Polygenic scores as a clinical diagnosis tool for abnormal hematological values
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,015 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,005 | 0,003 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,006 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».