Integrating environmental health and genomics research in Africa: challenges and opportunities identified during a Human Heredity and Health in Africa (H3Africa) Consortium workshop
Bibliographic record
Abstract
Individuals with African ancestry have extensive genomic diversity but have been underrepresented in genomic research. There is also extensive global diversity in the exposome (the totality of human environmental exposures from conception onwards) which should be considered for integrative genomic and environmental health research in Africa. To address current research gaps, we organized a workshop on environmental health research in Africa in conjunction with the H3Africa Consortium and the African Society of Human Genetics meetings in Kigali, Rwanda. The workshop was open to all researchers with an interest in environmental health in Africa and involved presentations from experts within and outside of the Consortium. This workshop highlighted innovative research occurring on the African continent related to environmental health and the interplay between the environment and the human genome. Stories of success, challenges, and collaborative opportunities were discussed through presentations, breakout sessions, poster presentations, and a panel discussion. The workshop informed participants about environmental risk factors that can be incorporated into current or future epidemiology studies and addressed research design considerations, biospecimen collection and storage, biomarkers for measuring chemical exposures, laboratory strategies, and statistical methodologies. Inclusion of environmental exposure measurements with genomic data, including but not limited to H3Africa projects, can offer a strong platform for building gene-environment (G x E) research in Africa. Opportunities to leverage existing resources and add environmental exposure data for ongoing and planned studies were discussed. Future directions include expanding the measurement of both genomic and exposomic risk factors and incorporating sophisticated statistical approaches for analyzing high dimensional G x E data. A better understanding of how environmental and genomic factors interact with nutrition and infection is also needed. Considering that the environment represents many modifiable risk factors, these research findings can inform intervention and prevention efforts towards improving global health.
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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 | 0.078 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.006 |
| Scholarly communication | 0.013 | 0.006 |
| Open science | 0.003 | 0.020 |
| Research integrity | 0.011 | 0.015 |
| Insufficient payload (model declined to judge) | 0.005 | 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".