Bayesian Inference Associates Rare <i>KDR</i> Variants With Specific Phenotypes in Pulmonary Arterial Hypertension
Bibliographic record
Abstract
Background: Approximately 25% of patients with pulmonary arterial hypertension (PAH) have been found to harbor rare mutations in disease-causing genes. To identify missing heritability in PAH, we integrated deep phenotyping with whole-genome sequencing data using Bayesian statistics. Methods: We analyzed 13 037 participants enrolled in the NBR study (NIHR BioResource—Rare Diseases), of which 1148 were recruited to the PAH domain. To test for genetic associations between genes and selected phenotypes of pulmonary hypertension, we used the Bayesian rare variant association method BeviMed. Results: Heterozygous, high impact, likely loss-of-function variants in the kinase insert domain receptor ( KDR ) gene were strongly associated with significantly reduced transfer coefficient for carbon monoxide (posterior probability=0.989) and older age at diagnosis (posterior probability=0.912). We also provide evidence for familial segregation of a rare nonsense KDR variant with these phenotypes. On computed tomographic imaging of the lungs, a range of parenchymal abnormalities were observed in the 5 patients harboring these predicted deleterious variants in KDR . Four additional PAH cases with rare likely loss-of-function variants in KDR were independently identified in the US PAH Biobank cohort with similar phenotypic characteristics. Conclusions: The Bayesian inference approach allowed us to independently validate KDR , which encodes for the VEGFR2 (vascular endothelial growth factor receptor 2), as a novel PAH candidate gene. Furthermore, this approach specifically associated high impact likely loss-of-function variants in the genetically constrained gene with distinct phenotypes. These findings provide evidence for KDR being a clinically actionable PAH gene and further support the central role of the vascular endothelium in the pathobiology of PAH.
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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 | 0.015 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".