Influence of human genome polymorphism on gene expression
Bibliographic record
Abstract
Genetic variation, through its effects on gene expression, plays a crucial role in phenotypic variation and disease susceptibility. Recent studies from our group and others have integrated a number of resources and technologies to assess several aspects of genome variation affecting gene expression. Some of these large-scale mapping studies involving expression quantitative traits have recently been reviewed [Gibson, G. and Weir, B. (2005) The quantitative genetics of transcription. Trends Genet., 21, 616-623; de Koning, D.J. and Haley, C.S. (2005) Genetical genomics in humans and model organisms. Trends Genet., 21, 377-381], with particular attention to the statistical issues. In this review, we compare allele-specific expression studies in human samples (primarily lymphoblastoid cell lines from the CEPH HapMap panel), as a prelude to a discussion on study design issues and sources of variation, in order to propose the steps required to build a detailed map of cis-acting regulatory variation in the human genome. Obtaining panels of tissues from large numbers of individuals remains an important limitation. We also conclude that there is insufficient knowledge as to the feasibility of comprehensive studies of trans-acting variation in the human genome.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".