GPCards: An integrated database of genotype–phenotype correlations in human genetic diseases

• DOI: 10.1016/j.csbj.2021.03.011
• Publication Date: 2021-03-23T08:45:34Z
• Authors: Bin Li; Zheng Wang; Qian Chen; Kuokuo Li; Xiaomeng Wang; Yijing Wang; Qian Zeng; Ying Han; Bin Lu; Yuwen Zhao; Rui Zhang; Li Jiang; Hongxu Pan; Tengfei Luo; Yi Zhang; Zhenghuan Fang; Xuewen Xiao; Xun Zhou; Rui Wang; Lu Zhou; Yige Wang; Zhenhua Yuan; Lu Xia; Jifeng Guo; Beisha Tang; Kun Xia; Guihu Zhao; Jinchen Li
• Keywords: 0301 basic medicine; 0303 health sciences; 03 medical and health sciences; Phenotype; Genotype; Variant; TP248.13-248.65; GPCards; Biotechnology; Data Article; 3. Good health

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.