Whole-genome sequencing of African Americans implicates differential genetic architecture in inflammatory bowel disease
Bibliographic record
Abstract
Whether or not populations diverge with respect to the genetic contribution to risk of specific complex diseases is relevant to understanding the evolution of susceptibility and origins of health disparities. Here, we describe a large-scale whole-genome sequencing study of inflammatory bowel disease encompassing 1,774 affected individuals and 1,644 healthy control Americans with African ancestry (African Americans). Although no new loci for inflammatory bowel disease are discovered at genome-wide significance levels, we identify numerous instances of differential effect sizes in combination with divergent allele frequencies. For example, the major effect at PTGER4 fine maps to a single credible interval of 22 SNPs corresponding to one of four independent associations at the locus in European ancestry individuals but with an elevated odds ratio for Crohn disease in African Americans. A rare variant aggregate analysis implicates Ca 2+ -binding neuro-immunomodulator CALB2 in ulcerative colitis. Highly significant overall overlap of common variant risk for inflammatory bowel disease susceptibility between individuals with African and European ancestries was observed, with 41 of 241 previously known lead variants replicated and overall correlations in effect sizes of 0.68 for combined inflammatory bowel disease. Nevertheless, subtle differences influence the performance of polygenic risk scores, and we show that ancestry-appropriate weights significantly improve polygenic prediction in the highest percentiles of risk. The median amount of variance explained per locus remains the same in African and European cohorts, providing evidence for compensation of effect sizes as allele frequencies diverge, as expected under a highly polygenic model of disease.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".