GPCards: An integrated database of genotype–phenotype correlations in human genetic diseases
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 urgen...
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| Format: | Article |
| Language: | English |
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Elsevier
2021-01-01
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| Series: | Computational and Structural Biotechnology Journal |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2001037021000830 |
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Article |
| collection |
DOAJ |
| language |
English |
| format |
Article |
| sources |
DOAJ |
| author |
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 |
| spellingShingle |
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 GPCards: An integrated database of genotype–phenotype correlations in human genetic diseases Computational and Structural Biotechnology Journal GPCards Phenotype Genotype Variant |
| author_facet |
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 |
| author_sort |
Bin Li |
| title |
GPCards: An integrated database of genotype–phenotype correlations in human genetic diseases |
| title_short |
GPCards: An integrated database of genotype–phenotype correlations in human genetic diseases |
| title_full |
GPCards: An integrated database of genotype–phenotype correlations in human genetic diseases |
| title_fullStr |
GPCards: An integrated database of genotype–phenotype correlations in human genetic diseases |
| title_full_unstemmed |
GPCards: An integrated database of genotype–phenotype correlations in human genetic diseases |
| title_sort |
gpcards: an integrated database of genotype–phenotype correlations in human genetic diseases |
| publisher |
Elsevier |
| series |
Computational and Structural Biotechnology Journal |
| issn |
2001-0370 |
| publishDate |
2021-01-01 |
| description |
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. |
| topic |
GPCards Phenotype Genotype Variant |
| url |
http://www.sciencedirect.com/science/article/pii/S2001037021000830 |
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| _version_ |
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doaj-d776ccf35aa543dab9583e7539073d3c2021-03-31T04:08:38ZengElsevierComputational and Structural Biotechnology Journal2001-03702021-01-011916031611GPCards: An integrated database of genotype–phenotype correlations in human genetic diseasesBin Li0Zheng Wang1Qian Chen2Kuokuo Li3Xiaomeng Wang4Yijing Wang5Qian Zeng6Ying Han7Bin Lu8Yuwen Zhao9Rui Zhang10Li Jiang11Hongxu Pan12Tengfei Luo13Yi Zhang14Zhenghuan Fang15Xuewen Xiao16Xun Zhou17Rui Wang18Lu Zhou19Yige Wang20Zhenhua Yuan21Lu Xia22Jifeng Guo23Beisha Tang24Kun Xia25Guihu Zhao26Jinchen Li27National Clinical Research Center for Geriatric Disorders, Department of Geriatrics, Xiangya Hospital, Central South University, Changsha, Hunan 410008, China; Department of Neurology, Xiangya Hospital, Central South University, Changsha, Hunan 410008, China; Mobile Health Ministry of Education - China Mobile Joint Laboratory, Xiangya Hospital, Central South University, Changsha, Hunan 410008, ChinaNational Clinical Research Center for Geriatric Disorders, Department of Geriatrics, Xiangya Hospital, Central South University, Changsha, Hunan 410008, ChinaNational Clinical Research Center for Geriatric Disorders, Department of Geriatrics, Xiangya Hospital, Central South University, Changsha, Hunan 410008, ChinaCenter for Medical Genetics & Hunan Key Laboratory, School of Life Sciences, Central South University, Changsha Hunan 410008, ChinaCenter for Medical Genetics & Hunan Key Laboratory, School of Life Sciences, Central South University, Changsha Hunan 410008, ChinaCenter for Medical Genetics & Hunan Key Laboratory, School of Life Sciences, Central South University, Changsha Hunan 410008, ChinaDepartment of Neurology, Xiangya Hospital, Central South University, Changsha, Hunan 410008, ChinaCenter for Medical Genetics & Hunan Key Laboratory, School of Life Sciences, Central South University, Changsha Hunan 410008, ChinaDepartment of Pathogen Biology, School of Basic Medical Sciences, Central South University, Changsha, Hunan 410008, ChinaDepartment of Neurology, Xiangya Hospital, Central South University, Changsha, Hunan 410008, ChinaDepartment of Neurology, Xiangya Hospital, Central South University, Changsha, Hunan 410008, ChinaDepartment of Neurology, Xiangya Hospital, Central South University, Changsha, Hunan 410008, ChinaDepartment of Neurology, Xiangya Hospital, Central South University, Changsha, Hunan 410008, ChinaCenter for Medical Genetics & Hunan Key Laboratory, School of Life Sciences, Central South University, Changsha Hunan 410008, ChinaNational Clinical Research Center for Geriatric Disorders, Department of Geriatrics, Xiangya Hospital, Central South University, Changsha, Hunan 410008, ChinaCenter for Medical Genetics & Hunan Key Laboratory, School of Life Sciences, Central South University, Changsha Hunan 410008, ChinaDepartment of Neurology, Xiangya Hospital, Central South University, Changsha, Hunan 410008, ChinaDepartment of Neurology, Xiangya Hospital, Central South University, Changsha, Hunan 410008, ChinaCenter for Medical Genetics & Hunan Key Laboratory, School of Life Sciences, Central South University, Changsha Hunan 410008, ChinaDepartment of Neurology, Xiangya Hospital, Central South University, Changsha, Hunan 410008, ChinaDepartment of Neurology, Xiangya Hospital, Central South University, Changsha, Hunan 410008, ChinaDepartment of Neurology, Xiangya Hospital, Central South University, Changsha, Hunan 410008, ChinaCenter for Medical Genetics & Hunan Key Laboratory, School of Life Sciences, Central South University, Changsha Hunan 410008, ChinaDepartment of Neurology, Xiangya Hospital, Central South University, Changsha, Hunan 410008, ChinaNational Clinical Research Center for Geriatric Disorders, Department of Geriatrics, Xiangya Hospital, Central South University, Changsha, Hunan 410008, China; Department of Neurology, Xiangya Hospital, Central South University, Changsha, Hunan 410008, ChinaCenter for Medical Genetics & Hunan Key Laboratory, School of Life Sciences, Central South University, Changsha Hunan 410008, ChinaNational Clinical Research Center for Geriatric Disorders, Department of Geriatrics, Xiangya Hospital, Central South University, Changsha, Hunan 410008, China; Department of Neurology, Xiangya Hospital, Central South University, Changsha, Hunan 410008, China; Corresponding authors at: National Clinical Research Center for Geriatric Disorders, Department of Geriatrics, Xiangya Hospital, Central South University, Changsha, Hunan 410008, China.National Clinical Research Center for Geriatric Disorders, Department of Geriatrics, Xiangya Hospital, Central South University, Changsha, Hunan 410008, China; Center for Medical Genetics & Hunan Key Laboratory, School of Life Sciences, Central South University, Changsha Hunan 410008, China; Department of Neurology, Xiangya Hospital, Central South University, Changsha, Hunan 410008, China; Corresponding authors at: National Clinical Research Center for Geriatric Disorders, Department of Geriatrics, Xiangya Hospital, Central South University, Changsha, Hunan 410008, China.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.http://www.sciencedirect.com/science/article/pii/S2001037021000830GPCardsPhenotypeGenotypeVariant |