From Prehistory to Precision Medicine: Are Genetic Risk Scores Our Next Step?
Bibliographic record
Abstract
Since the dawn of civilization, man has demonstrated understanding of, and the ability to exploit, the concept of heredity. The earliest known evidence of selective breeding dates to the Mesolithic era, and a 6000-year-old tablet from Babylon shows a horse pedigree containing suggestions of the species’ hereditary traits1. Despite lacking a physical or chemical explanation of the substance transferred from parent to offspring, Gregor Mendel’s 1865 paper2, which defined the laws of monogenetic heredity, gave an indication of its existence. Around the same time, Francis Galton, a cousin of Charles Darwin, evaluated the effect of nature versus nurture through a considerable number of family and twin studies3. The work of the 2 scientists eventually gave rise to 2 fundamentally different views on heredity, with the critics of Mendelian inheritance arguing that it could not possibly explain traits displaying extreme individual variation in a population, such as stature. The dispute was finally resolved in 1918, when R.A. Fisher4 elegantly demonstrated that when a large number of genes with a Mendelian inheritance pattern act on top of each other, the result is a normally distributed variation in the characteristic, such as that seen for stature. Today, a century after Fisher’s publication, we have only just begun to comprehend the magnitude of genetic factors influencing a particular phenotype. For many complex diseases, hundreds or thousands of single-nucleotide polymorphisms (SNP) appear to be involved in the etiology5, each typically with an almost negligible effect size. With this realization, the early hopes that genome-wide association studies (GWAS) would lead to the identification of persons at risk of a particular disease, ultimately allowing new preventive and therapeutic strategies, were frustrated6. Thus, there remains a critical need for the development of new methods that integrate the … Address correspondence to Dr. S. Reid, Uppsala University, Medical Sciences, Akademiska sjukhuset, Ingång 40, 5 tr, 751 85 Uppsala, Sweden. Email: sarah.reid{at}medsci.uu.se.
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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 | 0.027 | 0.110 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.010 | 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".