ArticleAmerican journal of human genetics2016
Phenotype Similarity Regression for Identifying the Genetic Determinants of Rare Diseases.
Article in American journal of human genetics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers.
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Who cites it
33 citing papers in PubMed.
- A Hypergraph powered approach to Phenotype-driven Gene Prioritization and Rare Disease Prediction.Scientific reports · 2025Article
- Whole-exome sequencing in pediatric patients with glomerulonephritis.Frontiers in genetics · 2025Article
- Pheno-Ranker: a toolkit for comparison of phenotypic data stored in GA4GH standards and beyond.BMC bioinformatics · 2024Article
- Diagnostic efficacy and clinical utility of whole-exome sequencing in Czech pediatric patients with rare and undiagnosed diseases.Scientific reports · 2024Article
- Concordance Between Biochemical and Molecular Diagnosis Obtained by WES in Mexican Patients with Inborn Errors of Intermediary Metabolism: Utility for Therapeutic Management.International journal of molecular sciences · 2024Article
- Variants in HCFC1 and MN1 genes causing intellectual disability in two Pakistani families.BMC medical genomics · 2024Article
- Whole exome sequencing in Serbian patients with hereditary spastic paraplegia.Neurogenetics · 2024Article
- PheSeq, a Bayesian deep learning model to enhance and interpret the gene-disease association studies.Genome medicine · 2024Article
- Identification of Novel and Recurrent Variants inJournal of clinical medicine · 2024Article
- Brachyolmia, dental anomalies and short stature (DASS): Phenotype and genotype analyses of Egyptian and Pakistani patients.Heliyon · 2024Article
- Autosomal Dominant, Long-Standing Dysglycemia in 2 Families with Unique Phenotypic Features.Clinical medicine insights. Endocrinology and diabetes · 2024Article
- Structural and functional implications of SLC13A3 and SLC9A6 mutations: an in silico approach to understanding intellectual disability.BMC neurology · 2023Article
- Machine learning in rare disease.Nature methods · 2023Review
- Genetic association analysis of 77,539 genomes reveals rare disease etiologies.Nature medicine · 2023Article
- Autosomal recessive variants c.953A>C and c.97-1G>C in NSUN2 causing intellectual disability: a molecular dynamics simulation study of loss-of-function mechanisms.Frontiers in neurology · 2023Article
- Enhanced rare disease mapping for phenome-wide genetic association in the UK Biobank.Genome medicine · 2022Article
- Multi-faceted semantic clustering with text-derived phenotypes.Computers in biology and medicine · 2021Article
- Article
- Powerful use of automated prioritization of candidate variants in genetic hearing loss with extreme etiologic heterogeneity.Scientific reports · 2021Article
- Development and application of the ocular immune-mediated inflammatory diseases ontology enhanced with synonyms from online patient support forum conversation.Computers in biology and medicine · 2021Article
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4 authors.
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Abstract
Rare genetic disorders, which can now be studied systematically with affordable genome sequencing, are often caused by high-penetrance rare variants. Such disorders are often heterogeneous and characterized by abnormalities spanning multiple organ systems ascertained with variable clinical precision. Existing methods for identifying genes with variants responsible for rare diseases summarize phenotypes with unstructured binary or quantitative variables. The Human Phenotype Ontology (HPO) allows composite phenotypes to be represented systematically but association methods accounting for the ontological relationship between HPO terms do not exist. We present a Bayesian method to model the association between an HPO-coded patient phenotype and genotype. Our method estimates the probability of an association together with an HPO-coded phenotype characteristic of the disease. We thus formalize a clinical approach to phenotyping that is lacking in standard regression techniques for rare disease research. We demonstrate the power of our method by uncovering a number of true associations in a large collection of genome-sequenced and HPO-coded cases with rare diseases.
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