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Record W3129927710 · doi:10.1101/2021.02.20.432102

Widespread cryptic variation in genetic architecture between the sexes

2021· preprint· en· W3129927710 on OpenAlexaff
Wouter van der Bijl, Judith E. Mank

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2021
Typepreprint
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetic and Clinical Aspects of Sex Determination and Chromosomal Abnormalities
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsSexual dimorphismGenetic architectureBiologyPhenotypeEvolutionary biologyGenetic variationGeneGeneticsZoology

Abstract

fetched live from OpenAlex

Abstract The majority of the genome is shared between the sexes, and it is expected that the genetic architecture of most traits is shared as well. This common architecture has been viewed as a major source of constraint on the evolution of sexual dimorphism (SD). SD is nonetheless common in nature, leading to assumptions that it results from differential regulation of shared genetic architecture. Here, we study the effect of thousands of gene knock-out mutations on 202 mouse phenotypes to explore how regulatory variation affects SD. We show that many traits are dimorphic to some extent, and that a surprising proportion of knock-outs have sex-specific phenotypic effects. Many traits, regardless whether they are monomorphic or dimorphic, harbor cryptic differences in genetic architecture between the sexes, resulting in sexually discordant phenotypic effects from sexually concordant regulatory changes. This provides an alternative route to dimorphism through sex-specific genetic architecture, rather than differential regulation of shared architecture.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.010
GPT teacher head0.226
Teacher spread0.216 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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
Published2021
Admission routes1
Has abstractyes

Explore more

Same venuebioRxiv (Cold Spring Harbor Laboratory)→Same topicGenetic and Clinical Aspects of Sex Determination and Chromosomal Abnormalities→French-language works237,207→