ArticleComputational and structural biotechnology journal2021
GPCards: An integrated database of genotype-phenotype correlations in human genetic diseases.
Article in Computational and structural biotechnology journal, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed, 14 citations in OpenAlex.
- geneEX: An Integrated Phenotype-Driven Algorithm for Rapid Identification of Causative Variants in Monogenic Disorders.Molecular genetics & genomic medicine · 2025Article
- The application of Large Language Models to the phenotype-based prioritization of causative genes in rare disease patients.Scientific reports · 2025Article
- Prevalence of comorbidities in individuals with neurodevelopmental disorders from the aggregated phenomics data of 51,227 pediatric individuals.Nature medicine · 2024Article
- Starvar: symptom-based tool for automatic ranking of variants using evidence from literature and genomes.BMC bioinformatics · 2023Article
- Repurposing Drugs via Network Analysis: Opportunities for Psychiatric Disorders.Pharmaceutics · 2022Review
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Authors and funding
28 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Genotype-phenotype correlations are the basis of precision medicine of human genetic diseases. However, it remains a challenge for clinicians and researchers to conveniently access detailed individual-level clinical phenotypic features of patients with various genetic variants. To address this urgent need, we manually searched for genetic studies in PubMed and catalogued 8,309 genetic variants in 1,288 genes from 17,738 patients with detailed clinical phenotypic features from 1,855 publications. Based on genotype-phenotype correlations in this dataset, we developed an user-friendly online database called GPCards (http://genemed.tech/gpcards/), which not only provided the association between genetic diseases and disease genes, but also the prevalence of various clinical phenotypes related to disease genes and the patient-level mapping between these clinical phenotypes and genetic variants. To accelerate the interpretation of genetic variants, we integrated 62 well-known variant-level and gene-level genomic data sources, including functional predictions, allele frequencies in different populations, and disease-related information. Furthermore, GPCards enables automatic analyses of users' own genetic data, comprehensive annotation, prioritization of candidate functional variants, and identification of genotype-phenotype correlations using custom parameters. In conclusion, GPCards is expected to accelerate the interpretation of genotype-phenotype correlations, subtype classification, and candidate gene prioritisation in human genetic diseases.
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