Polygenic scores as a clinical diagnosis tool for abnormal hematological values
Bibliographic record
Abstract
Abstract Background Abnormal hematological values are one of the most frequent reasons for hematological consultations. However, a hematological condition is not always diagnosed, and some hematological abnormality values remain unexplained. Polygenic score (PGS) built from genome-wide association studies have been shown to be associated with hematological values. The PGS value reflects the genetically determined hematological value of an individual. Whether PGS can be useful in a hematological clinical setting is unknown. Indeed, whether an extreme value of a PGS can, by itself, explain an abnormal hematological value remains to be studied. We aimed to assess whether individuals with extreme PGS values were likely to have abnormal hematological values. Methods We gathered data from individuals in the UK Biobank who had no conditions or treatments that could affect their hematological values. From these 200,816 individuals (108,745 females and 92,071 males), we used published cross-ancestry PGS to compute the PGS for five different hematological values: hemoglobin (HGB), mean corpuscular volume (MCV), platelet count (PLT), mean platelet volume (MPV), and neutrophil count (NEU). We defined individuals with extreme PGS value as those with a low PGS (i.e., PGS percentile ≤0.1%) or a high PGS (i.e., PGS percentile ≥0.1%). We defined two levels of abnormal hematological values: mildly abnormal (values above or below the normal range without reaching the markedly abnormal threshold) and markedly abnormal (values above or below a more extreme threshold). For HGB, we considered different thresholds for males and females, as the normal value depends on sex. Results The percentage of mildly abnormal hematological value among the individuals with extreme PGS varies according to the hematological parameter considered. For HGB, we found the opposite proportion depending on sex: 21% of females with a low PGS had HGB <120 g/L whereas only 7% of males with a low PGS had a HGB <130 g/L. On the contrary, 3% of females with a high PGS had a HGB >160 g/L and 23% of males with a high PGS had a HGB >165 g/L. For MCV, 8% of individuals with a low PGS had a MCV <80 fL, and 19% of those with a high PGS had a MCV >100 fL. For PLT, 21% of individuals with a low PGS had a PLT <150x109/L, whereas only 6% of those with a high PGS had a PLT >450x109/L. For MPV, 9% with a low PGS had a MPV <7 fL and 100% of individuals with high PGS had a MPV >9 fL. For NEU, 8% with a low PGS had a NEU <1.5x109/L and 13% with a high PGS had a NEU >7.5x109/L. Markedly abnormal hematological values were uncommon in patients with extreme PGS value, as it concerned <3% of individuals for HGB (thresholds: <105 and >175 g/L for female <115 and >180 g/L for male), MCV (<75 and > 105 fL), PLT (<100 and >500x109/L), low MPV (<6 fL), and NEU (<1.5 and >8.0x109/L). Markedly high MPV (>10 fL) was found in 81% of individuals with a high PGS. Conclusion In sum, an extreme PGS value can explain mildly abnormal hematological value for some parameters: low HGB in females, high HGB in males, high MCV, low PLT, high MPV, and to a lesser extent high NEU. In these settings, PGS testing can be considered as a diagnosis tool of for a mildly abnormal hematological value. The effect is especially pronounced for MPV. For the other mildly abnormal hematological values and for all the markedly abnormal hematological values (except for MPV), the PGS does not provide an explanation for the presence of these abnormal values. In these settings, genetically determined variation of the normal should not be retained as a diagnosis.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".