Genes and genomes and unnecessary complexity in precision medicine
Bibliographic record
Abstract
The sequencing of the human genome heralded the new age of 'genetic medicine' and raised the hope of precision medicine facilitating prolonged and healthy lives. Recent studies have dampened this expectation, as the relationships among mutations (termed 'risk factors'), biological processes, and diseases have emerged to be more complex than initially anticipated. In this review, we elaborate upon the nature of the relationship between genotype and phenotype, between chance-laden molecular complexity and the evolution of complex traits, and the relevance of this relationship to precision medicine. Molecular contingency, i.e., chance-driven molecular changes, in conjunction with the blind nature of evolutionary processes, creates genetic redundancy or multiple molecular pathways to the same phenotype; as time goes on, these pathways become more complex, interconnected, and hierarchically integrated. Based on the proposition that gene-gene interactions provide the major source of variation for evolutionary change, we present a theory of molecular complexity and posit that it consists of two parts, necessary and unnecessary complexity, both of which are inseparable and increase over time. We argue that, unlike necessary complexity, comprising all aspects of the organism's genetic program, unnecessary complexity is evolutionary baggage: the result of molecular constraints, historical circumstances, and the blind nature of evolutionary forces. In the short term, unnecessary complexity can give rise to similar risk factors with different genetic backgrounds; in the long term, genes become functionally interconnected and integrated, directly or indirectly, affecting multiple traits simultaneously. We reason that in addition to personal genomics and precision medicine, unnecessary complexity has consequences in evolutionary biology.
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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 | 0.009 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.033 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".