Digest: Gene duplication and social evolution-Using big, open data to answer big, open questions
Bibliographic record
Abstract
What genomic features underpin the evolution of behavioral complexity? Do species exhibiting such complexity have more genes, more complex gene regulation, or both? These questions have driven decades of research in humans and primates. Social insect biologists have wanted to explore these questions in eusocial species for decades (Gadagkar ), but have lacked access to appropriately selected genome sequences. With the sequencing efforts of the last decade, a dozen bee genomes are now publicly available (Kapheim et al. ), and more are on the way. This growing database allows us to look with ever‐increasing detail at the genomic correlates of social evolution within insects (e.g., Kapheim et al. ). In this issue, Chau and Goodisman () take advantage of the recent sequencing of multiple bee genomes and assess the relationship between sociality and the rate of gene duplication across bee species. Their samples span the social spectrum, ranging from the solitary, annual leafcutter bee (Megachile rotundata) to the highly eusocial, perennial, European honey bee (Apis mellifera). Within Apoidea, eusociality has evolved independently across multiple taxa, making this group an excellent system to directly examine the genomic changes associated with social organization (Rehan and Toth ). Chau and Goodisman () anticipated, and discovered, a modest trend for higher rates of gene duplication at increasing levels of social complexity (Fig. 0001). After establishing a set of duplicated genes, they conducted multiple separate gene expression experiments using eusocial honey bee genomes to quantify differences in gene expression between duplicate genes and singletons. The authors found greater differences between castes and sexes for duplicated genes than singletons. Using a eusocial honey bee dataset, they were then able to quantify how newly duplicated genes are expressed and how selection acts on these duplicates. The expression patterns of duplicated genes suggest that conservation is the predominant evolutionary process underpinning phenotypic diversity in bees. However, neofunctionalization and specialization also appear to be important evolutionary processes. Collectively, the results of the aforementioned gene expression analyses provide the strongest evidence for the importance of gene duplication to the evolution of disparate social phenotypes. The trend uncovered in Chau and Goodisman () between rate of gene duplication and level of social complexity within the apoidean bees. Chau and Goodisman () provide a useful jumping‐off point for investigating social evolution from a genomic perspective. They highlight avenues for continued investigation (i.e., novel genes) as well as muddy points to clarify. One such point is the persistent problem of other correlates of molecular evolution—chiefly, effective population size (Ne; Lynch and Walsh ). Ne is a critical parameter that influences how efficient selection is at removing deleterious mutations (or newly duplicated genes). If eusocial species have smaller Ne than their solitary relatives (e.g., Romiguier et al. ), we may expect higher inferred gene duplication rates along with more polymorphic weakly deleterious mutations. Chau and Goodisman's () results are particularly interesting in light of past hypotheses on the role of duplication in social evolution (Gadagkar ). Duplication and brief periods of relaxed constraint may have allowed the divergence of social insect castes and sexes as hypothesized by Gadagkar (). The conclusions reached in this study should be explored further across the Blattodea, Hemiptera, and Coleoptera, which also have species that vary in social complexity. This will allow us to determine whether the same relationships uncovered here are preserved across a broader range of taxa. Associate Editor: K. Moore Handling Editor: M. Noor Digests are short (∼500 word), news articles about selected original research included in the journal, written by students or postdocs. These digests are published online and linked to their corresponding original research articles. For instructions on Digests preparation and submission, please visit the following link https://sites.duke.edu/evodigests/. This article corresponds to Chau, L. M. and M. A. D. Goodisman. 2017. Gene duplication and the evolution of phenotypic diversity in insect societies. Evolution. https://doi.org/10.1111/evo.13356.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.042 | 0.043 |
| Insufficient payload (model declined to judge) | 0.009 | 0.007 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".