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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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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