scholarly journals An exploratory assessment of the applicability of direct-to-consumer genetic testing to translational research in Japan

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Masahiro Inoue ◽  
Shota Arichi ◽  
Tsuyoshi Hachiya ◽  
Anna Ohtera ◽  
Seok-Won Kim ◽  
...  
Keyword(s):  
Genetic Testing ◽  
Translational Research ◽  
Drug Target ◽  
Target Genes ◽  
Healthy Individuals ◽  
Alcohol Sensitivity ◽  
Direct To Consumer ◽  
Disease States ◽  
Multiple Phenotypes ◽  
Related Phenotype

Abstract Objective In order to assess the applicability of a direct-to-consumer (DTC) genetic testing to translational research for obtaining new knowledge on relationships between drug target genes and diseases, we examined possibility of these data by associating SNPs and disease related phenotype information collected from healthy individuals. Results A total of 12,598 saliva samples were collected from the customers of commercial service for SNPs analysis and web survey were conducted to collect phenotype information. The collected dataset revealed similarity to the Japanese data but distinguished differences to other populations of all dataset of the 1000 Genomes Project. After confirmation of a well-known relationship between ALDH2 and alcohol-sensitivity, Phenome-Wide Association Study (PheWAS) was performed to find association between pre-selected drug target genes and all the phenotypes. Association was found between GRIN2B and multiple phenotypes related to depression, which is considered reliable based on previous reports on the biological function of GRIN2B protein and its relationship with depression. These results suggest possibility of using SNPs and phenotype information collected from healthy individuals as a translational research tool for drug discovery to find relationship between a gene and a disease if it is possible to extract individuals in pre-disease states by properly designed questionnaire.

Experiences of individuals receiving “Not Parent Expected” (NPE) results through direct-to-consumer genetic testing

2021 ◽  
Vol 132 ◽  
pp. S289
Author(s):  
Julia Becker ◽  
Janey Youngblom ◽  
Brianne Kirkpatrick ◽  
Liane Abrams
Keyword(s):  
Genetic Testing ◽  
Direct To Consumer

scholarly journals Integrated network analysis identifying potential novel drug candidates and targets for Parkinson's disease

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Pusheng Quan ◽  
Kai Wang ◽  
Shi Yan ◽  
Shirong Wen ◽  
Chengqun Wei ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Drug Target ◽  
Target Genes ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Functional Enrichment ◽  
Control Group ◽  
Drug Candidates ◽  
Novel Drug

AbstractThis study aimed to identify potential novel drug candidates and targets for Parkinson’s disease. First, 970 genes that have been reported to be related to PD were collected from five databases, and functional enrichment analysis of these genes was conducted to investigate their potential mechanisms. Then, we collected drugs and related targets from DrugBank, narrowed the list by proximity scores and Inverted Gene Set Enrichment analysis of drug targets, and identified potential drug candidates for PD treatment. Finally, we compared the expression distribution of the candidate drug-target genes between the PD group and the control group in the public dataset with the largest sample size (GSE99039) in Gene Expression Omnibus. Ten drugs with an FDR < 0.1 and their corresponding targets were identified. Some target genes of the ten drugs significantly overlapped with PD-related genes or already known therapeutic targets for PD. Nine differentially expressed drug-target genes with p < 0.05 were screened. This work will facilitate further research into the possible efficacy of new drugs for PD and will provide valuable clues for drug design.

scholarly journals Secondary Data Usage in Direct-to-Consumer Genetic Testing: To What Extent Are Customers Aware and Concerned?

2021 ◽  
pp. 1-8
Author(s):  
Janessa Mladucky ◽  
Bonnie Baty ◽  
Jeffrey Botkin ◽  
Rebecca Anderson
Keyword(s):  
Genetic Testing ◽  
Secondary Data ◽  
Structured Interviews ◽  
Customer Data ◽  
Secondary Use ◽  
Data Usage ◽  
Direct To Consumer ◽  
New Information ◽  
Opt Out ◽  
Potential Risks

Introduction: Customer data from direct-to-consumer genetic testing (DTC GT) are often used for secondary purposes beyond providing the customer with test results. Objective: The goals of this study were to determine customer knowledge of secondary uses of data, to understand their perception of risks associated with these uses, and to determine the extent of customer concerns about privacy. Methods: Twenty DTC GT customers were interviewed about their experiences. The semi-structured interviews were transcribed, coded, and analyzed for common themes. Results: Most participants were aware of some secondary uses of data. All participants felt that data usage for research was acceptable, but acceptability for non-research purposes varied across participants. The majority of participants were aware of the existence of a privacy policy, but few read the majority of the privacy statement. When previously unconsidered uses of data were discussed, some participants expressed concern over privacy protections for their data. Conclusion: When exposed to new information on secondary uses of data, customers express concerns and a desire to improve consent with transparency, more opt-out options, improved readability, and more information on future uses and potential risks from direct-to-consumer companies. Effective ways to improve readership about the secondary use, risk of use, and protection of customer data should be investigated and the findings implemented by DTC companies to protect public trust in these practices.

scholarly journals Artificial intelligence-based computational framework for drug-target prioritization and inference of novel repositionable drugs for Alzheimer’s disease

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Shingo Tsuji ◽  
Takeshi Hase ◽  
Ayako Yachie-Kinoshita ◽  
Taiko Nishino ◽  
Samik Ghosh ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Deep Learning ◽  
Drug Target ◽  
Target Genes ◽  
Drug Repositioning ◽  
Therapeutic Targets ◽  
Machine Learning Techniques ◽  
Computational Framework ◽  
Novel Therapeutic

Abstract Background Identifying novel therapeutic targets is crucial for the successful development of drugs. However, the cost to experimentally identify therapeutic targets is huge and only approximately 400 genes are targets for FDA-approved drugs. As a result, it is inevitable to develop powerful computational tools that can identify potential novel therapeutic targets. Fortunately, the human protein-protein interaction network (PIN) could be a useful resource to achieve this objective. Methods In this study, we developed a deep learning-based computational framework that extracts low-dimensional representations of high-dimensional PIN data. Our computational framework uses latent features and state-of-the-art machine learning techniques to infer potential drug target genes. Results We applied our computational framework to prioritize novel putative target genes for Alzheimer’s disease and successfully identified key genes that may serve as novel therapeutic targets (e.g., DLG4, EGFR, RAC1, SYK, PTK2B, SOCS1). Furthermore, based on these putative targets, we could infer repositionable candidate-compounds for the disease (e.g., tamoxifen, bosutinib, and dasatinib). Conclusions Our deep learning-based computational framework could be a powerful tool to efficiently prioritize new therapeutic targets and enhance the drug repositioning strategy.

scholarly journals Identification of anticancer drug target genes using an outside competitive dynamics model on cancer signaling networks

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Tien-Dzung Tran ◽  
Duc-Tinh Pham
Keyword(s):  
Anticancer Drug ◽  
Drug Target ◽  
Target Genes ◽  
Signaling Network ◽  
Competitive Dynamics ◽  
Signaling Networks ◽  
Molecular Signaling ◽  
Dynamics Model ◽  
Cancer Signaling ◽  
The Impact

AbstractEach cancer type has its own molecular signaling network. Analyzing the dynamics of molecular signaling networks can provide useful information for identifying drug target genes. In the present study, we consider an on-network dynamics model—the outside competitive dynamics model—wherein an inside leader and an opponent competitor outside the system have fixed and different states, and each normal agent adjusts its state according to a distributed consensus protocol. If any normal agent links to the external competitor, the state of each normal agent will converge to a stable value, indicating support to the leader against the impact of the competitor. We determined the total support of normal agents to each leader in various networks and observed that the total support correlates with hierarchical closeness, which identifies biomarker genes in a cancer signaling network. Of note, by experimenting on 17 cancer signaling networks from the KEGG database, we observed that 82% of the genes among the top 3 agents with the highest total support are anticancer drug target genes. This result outperforms those of four previous prediction methods of common cancer drug targets. Our study indicates that driver agents with high support from the other agents against the impact of the external opponent agent are most likely to be anticancer drug target genes.

scholarly journals Direct-to-Consumer Testing 2.0: Emerging Models of Direct-to-Consumer Genetic Testing

2018 ◽  
Vol 93 (1) ◽  
pp. 113-120 ◽  
Author(s):  
Megan A. Allyse ◽  
David H. Robinson ◽  
Matthew J. Ferber ◽  
Richard R. Sharp
Keyword(s):  
Genetic Testing ◽  
Direct To Consumer ◽  
Consumer Testing
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