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Cited by 24Insect and Arachnid Ecology and BehaviorAnimal Behavior and ReproductionEvolutionary Game Theory and Cooperation

Sociomics: Using Omic Approaches to Understand Social Evolution

Ghoul, Melanie, Andersen, Sandra B., West, Stuart A.·2017·Trends in Genetics
DOI 10.1016/j.tig.2017.03.009
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Abstract

All of life is social, from genes cooperating to form organisms, to animals cooperating to form societies. Omic approaches offer exceptional opportunities to solve major outstanding problems in the study of how sociality evolves. First, omics can be used to clarify the extent and form of sociality in natural populations. This is especially useful in species where it is difficult to study social traits in natural populations, such as bacteria and other microbes. Second, omics can be used to examine the consequences of sociality for genome evolution and gene expression. This is especially useful in cases where there is clear variation in the level of sociality, such as the social insects. Major tasks for the future are to apply these approaches to a wider range of non-model organisms, and to move from exploratory analyses to the testing of evolutionary theory.

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  • Similar problems, and a need for phylogenetic methods, arise when using data from other omic methods, such as transcriptomics, metagenomics or metabolomics.

    “Similar problems, and a need for phylogenetic methods, arise when using data from other omic methods, such as transcriptomics, metagenomics or metabolomics61–64.”

    From A phylogenetic approach to comparative genomics (2025) · cites this paper as [64]

    The paper identifies interpretive challenges for omic data in “Challenges to the interpretation of omic data” (p. 13), and “Concluding Remarks” (p. 10) asks whether omic signatures show patterns across phylogenies. “Statistical power in comparative studies” (p. 13) discusses phylogenetically independent transitions, but only for comparative genomic analyses; it does not establish a general need for phylogenetic methods when analysing transcriptomic, metagenomic, or metabolomic data. (LLM, conf=0.82)

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Cites

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Research works printed but not linked: 28

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Authors
Ghoul, M., Andersen, S. B., & West, S. A.
Affiliations
  • University of Oxford (GB)
  • NYU Langone Health (US)
Corresponding author
  • Ghoul, Melanie
  • Andersen, Sandra B.
ORCID
Venue
Trends in Genetics
Publisher
Elsevier BV
Published
2017-06
Volume
33
Issue
6
Pages
408-419
ISSN
0168-9525 (ISSN-L)
DOI
10.1016/j.tig.2017.03.009
OpenAlex
W2613233718
Cited by
24
References
2
Citations by year
2025: 2 · 2024: 4 · 2023: 4 · 2022: 1 · 2021: 6 · 2020: 2 · 2019: 1 · 2018: 4
FWCI
0.25
Type
journal article
Language
en
Open access
closed
Licence
www.elsevier.com/tdm/userlicense/1.0/ (tdm)
Locations
  • Trends in Genetics (publishedVersion)
  • PubMed (publishedVersion)
  • Oxford University Research Archive (ORA) (University of Oxford) (acceptedVersion)
  • Research at the University of Copenhagen (University of Copenhagen) (submittedVersion)
Funders
Novo Nordisk Fonden (NNF16OC0018638)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Insect and Arachnid Ecology and Behavior
  • Life Sciences › Agricultural and Biological Sciences › Ecology, Evolution, Behavior and Systematics › Animal Behavior and Reproduction
  • Social Sciences › Sociology and Political Science › Evolutionary Game Theory and Cooperation
Keywords
social evolution, sociality, genome evolution, gene expression, social insects, microbial cooperation, non-model organisms
MeSH terms
Animals, Biological Evolution, Gene Expression Regulation, Social Behavior, Genome, Genomics, Proteomics

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