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Record W2729655313 · doi:10.1186/s12711-017-0331-4

Putative enhancer sites in the bovine genome are enriched with variants affecting complex traits

2017· article· en· W2729655313 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueGenetics Selection Evolution · 2017
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenomics and Chromatin Dynamics
Canadian institutionsnot available
FundersAgriculture VictoriaState Government of VictoriaUniversity of Alberta
KeywordsEnhancerBiologyGeneticsGeneBovine genomeGenomeEnhancer RNAsRegulatory sequenceRegulation of gene expressionComputational biologyGene expression

Abstract

fetched live from OpenAlex

BACKGROUND: Enhancers are non-coding DNA sequences, which when they are bound by specific proteins increase the level of gene transcription. Enhancers activate unique gene expression patterns within cells of different types or under different conditions. Enhancers are key contributors to gene regulation, and causative variants that affect quantitative traits in humans and mice have been located in enhancer regions. However, in the bovine genome, enhancers as well as other regulatory elements are not yet well defined. In this paper, we sought to improve the annotation of bovine enhancer regions by using publicly available mammalian enhancer information. To test if the identified putative bovine enhancer regions are enriched with functional variants that affect milk production traits, we performed genome-wide association studies using imputed whole-genome sequence data followed by meta-analysis and enrichment analysis. RESULTS: We produced a library of candidate bovine enhancer regions by using publicly available bovine ChIP-Seq enhancer data in combination with enhancer data that were identified based on sequence homology with human and mouse enhancer databases. We found that imputed whole-genome sequence variants associated with milk production traits in 16,581 dairy cattle were enriched with enhancer regions that were marked by bovine-liver H3K4me3 and H3K27ac histone modifications from both permutation tests and gene set enrichment analysis. Enhancer regions that were identified based on sequence homology with human and mouse enhancer regions were not as strongly enriched with trait-associated sequence variants as the bovine ChIP-Seq candidate enhancer regions. The bovine ChIP-Seq enriched enhancer regions were located near genes and quantitative trait loci that are associated with pregnancy, growth, disease resistance, meat quality and quantity, and milk quality and quantity traits in dairy and beef cattle. CONCLUSIONS: Our results suggest that sequence variants within enhancer regions that are located in bovine non-coding genomic regions contribute to the variation in complex traits. The level of enrichment was higher in bovine-specific enhancer regions that were identified by detecting histone modifications H3K4me3 and H3K27ac in bovine liver tissues than in enhancer regions identified by sequence homology with human and mouse data. These results highlight the need to use bovine-specific experimental data for the identification of enhancer regions.

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.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.969
Threshold uncertainty score0.554

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 head0.014
GPT teacher head0.252
Teacher spread0.238 · 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