Frequencies of poor metabolizer alleles of 12 pharmacogenomic actionable genes in Punjabi Sikhs of Indian Origin

Sanghera, Dharambir K.; Bejar, Cynthia; Sapkota, Bishwa Raj; Wander, Gurpreet Singh; Ralhan, Sarju

doi:10.1038/s41598-018-33981-z

Cited by 11 publications

(7 citation statements)

References 46 publications

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“…There is a global concern to increase pharmacogenetic testing to ensure drug safety and enhance drug efficacy [125,126]. However, most GWAS to identify drug-response related variants have been performed in the western populations and others have lagged behind [7,127,128]. It is important to understand the interpopulation or interethnic variability in drug response so that population/ethnicity-specific guidelines can be produced.…”

Section: Resultsmentioning

confidence: 99%

“…Differences in drug response can also be caused by variants in leukocyte antigen genes and drug targets [3]. These variants can modulate efficacy of drugs as well as result in ADRs, which are major causes of hospitalizations and mortalities in both adults and children [4][5][6][7]. Such adverse reactions not only exacerbate the patients' illness, but also cause economic losses [8].…”

Section: Introductionmentioning

confidence: 99%

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Heterogeneity in the distribution of 159 drug-response related SNPs in world populations and their genetic relatedness

2020

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Interethnic variability in drug response arises from genetic differences associated with drug metabolism, action and transport. These genetic variations can affect drug efficacy as well as cause adverse drug reactions (ADRs). We retrieved drug-response related single nucleotide polymorphism (SNP) associated data from databases and analyzed to elucidate population specific distribution of 159 drug-response related SNPs in twenty six populations belonging to five super-populations (African, Admixed Americans, East Asian, European and South Asian). Significant interpopulation differences exist in the minor (variant) allele frequencies (MAFs), linkage disequilibrium (LD) and haplotype distributions among these populations. 65 of the drug-response related alleles, which are considered as minor (variant) in global population, are present as the major alleles (frequency �0.5) in at least one or more populations. Populations that belong to the same super-population have similar distribution pattern for majority of the variant alleles. These drug response related variant allele frequencies and their pairwise LD measure (r 2) can clearly distinguish the populations in a way that correspond to the known evolutionary history of human and current geographic distributions, while D' cannot. The data presented here may aid in identifying drugs that are more appropriate and/or require pharmacogenetic testing in these populations. Our findings emphasize on the importance of distinct, ethnicity-specific clinical guidelines, especially for the African populations, to avoid ADRs and ensure effective drug treatment.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Heterogeneity in the distribution of 159 drug-response related SNPs in world populations and their genetic relatedness

2020

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“…Further, an IoT e-health service finds the type of heart attack using mobile application based conceptual model but performance of the proposed application is not evaluated on cloud environments. Akrivopoulos et al [14] proposed an ECG-based Healthcare (ECGH) system to diagnose cardiac abnormalities [15] using ECG but has low accuracy and high response time of detecting abnormal events because they are fetching data directly without using data analytics or other feature extraction techniques. Further, the data transmission to cloud server in case of large number of requests increases latency and consumes more energy consumption, which degrades the performance of the system.…”

Section: Related Workmentioning

confidence: 99%

HealthFog: An ensemble deep learning based Smart Healthcare System for Automatic Diagnosis of Heart Diseases in integrated IoT and fog computing environments

Tuli

Basumatary

Gill

et al. 2020

Future Generation Computer Systems

495

248

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Cloud computing provides resources over the Internet and allows a plethora of applications to be deployed to provide services for different industries. The major bottleneck being faced currently in these cloud frameworks is their limited scalability and hence inability to cater to the requirements of centralized Internet of Things (IoT) based compute environments. The main reason for this is that latency-sensitive applications like health monitoring and surveillance systems now require computation over large amounts of data (Big Data) transferred to centralized database and from database to cloud data centers which leads to drop in performance of such systems. The new paradigms of fog and edge computing provide innovative solutions by bringing resources closer to the user and provide low latency and energy efficient solutions for data processing compared to cloud domains. Still, the current fog models have many limitations and focus from a limited perspective on either accuracy of results or reduced response time but not both. We proposed a novel framework called HealthFog for integrating ensemble deep learning in Edge computing devices and deployed it for a real-life application of automatic Heart Disease analysis. HealthFog delivers healthcare as a fog service using IoT devices and efficiently manages the data of heart patients, which comes as user requests. Fog-enabled cloud framework, FogBus is used to deploy and test the performance of the proposed model in terms of power consumption, network bandwidth, latency, jitter, accuracy and execution time. HealthFog is configurable to various operation modes which provide the best Quality of Service or prediction accuracy, as required, in diverse fog computation scenarios and for different user requirements.

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“…This system is restricted to small amounts of data and operates locally. Sanghera et al [ 32 ] developed a healthcare framework that uses ECG signals to detect heart abnormalities [ 33 ]. This system has certain limitations, such as low accuracy and excessive computation time, reducing its performance.…”

Section: Related Workmentioning

confidence: 99%

An ML-Enabled Internet of Things Framework for Early Detection of Heart Disease

Muhammad

Almoteri

Mujlid

et al. 2022

BioMed Research International

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Healthcare occupies a central role in sustainable societies and has an undeniable impact on the well-being of individuals. However, over the years, various diseases have adversely affected the growth and sustainability of these societies. Among them, heart disease is escalating rapidly in both economically settled and undeveloped nations and leads to fatalities around the globe. To reduce the death ratio caused by this disease, there is a need for a framework to continuously monitor a patient’s heart status, essentially doing early detection and prediction of heart disease. This paper proposes a scalable Machine Learning (ML) and Internet of Things-(IoT-) based three-layer architecture to store and process a large amount of clinical data continuously, which is needed for the early detection and monitoring of heart disease. Layer 1 of the proposed framework is used to collect data from IoT wearable/implanted smart sensor nodes, which includes various physiological measures that have significant impact on the deterioration of heart status. Layer 2 stores and processes the patient data on a local web server using various ML classification algorithms. Finally, Layer 3 is used to store the critical data of patients on the cloud. The doctor and other caregivers can access the patient health conditions via an android application, provide services to the patient, and inhibit him/her from further damage. Various performance evaluation measures such as accuracy, sensitivity, specificity, F1-measure, MCC-score, and ROC curve are used to check the efficiency of our proposed IoT-based heart disease prediction framework. It is anticipated that this system will assist the healthcare sector and the doctors in diagnosing heart patients in the initial phases.

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Frequencies of poor metabolizer alleles of 12 pharmacogenomic actionable genes in Punjabi Sikhs of Indian Origin

Cited by 11 publications

References 46 publications

Heterogeneity in the distribution of 159 drug-response related SNPs in world populations and their genetic relatedness

Heterogeneity in the distribution of 159 drug-response related SNPs in world populations and their genetic relatedness

HealthFog: An ensemble deep learning based Smart Healthcare System for Automatic Diagnosis of Heart Diseases in integrated IoT and fog computing environments

An ML-Enabled Internet of Things Framework for Early Detection of Heart Disease

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