Human gene and disease associations for clinical‐genomics and precision medicine research

Zeeshan Ahmed, Saman Zeeshan, Dinesh Mendhe, XinQi Dong

Publish Year: 2020

Abstract: We are entering the era of personalized medicine in which an individual’s genetic makeup will eventually determine how a doctor can tailor his or her therapy. Therefore, it is becoming critical to understand the genetic basis of common diseases, for example, which genes predispose and rare genetic variants contribute to diseases, and so on. Our study focuses on helping researchers, medical practitioners, and pharmacists in having a broad view of genetic variants that may be implicated in the likelihood of developing certain diseases. Our focus here is to create a comprehensive database with mobile access to all available, authentic and actionable genes, SNPs, and classified diseases and drugs collected from different clinical and genomics databases worldwide, including Ensembl, GenCode, ClinVar, GeneCards, DISEASES, HGMD, OMIM, GTR, CNVD, Novoseek, Swiss‐Prot, LncRNADisease, Orphanet, GWAS Catalog, SwissVar, COSMIC, WHO, and FDA. We present a new cutting‐edge gene‐SNP‐disease‐drug mobile database with a smart phone application, integrating information about classified diseases and related genes, germline and somatic mutations, and drugs. Its database includes over 59 000 protein‐coding and noncoding genes; over 67 000 germline SNPs and over a million somatic mutations reported for over 19 000 protein‐coding genes located in over 1000 regions, published with over 3000 articles in over 415 journals available at the PUBMED; over 80 000 ICDs; over 123 000 NDCs; and over 100 000 classified gene‐SNP‐disease associations. We present an application that can provide new insights into the information about genetic basis of human complex diseases and contribute to assimilating genomic with phenotypic data for the availability of gene‐based designer drugs, precise targeting of molecular fingerprints for tumor, appropriate drug therapy, predicting individual susceptibility to disease, diagnosis, and treatment of rare illnesses are all a few of the many transformations expected in the decade to come.

Conclusions: This is the era of Big Data, where human‐related biological databases continue to grow not only in count but also in volume, posing unprecedented challenges in data storage, processing, exchange, and curation. We developed a cutting‐edge gene‐SNP‐disease‐drug database accessible through a smart phone application, integrating information about classified diseases and related genes, germline and somatic mutations, and drugs. The study focused on developing a tool that might help others (mainly researchers, medical practitioners, and pharmacists) in having a broad view of genetic variants that may be implicated in the likelihood of developing certain diseases.1 We have developed a platform that can provide new understandings into the information about genetic basis of human complex diseases and contribute to assimilating genomic with phenotypic data for the availability of gene‐based designer drugs, precise targeting of molecular fingerprints for tumor, appropriate drug therapy, predicting individual susceptibility to disease, diagnosis and treatment of rare illnesses are all a few of the many transformations expected in the decade to come.
https://doi.org/10.1002/ctm2.28

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