The Search for Genetic Modifiers of Disease Severity in the -Hemoglobinopathies
Bibliographic record
Abstract
Sickle cell disease (SCD) and β-thalassemia, two monogenic diseases caused by mutations in the β-globin gene, affect millions of individuals worldwide. These hemoglobin disorders are characterized by extreme clinical heterogeneity, complicating patient management and treatment. A better understanding of this patient-to-patient clinical variability would dramatically improve care and might also guide the development of novel therapies. Studies of the natural history of these β-hemoglobinopathies have identified fetal hemoglobin levels and concomitant α-thalassemia as important modifiers of disease severity. Several small-scale studies have attempted to identify additional genetic modifiers of SCD and β-thalassemia, without much success. Fortunately, improved knowledge of the human genome and the development of new genomic tools, such as genome-wide genotyping arrays and next-generation DNA sequencers, offer new opportunities to use genetics to better understand the causes of the many complications observed in β-hemoglobinopathy patients. Here I discuss the most important factors to consider when planning an experiment to find associations between β-hemoglobinopathy-related complications and DNA sequence variants, with a focus on how to successfully perform a genome-wide association study. I also review the literature and explain why most published findings in the field of SCD modifier genetics are likely to be false-positive reports, with the goal to draw lessons allowing investigators to design better genetic experiments.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".