GA4GH Phenopacket-Driven Characterization of Genotype-Phenotype Correlations in Mendelian Disorders
Rekerle, L.; Danis, D.; Rehburg, F.; Graefe, A. S.; Bily, V.; Caballero-Oteyza, A.; Cacheiro, P.; Chimirri, L.; Chong, J. X.; Connelly, E.; de Vries, B. B.; Dingemans, A. J.; Duyzend, M. H.; Freiberger, T.; Gehle, P.; Groza, T.; Hansen, P.; Jacobsen, J.; Klocperk, A.; Ladewig, M. S.; Love, M. I.; Marcello, A. J.; Mordhorst, A.; Munoz-Torres, M. C.; Reese, J.; Schuetz, C.; Smedley, D.; Strauss, T.; Vladyka, O.; Zocche, D.; Thun, S.; Mungall, C. J.; Haendel, M. A.; Robinson, P. N.
Show abstract
Comprehensively characterizing genotype-phenotype correlations (GPCs) in Mendelian disease would create new opportunities for improving clinical management and understanding disease biology. However, heterogeneous approaches to data sharing, reuse, and analysis have hindered progress in the field. We developed Genotype Phenotype Evaluation of Statistical Association (GPSEA), a software package that leverages the Global Alliance for Genomics and Health (GA4GH) Phenopacket Schema to represent case-level clinical and genetic data about individuals. GPSEA applies an independent filtering strategy to boost statistical power to detect categorical GPCs represented by Human Phenotype Ontology terms. GPSEA additionally enables visualization and analysis of continuous phenotypes, clinical severity scores, and survival data such as age of onset of disease or clinical manifestations. We applied GPSEA to 85 cohorts with 6613 previously published individuals with variants in one of 80 genes associated with 122 Mendelian diseases and identified 225 significant GPCs, with 48 cohorts having at least one statistically significant GPC. These results highlight the power of standardized representations of clinical data for scalable discovery of GPCs in Mendelian disease.
Matching journals
The top 2 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Towards robust clinical genome interpretation: developing a consistent terminology to characterize disease-gene relationships - allelic requirement, inheritance modes and disease mechanisms 95%
- The Importance of Automation in Genetic Diagnosis: Lessons from Analyzing an Inherited Retinal Degeneration Cohort with the Mendelian Analysis Toolkit (MATK) 95%
- A gene pathogenicity tool 'GenePy' identifies missed biallelic diagnoses in the 100,000 Genomes Project 95%
Similar papers in this journal
Similar papers in this journal
- Prioritization of disease genes from GWAS using ensemble based positive-unlabeled learning 94%
- Polycomb-associated and Trithorax-associated developmental conditions – phenotypic convergence and heterogeneity 94%
- A genome-wide association analysis of loss of ambulation in dystrophinopathy patients suggests multiple candidate modifiers of disease severity 94%
Similar papers in this journal
- IMPROVE-DD: Integrating Multiple Phenotype Resources Optimises Variant Evaluation in genetically determined Developmental Disorders 96%
- Gene Specific Pathogenicity Predictor for Chromatin-Remodeling BAF Complex-Associated Neurodevelopmental Disorders 94%
- Long-read genome sequencing for the diagnosis of neurodevelopmental disorders 93%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.