Fish and chips: the origin of human gene families is a predictor of the location of GWAS signals
Bibliographic record
Abstract
Abstract GWAS have identified thousands of loci associated with human complex diseases and traits. How these loci are distributed through the genome has not been systematically evaluated. We hypothesised that the location of GWAS loci differ between ancestral linkage groups (ALGs) related to the paralogy and function of genes. We used data from the NHGRI-EBI GWAS catalog to determine whether the density of GWAS loci relative to HapMap variants in each ALG differed, and whether ALG’s were enriched for experimental factor ontological (EFO) terms assigned to the GWAS traits. In a gene-level analyses we explored the characteristics of genes linked to GWAS loci and those mapping to the ALG’s. We find that GWAS loci were enriched or deficient in 9 and 7 of the 17 ALG’s respectively, while there was no difference in the number of GWAS loci in regions of the human genome unassigned to an ALG. All but 2 ALG’s were significantly enriched or deficient for one or more EFO terms. Lastly, we find that genes assigned to an ALG are under higher levels of selective constraint, have longer coding sequences and higher median expression in the tissue of highest expression than genes not mapping to an ALG. On the other hand, genes associated with GWAS loci have longer genomic length and exhibit higher levels of selective constraint relative to non-GWAS genes.Collectively, this suggests that understanding the location and ancestral origins of GWAS signals may be informative for the development of tools for variant prioritization and interpretation.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".