Genetic Research

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Supervised Machine Learning for Population Genetics: A New Paradigm

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Schrider, Daniel R., Kern, Andrew D.·2018·Trends in Genetics

DOI 10.1016/j.tig.2017.12.005PMC PMC5905713

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Abstract

As population genomic datasets grow in size, researchers are faced with the daunting task of making sense of a flood of information. To keep pace with this explosion of data, computational methodologies for population genetic inference are rapidly being developed to best utilize genomic sequence data. In this review we discuss a new paradigm that has emerged in computational population genomics: that of supervised machine learning (ML). We review the fundamentals of ML, discuss recent applications of supervised ML to population genetics that outperform competing methods, and describe promising future directions in this area. Ultimately, we argue that supervised ML is an important and underutilized tool that has considerable potential for the world of evolutionary genomics.

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“7,8 For instance, Ming et al 9 applied machine learning techniques, including LASSO, to identify critical genes involved in sepsis-associated ARDS and explored their relationship with immune cell infiltration, which has been recognized as a key factor in the pathogenesis of sepsis.”
Plant domestication revisited: Genomic insights into origins, mechanisms, and convergent evolution
Cao, Y., Yan, J., Ross‐Ibarra, J., & Yang, N. (2025) · iScience · doi:10.1016/j.isci.2025.114062 · Cited by 7
“221,222 Although these methods require extensive datasets, high computational power, and specialized expertise, they hold great promise for uncovering cryptic genetic features beyond the reach of conventional approaches, thereby expanding our understanding of the genetic basis of domestication.”
Effects of rescaling forward-in-time population genetic simulations
Marsh, J. I., Kaushik, S., & Johri, P. (2025) · Genetics · doi:10.1093/genetics/iyaf263 · Cited by 5
“In addition, simulation-based inference approaches use simulated data as input to infer population parameters with statistical approaches such as approximate Bayesian computation (ABC; Beaumont et al. 2002) and by training machine learning models (Schrider and Kern 2018; Korfmann et al. 2023 ).”
Integrating genomic technologies with artificial intelligence, CRISPR editing, and high-throughput phenotyping for engineering disease-resistant crops
Kamran, M., Zhijun, T., Ullah, A., Alvi, M. H. A., Aqeel, T., Xuejun, C., Sagheer, S., Umaira, Bingguang, X., & Xu, H. (2026) · Plant Stress · doi:10.1016/j.stress.2026.101317 · Cited by 7
HLBS-PopOmics: an online knowledge base to accelerate dissemination and implementation of research advances in population genomics to reduce the burden of heart, lung, blood, and sleep disorders
Mensah, G. A., Yu, W., Barfield, W. L., Clyne, M., Engelgau, M. M., & Khoury, M. J. (2018) · Genetics in Medicine · doi:10.1038/s41436-018-0118-1 · Cited by 5
Supervised Machine-Learning Algorithm using Low Data Sets: Flow Chemistry Optimization of the Key Urea Moiety Construction in Larotrectinib
Haripriya, T., Jayaraman, V. U., Uppada, M. K., Eda, V. R., Sen, S., Bandichhor, R., & Oruganti, S. (2024) · Organic Process Research & Development · doi:10.1021/acs.oprd.3c00489 · Cited by 5
Assessing simulation-based supervised machine learning for demographic parameter inference from genomic data
Quelin, A., Austerlitz, F., & Jay, F. (2025) · Heredity · doi:10.1038/s41437-025-00773-x · Cited by 8
“More recently, simulation-based approaches combined with supervised machine learning algorithms were developed for demographic inference (Schrider and Kern 2018; Collin et al. 2021; Korfmann et al. 2023).”
A Semi-Supervised Generative Adversarial Network for Prediction of Genetic Disease Outcomes
Davi, C. C. M., & Braga-Neto, U. (2021) · Machine learning for signal processing ... · doi:10.1109/mlsp52302.2021.9596351 · Cited by 5
Global coordination of the mutation and growth rates across the genetic and nutritional variety in Escherichia coli
Lao, Z., Matsui, Y., Ijichi, S., & Ying, B. (2022) · Frontiers in Microbiology · doi:10.3389/fmicb.2022.990969 · Cited by 5
“, 2020), genetics (Libbrecht and Noble, 2015; Schrider and Kern, 2018), evolution (Wang et al.”
Application of multivariate machine learning methods to investigate organic compound content of different pepper spices
Durmuş, Y., & Atasoy, A. F. (2022) · Food Bioscience · doi:10.1016/j.fbio.2022.102216 · Cited by 5
Elevator dynamic monitoring and early warning system based on machine learning algorithm
Zhang, S., Yin, Q., & Wang, J. (2022) · IET Networks · doi:10.1049/ntw2.12077 · Cited by 6
Phronesis and Automated Science: The Case of Machine Learning and Biology
Ratti, E. (2020) · Human perspectives in health sciences and technology · doi:10.1007/978-3-030-25001-0_8 · Cited by 5
A Neophyte With AutoML: Evaluating the Promises of Automatic Machine Learning ToolsPreprint
Bezrukavnikov, O., & Linder, R. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2101.05840 · Cited by 9
“TPOT was developed at the Computational Genetics Laboratory of the University of Pennsylvania with the goal of maximizing classification accuracy on supervised tasks [32].”
Method
Genetic diversity landscapes in outcrossing and selfing Caenorhabditis nematodesPreprint
Teterina, A., Willis, J. H., Lukac, M., Jovelin, R., Cutter, A., & Phillips, P. C. (2022) · bioRxiv · doi:10.1101/2022.12.13.520296 · Cited by 4
Utilizing Machine Learning Algorithms to Detect Emotions from Tweets
Julian, C. R., Athevan, G. R., Hanjaya, N. G., Lucky, H., & Suhartono, D. (2022) · International Conference on Advanced Informatics: Concepts, Theory and Applications (ICAICTA) · doi:10.1109/icaicta56449.2022.9932908 · Cited by 4
“According to [7], supervised machine learning is doing predictions using labeled data or trained datasets where the true values are acknowledged.”
Background
Power of neutrality tests for detecting natural selection
Tanaka, T., Hayakawa, T., & Teshima, K. M. (2023) · G3 Genes Genomes Genetics · doi:10.1093/g3journal/jkad161 · Cited by 7
A Novel Approach Utilizing Domain Adversarial Neural Networks for the Detection and Classification of Selective Sweeps
Song, H., Chu, J., Li, W., Li, X., Fang, L., Han, J., Zhao, S., & Ma, Y. (2024) · Advanced Science · doi:10.1002/advs.202304842 · Cited by 3
Design of Ni-based single crystal superalloys by machine learning based on data-driven multi-task optimization
Lian, L., Li, W., Zhang, Y., Gong, X., Wang, H., & Liu, Y. (2025) · Computational Materials Science · doi:10.1016/j.commatsci.2025.113969 · Cited by 4
Application of near-infrared spectroscopy for physicochemical characterization of soft and flint corn grains in pre-processing, storage and industrial unit as alternative to the subjective physical classification method
de Moraes, R. S., Bilhalva, N. D. S., Leal, M. M., Lemos, A. B., & Coradi, P. C. (2025) · Journal of Stored Products Research · doi:10.1016/j.jspr.2025.102717 · Cited by 4
Measuring natural selection on the transcriptome
Stinchcombe, J. R., & Kelly, J. K. (2025) · New Phytologist · doi:10.1111/nph.70287 · Cited by 4
“While this ﬁeld is moving quickly and a full review is beyond our scope here (see Schrider & Kern, 2018, for an entry point), there are several features of these algorithms that suggest promise in the context of measuring selection on gene expression.”
A Survey for Predicting ATP Binding Residues of Proteins Using Machine Learning Methods
Yang, Y. R., Wang, J.-S., Yuan, S.-S., Liu, M., Su, W., Lin, H., & Zhang, Z. (2021) · Current Medicinal Chemistry · doi:10.2174/0929867328666210910125802 · Cited by 4
Whole-Genome Sequencing of Plants: Past, Present, and Future
Usha, T., Panda, P., Goyal, A. K., Kukanur, A. A., Kamala, A., Prasannakumar, M. K., Sidhalinghamurthy, K. R., & Middha, S. K. (2022) · Springer eBooks · doi:10.1007/978-981-16-6974-3_8 · Cited by 4
A new method for detecting autocorrelation of evolutionary rates in large phylogeniesPreprint
Tao, Q., Tamura, K., Battistuzzi, F. U., & Kumar, S. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/346635 · Cited by 4
“Machine learning (McL) is widely used to solve problems in many fields, including ecology (Christin et al. 2018; Willcock et al. 2018) and population genetics (Saminadin-Peter et al. 2012; Schrider and Kern 2016; Schrider and Kern 2018).”
Background
Comparative evolutionary analysis and prediction of deleterious mutation patterns between sorghum and maizePreprint
Lozano, R. D., Gazave, É., dos Santos, J. P. R., Stetter, M. G., Valluru, R., Bandillo, N. B., Fernandes, S. B., Brown, P. J., Shakoor, N., Mockler, T. C., et al. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/777623 · Cited by 4
The reasonable effectiveness of domain adaptation for inference of introgressionPreprint
Cobb, K. A., & Smith, M. L. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.01.17.633659 · Cited by 5
“Specifically, when the models of interest are intractable in likelihood and Bayesian frameworks, supervised machine learning offers a promising solution (Schrider and Kern, 2018; Flagel et al., 2019; Korfmann et al., 2023).”
Deep learning insights into distinct patterns of polygenic adaptation across human populations
Tripathi, D., Bhattacharyya, C., & Basu, A. (2024) · Nucleic Acids Research · doi:10.1093/nar/gkae1027 · Cited by 3
Genomic advancements in goat breeding: enhancing productivity, disease resistance, and sustainability in India’s rural economy
Panigrahi, M., Nayak, S. S., Rajawat, D., Bose, A., Bharia, N., Das, S., Sharma, A., & Dutt, T. (2025) · Mammalian Genome · doi:10.1007/s00335-025-10138-8 · Cited by 8
Machine learning based on pangenome-wide association studies reveals the impact of host source on the zoonotic potential of closely related bacterial pathogens
Han, C., Lu, S., Hu, P., Chang, J., Zou, D., Li, F., Li, Y., Lü, Q., & Ren, H. (2025) · Communications Biology · doi:10.1038/s42003-025-08650-3 · Cited by 3
“Supervised ML uses labeled datasets to train algorithms such as Random Forest (RF), Decision Trees (DTC), Support Vector Machine (SVM), K-Nearest Neigh-bors (KNN), and Multilayer Perceptron (MLP) to predict output based on labeled input data 21 – 23 .”
Decoding the genomic symphony: unravelling brain disorders through data integration and machine learning
Bracher‐Smith, M., & Escott‐Price, V. (2025) · Molecular Psychiatry · doi:10.1038/s41380-025-03330-4 · Cited by 5
Neural posterior estimation for population genetics
Min, J., Ning, Y., Pope, N. S., Baumdicker, F., & Kern, A. D. (2026) · Genetics · doi:10.1093/genetics/iyag107 · Cited by 5
“Normalizing flows were introduced by Rezende and Mohamed (2015) and have since been extended to conditional density estimation for simulation-based inference (Papamakarios and Murray 2016; Lueckmann et al. 2017; Greenberg et al. 2019); see Papamakarios et al. (2021) for a comprehensive review and…”
Unveiling the Genomic Architecture of Phenotypic Plasticity Using Multiple GWAS Approaches Under Contrasting Conditions of Water Availability: A Model for Barley
Arenas, S., & Cortés, A. J. (2026) · International Journal of Molecular Sciences · doi:10.3390/ijms27020652 · Cited by 3
“Machine learning models identified a broader set of associations [52,53], likely capturing non-linear and interactive effects, while BLINK and XGBoost prioritized highly conservative signals.”
On the Utility of Deep Learning for Model Classification and Parameter Estimation on Complex Diversification Scenarios
de la Peña, P. G., Iglesias, G., Talavera, E., Meseguer, A. S., & Sanmartín, I. (2026) · Systematic Biology · doi:10.1093/sysbio/syag030 · Cited by 3
Omics Technologies in Molecular Biology
Cortés, A. J. (2026) · International Journal of Molecular Sciences · doi:10.3390/ijms27083349 · Cited by 3

Authors: Schrider, D. R., & Kern, A. D.
Affiliations: Rutgers, The State University of New Jersey (US)
Corresponding author: Schrider, Daniel R.
Kern, Andrew D.
ORCID: Schrider, Daniel R.
Kern, Andrew D.
Venue: Trends in Genetics
Publisher: Elsevier BV
Published: 2018-04
Volume: 34
Issue: 4
Pages: 301-312
ISSN: 0168-9525 (ISSN-L)
DOI: 10.1016/j.tig.2017.12.005
PMC: PMC5905713
OpenAlex: W2783880833
Cited by: 453
References: 110
Citations by year: 2026: 41 · 2025: 55 · 2024: 51 · 2023: 50 · 2022: 68 · 2021: 65 · 2020: 57 · 2019: 46 · 2018: 20
FWCI: 6.48
Type: journal article
Language: en
Open access: hybrid
doi.org
Licence: cc-by
www.elsevier.com/tdm/userlicense/1.0/ (tdm)
www.elsevier.com/legal/tdmrep-license (tdm)
creativecommons.org/licenses/by/4.0/ (vor)
Locations: Trends in Genetics (publishedVersion, cc-by)
bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
PubMed (publishedVersion)
Europe PMC (PubMed Central) (acceptedVersion, cc-by)
PubMed Central (acceptedVersion, cc-by)
Funders: National Institutes of Health
National Human Genome Research Institute (K99HG008696)
National Institute of General Medical Sciences (R01GM117241)
Topics: Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic diversity and population structure
Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Forensic and Genetic Research
Keywords: supervised learning, population genetics, population genetic inference, genome sequencing data, evolutionary genomics
MeSH terms: Supervised Machine Learning, Biological Evolution, Genetics, Population, Humans, Selection, Genetic, Genome, Human, Data Mining, Datasets as Topic

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Supervised Machine Learning for Population Genetics: A New Paradigm

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