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Record W4392185185 · doi:10.1086/730186

Conditionally Deleterious Mutation Load Accumulates in Genomic Islands of Local Adaptation but Can Be Purged with Sufficient Genotypic Redundancy

2024· article· en· W4392185185 on OpenAlexaff
Jonathan A. Mee, Bryce Carson, Sam Yeaman

Bibliographic record

VenueThe American Naturalist · 2024
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicEvolution and Genetic Dynamics
Canadian institutionsUniversity of CalgaryMount Royal University
Fundersnot available
KeywordsBiologyLocal adaptationMaladaptationGeneticsLocus (genetics)Balancing selectionAdaptation (eye)AlleleRedundancy (engineering)PhenotypeGenotypeGenetic loadEvolutionary biologyPopulationGeneComputer scienceNeuroscience

Abstract

fetched live from OpenAlex

AbstractLocal adaptation frequently evolves in patches or environments that are connected via migration. In these cases, genomic regions that are linked to a locally adapted locus experience reduced effective migration rates. Via individual-based simulations of a two-patch system, we show that this reduced effective migration results in the accumulation of conditionally deleterious mutations, but not universally deleterious mutations, adjacent to adaptive loci. When there is redundancy in the genetic basis of local adaptation (i.e., genotypic redundancy), turnover of locally adapted polymorphisms allows conditionally deleterious mutation load to be purged. The amount of mutational load that accumulates adjacent to locally adapted loci is dependent on redundancy, recombination rate, migration rate, population size, strength of selection, and the phenotypic effect size of adaptive alleles. Our results highlight the need to be cautious when interpreting patterns of local adaptation at the level of phenotype or fitness, as the genetic basis of local adaptation can be transient, and evolution may confer a degree of maladaptation to nonlocal environments.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.660
Threshold uncertainty score0.335

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.007
GPT teacher head0.249
Teacher spread0.242 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2024
Admission routes1
Has abstractyes

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