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

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.

Keywords: clinical-genomics; database; diseases; drugs; genes; germline mutations; precision medicine; somatic mutations.

FIGURE 1

PAS components design, development, and data flow. PAS is an iOS app developed with Swift programming language, XCODE integrated development environment for MacOS, MySQL database management system, PHP scripting language, and UNIX‐based web and database servers

FIGURE 2

PAS graphical user interface and work flow design

FIGURE 3

PAS (iPhone 11 Pro) screenshot of gene results from searches for the 10 most common infectious diseases in the United States: (A) 22 chlamydia, (B) 86 influenza, (C) 31 staph, (D) 103 herpes, (E) 16 shigellosis, (F) 102 syphilis, (G) 185 pneumonia, (H) 325 hepatitis‐C, (I) 20 common cold, and (J) 17 salmonellosis

FIGURE 4

PAS (iPhone 11 Pro) screen shots present searched results for all possible SNPs related to diabetes (2174), immune (2583), and schizophrenia (2286) diseases; searched results for diabetes‐related SNPs: HNF1 (142), HNF4A (46), and PAX4 (2); searched results for auto immune disease‐related SNPs: HLA‐DRB1 (216), TNFRSF1A (35), and PTPN22 (44); and searched results for schizophrenia‐related SNPs: DRD2 (10), CACNB2 (45), and GRM3 (16)

FIGURE 5

PAS (iPhone 11 Pro) screen shots present eight searched results for all possible SNPs related to the eight different genes: 216 results for “ESR1” (A), 178 results for “AKT1” (B), 600 results for “ERBB24” (C), 344 results for “BRCA1” (D), 574 results for “BRCA2” (E), 125 results for “RBM10” (F), 110 results for “PTPN13” (G), and 71 results for “PPP6C” (H). Figure 4 also presents four examples of searched results for all possible SNPs and their diseases relationships: 216 results for entered and searched keyword “ESR1” (I), 178 results for entered and searched keyword “AKT1” (J), 600 results for “ERBB24” (K), and 344 results for “BRCA1” (L)

FIGURE 6

PAS (iPhone 11 Pro) screen shots present searched results obtained during use cases for four different diseases: diabetes, influenza, fever, and sterile. Diabetes includes total 577 ICD (A), 69 ICD9 (B), 508 ICD10 (C), and 6 NDC (D). Influenza includes total 44 ICD (E), 16 ICD9 (F), 28 ICD10 (G), and 14 NDC (H). Fever includes total 110 ICD (I), 48 ICD9 (J), 62 ICD10 (K), and 284 NDC (L). Sterile includes total 18 ICD (M), 9 ICD9 (N), 9 ICD10 (O), and 138 NDC (P)

FIGURE 7

PAS (iPhone 11 Pro) screen shots present searched results obtained for infectious disease i.e., Coronavirus (Fig. 7A), genes (Fig. 7B and 7C), and variants (Fig. 7D and 7E) including ACE2 and TMPRSS2