The Genetic Architecture of Pigmentation Traits in Modern Human Populations
Bibliographic record
Abstract
Pigmentation traits, here defined as hair, eye and constitutive skin pigmentation are some the most variable phenotypes among human populations. They have a complex genetic basis, in which interactions among genes and pleiotropy are common. Several genes involved in the pigmentation pathway have been described across mammalian species, which explain a significant proportion of the normal pigmentation variation in humans. However, the full understanding of the genetic architecture of pigmentation in human populations has not yet been achieved. Major effect genes are known to explain a relatively high proportion of the variance for each pigmentation trait at a population-basis level, but a substantial number of genetic loci modulating pigmentation in several populations have small effects and remain unknown. The objective of my thesis was to contribute to and improve the state of knowledge of the genetic architecture of pigmentation traits in modern humans. To achieve this, I have taken advantage of novel approaches at different stages of the research program, such as the use of large sample sizes and different cohorts, the application of diverse statistical approaches, and the use of genomic and epigenomic databases and computational advances to follow-up putative causal loci. I have identified a complex genetic architecture across known pigmentation regions such as TYR and OCA2, in which a combination of missense and non-coding SNPs are independently associated with pigmentation traits. These associations seem to vary among populations and among pigmentation traits to certain extent, as evidenced by fine-mapping and genetic correlations in a Canadian cohort of European ancestry. Finally, I explored the putative regulatory role of pigmentation loci. I have identified shared causal signals between hair or eye colour and quantitative trait loci (i.e. methylation and expression), and I have identified significant associations between hair or eye colour and the expression of pigmentation genes such as OCA2 and SLC24A4. Overall, my research has nominated several candidate causal variants across loci, and has provided insights to further explore the biological role of these SNPs in human pigmentation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".