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Record W2160331596 · doi:10.1093/hmg/ddi420

Mapping common regulatory variants to human haplotypes

2005· article· en· W2160331596 on OpenAlexafffund
Tomi Pastinen, Bing Ge, Scott Gurd, Tiffany Gaudin, Carole Doré, Mathieu Lemire, Patricia Lepage, Eef Harmsen, Thomas J. Hudson

Bibliographic record

VenueHuman Molecular Genetics · 2005
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetic Associations and Epidemiology
Canadian institutionsMcGill University and Génome Québec Innovation Centre
FundersCanadian Institutes of Health ResearchGenome CanadaBurroughs Wellcome Fund
KeywordsBiologyInternational HapMap ProjectHaplotypeGeneticsAlleleGeneSingle-nucleotide polymorphismPhenotypeExpression quantitative trait lociGenetic variationGenetic associationComputational biologyGenotype

Abstract

fetched live from OpenAlex

Inter-individual variation in gene expression has proven to be in part governed by genetic determinants, which may be trans- or cis-acting. The underlying cause of cis-acting regulatory variation has been identified in only a handful of the hundreds of genes shown to display differential allelic expression. In this report, we describe a systematic effort to map common cis-acting variants in 64 genes, using association methods in HapMap samples. We identified 16 loci (25%), each of which harbors common haplotypes that affect total expression of a gene, and a further 17 loci (27%) with evidence of haplotypes affecting relative allelic expression in heterozygote samples. Our survey suggests that detailed mapping of allele-specific in vivo expression will provide a rich source of regulatory SNPs or haplotypes that should be given high priority in association studies of human phenotypes.

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)0.001
metaresearch head score (Gemma)0.002
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.019
GPT teacher head0.286
Teacher spread0.267 · 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

Citations86
Published2005
Admission routes2
Has abstractyes

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