Human Genetic Databases: Ethical, Legal and Social Issues: Preface to the Special Issue of Trames
Bibliographic record
Abstract
1. The boom of DNA banking The last few years have witnessed an important expansion of DNA banking all over the world. The collections of DNA samples vary in design and purpose (Cambon-Thomsen et al; 2003 Palmour 2003), occurring in a variety of circumstances from research to diagnostic and therapeutic activities, as well as in forensic services in identifying individuals through DNA. Most of these DNA banks are of small size, mostly set up in order to enable research in the context of disease studies. The very recent tendency to build up extensive population-based databases is related to the success of the Human Genome Project that has energized large-scale genetics and genomics research. While the mapping of the human genome has been a major scientific achievement, there still exists a large gap between gene discovery and our ability to utilize genetic information to improve health and prevent disease. The interest in human genetic variation and genetic epidemiology provide the basis for the construction of genetic databases. One hopes that the large-scale genetic databases enable us to understand the combined effects of genetic, lifestyle and environmental risk factors in the development of a disease. Also emerging areas of research, such as pharmacogenetics, also require access to large pools of genetic data. Pharmacogenetics, as the study of genetic variation that affects response to medical drugs, has the potential to improve the safety and efficacy of treatments, mostly by supporting the development of genetic tests that would allow to judge how likely a specific medicine is to help or harm a particular patient. People vary in their response to the same medicine due to the differences in their genetic make-up. The hope is that pharmacogenetics will enable to find the right medicine to the right patient in the right dosis. 2. What is a population-based genetic database? The population-based genetic databases rely on a large number of research subjects contributing their DNA samples in the form of blood or tissue that will be linked with medical, genealogical and lifestyle information. Iceland has been a pioneer in proposing a database complex consisting of Health Sector Database, genealogical database (Book of Icelanders) and Genetic Database. This was followed by the Estonian Genome Project (Estonia), UK BioBank (United Kingdom), CARTaGENE (Quebec, Canada), UmanGenomics (Vasterbotten, Sweden), Genome Database of the Latvian Population (Latvia), Genome Institute of Singapore (Singapore), Autogen Limited (Kingdom of Tonga). Also in USA and China projects are currently under way. The best overview of the planned genetic database projects can be found in Austin, Harding, McElroy 2003 and Cardinal, Deschenes 2003. It seems that all currently proposed population--based genetic database projects have the same goal--they intend to identify susceptibility genes for common diseases and attempt to improve the medical care and health of the populations involved. In some cases, like Iceland, Estonia and Latvia, the initiators of the projects also hope to boost the country's economy through expanding the biotechnology sector and creating new jobs. Although the database projects share the main objectives, they vary in size, subject participation, organization, as well as in the balance of government and commercial involvement. The planned projects have different consent procedures and only some (Estonia, Latvia) intend to give feedback to the participants. Researchers, physicians, patients, biotechnology firms and pharmaceutical companies are excited about the scientific and therapeutic potential presented by genetic databases. They are all interested in the discovery of the genetic causes of diseases and in the development of better treatments and cures. But their interests and motives for participating in genetic research are, however, different or even competing. …
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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 | 0.026 | 0.101 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.018 | 0.019 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.017 | 0.018 |
| Insufficient payload (model declined to judge) | 0.050 | 0.044 |
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".