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Record W2914101505 · doi:10.1111/mec.15051

The genomic pool of standing structural variation outnumbers single nucleotide polymorphism by threefold in the marine teleost <i>Chrysophrys auratus</i>

2019· article· en· W2914101505 on OpenAlexaff
Andrew Catanach, Ross Crowhurst, Cecilia Deng, Charles David, Louis Bernatchez, Maren Wellenreuther

Bibliographic record

VenueMolecular Ecology · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetic diversity and population structure
Canadian institutionsUniversité Laval
FundersMinistry of Business, Innovation and Employment
KeywordsBiologySingle-nucleotide polymorphismGeneticsPolymorphism (computer science)GeneGenotype

Abstract

fetched live from OpenAlex

Recent studies have highlighted an important role of structural variation (SV) in ecological and evolutionary processes, but few have studied nonmodel species in the wild. As part of our long-term research programme on the nonmodel teleost fish Australasian snapper (Chrysophrys auratus), we aim to build one of the first catalogues of genomic variants (SNPs and indels, and deletions, duplications and inversions) in fishes and evaluate overlap of genomic variants with regions under putative selection (Tajima's D and π), and coding sequences (genes). For this, we analysed six males and six females from three locations in New Zealand and generated a high-resolution genomic variation catalogue. We characterized 20,385 SVs and found they intersected with almost a third of all annotated genes. Together with small indels, SVs account for three times more variation in the genome in terms of bases affected compared to SNPs. We found that a sizeable portion of detected SVs was in the upper and lower genomic regions of Tajima's D and π, indicating that some of these have an effect on the phenotype. Together, these results shed light on the often neglected genomic variation that is produced by SVs and highlights the need to go beyond the mere measure of SNPs when investigating evolutionary processes, such as species diversification and adaptation.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.769
Threshold uncertainty score0.455

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 head0.004
GPT teacher head0.195
Teacher spread0.190 · 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 designBench or experimental
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

Citations108
Published2019
Admission routes1
Has abstractyes

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