A survey of genetic and epigenetic variation affecting human gene expression
Bibliographic record
Abstract
The identification of human sequence polymorphisms that regulate gene expression is key to understanding human genetic diseases. We report a survey of human genes that demonstrate allelic differences in gene expression, reflecting the presence of putative allele-specific cis-acting factors of either genetic or epigenetic nature. The expression of allelic transcripts in heterozygous samples is assessed directly by relative quantitation of intragenic marker alleles in messenger or heteronuclear RNA derived from cells or tissues. This survey used 193 single-nucleotide polymorphisms (SNPs) from 129 genes expressed in lymphoblastoid cell lines, to identify 23 genes (18%) with common allele-specific transcripts whose expression deviated from the expected equimolar ratio. A subset of these deviations, or "allelic imbalances," can be observed in multiple samples derived from reference CEPH ("Centre d'Etude du Polymorphisme Humain") pedigrees and demonstrate a spectrum of patterns of transmission, including cosegregation of allelic skewing across generations compatible with Mendelian inheritance as well as random monoallelic expression for three genes (IL1A, HTR2A, and FGB). Additional studies for BTN3A2 provide evidence of SNPs and haplotypes in complete linkage disequilibrium with high- and low-expressing transcripts. The pipeline described herein offers tools for efficient identification and characterization of allelic expression allowing identification of regulatory sequence variants as well as epigenetic variation affecting human gene expression.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".