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Data Resources and Approaches for Precision Medicine Research: New Developments...
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Data Resources and Approaches for Precision Medicine Research: New Developments and Applications

A special issue of Journal of Personalized Medicine (ISSN 2075-4426). This special issue belongs to the section "Omics/Informatics".

Deadline for manuscript submissions: closed (31 October 2021) | Viewed by 8439

Special Issue Editor

Dr. Dana C. Crawford

E-Mail Website
Guest Editor
Departments of Population and Quantitative Health Sciences and Genetics and Genome Sciences, Cleveland Institute for Computational Biology, Case Western Reserve University, Cleveland, OH, USA
Interests: human genetics; genomics; electronic health records; precision medicine; diversity

Special Issue Information

Dear Colleagues,

Technological developments in omics and informatics over the past ten years have enabled the generation of different data types from biospecimens linked to health, lifestyle, and exposure data extracted from multiple resources ranging from the traditional epidemiologic to the emerging hybrid cohort models offering access to electronic health records (EHRs).  In parallel, methodological developments both in data harmonization and statistical approaches encourage synthesis of data and powerful meta-analyses across studies and populations to generate new opportunities for risk stratification in precision medicine research. 

The aim of this Special Issue is to showcase the development of data resources and statistical methods and their use in precision medicine research.  We are inviting the submission of original articles and reviews for this Special Issue.  Example manuscript topics include (but are not limited to) reviews and critical comparisons of resources with biospecimens linked to health data, reviews and critical assessments and original research of methods developed for multiple data types, original research in methods development or applications of polygenic risk scores, original research in computable or electronic phenotyping using EHRs, and original research in methods development or application to datasets with multiple genetic ancestries.

Dr. Dana C. Crawford
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Journal of Personalized Medicine is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Polygenic risk scores
  • Electronic health records
  • Biobanks
  • Computable phenotyping
  • Genetic ancestry
  • Precision medicine
  • Whole genome sequencing
  • Whole exome sequencing
  • Genome-wide association studies
  • Phenome-wide association studies
  • Gene expression
  • Omics

Published Papers (3 papers)

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Research

13 pages, 23277 KiB  
Article
A Network-Based Analysis of Disease Complication Associations for Obstetric Disorders in the UK Biobank
by Vivek Sriram, Yonghyun Nam, Manu Shivakumar, Anurag Verma, Sang-Hyuk Jung, Seung Mi Lee and Dokyoon Kim
J. Pers. Med. 2021, 11(12), 1382; https://doi.org/10.3390/jpm11121382 - 17 Dec 2021
Cited by 3 | Viewed by 2669
Abstract
Background: Recent studies have found that women with obstetric disorders are at increased risk for a variety of long-term complications. However, the underlying pathophysiology of these connections remains undetermined. A network-based view incorporating knowledge of other diseases and genetic associations will aid our [...] Read more.
Background: Recent studies have found that women with obstetric disorders are at increased risk for a variety of long-term complications. However, the underlying pathophysiology of these connections remains undetermined. A network-based view incorporating knowledge of other diseases and genetic associations will aid our understanding of the role of genetics in pregnancy-related disease complications. Methods: We built a disease–disease network (DDN) using UK Biobank (UKBB) summary data from a phenome-wide association study (PheWAS) to elaborate multiple disease associations. We also constructed egocentric DDNs, where each network focuses on a pregnancy-related disorder and its neighboring diseases. We then applied graph-based semi-supervised learning (GSSL) to translate the connections in the egocentric DDNs to pathologic knowledge. Results: A total of 26 egocentric DDNs were constructed for each pregnancy-related phenotype in the UKBB. Applying GSSL to each DDN, we obtained complication risk scores for additional phenotypes given the pregnancy-related disease of interest. Predictions were validated using co-occurrences derived from UKBB electronic health records. Our proposed method achieved an increase in average area under the receiver operating characteristic curve (AUC) by a factor of 1.35 from 55.0% to 74.4% compared to the use of the full DDN. Conclusion: Egocentric DDNs hold promise as a clinical tool for the network-based identification of potential disease complications for a variety of phenotypes. Full article
(This article belongs to the Special Issue Data Resources and Approaches for Precision Medicine Research: New Developments and Applications)
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16 pages, 1536 KiB  
Article
Use of Drug Claims Data and a Medication Risk Score to Assess the Impact of CYP2D6 Drug Interactions among Opioid Users on Healthcare Costs
by Veronique Michaud, Ravil Bikmetov, Matt K. Smith, Pamela Dow, Lucy I. Darakjian, Malavika Deodhar, Brian Cicali, Kevin T. Bain and Jacques Turgeon
J. Pers. Med. 2021, 11(11), 1174; https://doi.org/10.3390/jpm11111174 - 10 Nov 2021
Cited by 4 | Viewed by 2658
Abstract
Cytochrome P450 2D6 (CYP2D6) activity is highly variable due to several factors, including genetic polymorphisms and drug-drug-gene interactions. Hydrocodone, oxycodone, codeine, and tramadol the most commonly prescribed CYP2D6-activated opioids for pain. However, the co-administration of CYP2D6 interacting drugs can modulate CYP2D6-medicated activation of [...] Read more.
Cytochrome P450 2D6 (CYP2D6) activity is highly variable due to several factors, including genetic polymorphisms and drug-drug-gene interactions. Hydrocodone, oxycodone, codeine, and tramadol the most commonly prescribed CYP2D6-activated opioids for pain. However, the co-administration of CYP2D6 interacting drugs can modulate CYP2D6-medicated activation of these opioids, affecting drug analgesia, effectiveness, and safety, and can impact healthcare costs. A retrospective, observational cohort analysis was performed in a large (n = 50,843) adult population. This study used drug claims data to derive medication risk scores and matching propensity scores to estimate the effects of opioid use and drug-drug interactions (DDIs) on medical expenditures. 4088 individuals were identified as opioid users; 95% of those were prescribed CYP2D6-activated opioids. Among those, 15% were identified as being at risk for DDIs. Opioid users had a significant increase in yearly medical expenditure compared to non-opioid users ($2457 vs. $1210). In matched individuals, average healthcare expenditures were higher for opioid users with DDIs compared to those without DDIs ($7841 vs. $5625). The derived medication risk score was higher in CYP2D6 opioid users with interacting drug(s) compared to no DDI (15 vs. 12). Higher costs associated with CYP2D6 opioid use under DDI conditions suggest inadequate CYP2D6 opioid prescribing practices. Efforts to improve chronic opioid use in adults should reduce interacting drug combinations, especially among patients using CYP2D6 activated opioids. Full article
(This article belongs to the Special Issue Data Resources and Approaches for Precision Medicine Research: New Developments and Applications)
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12 pages, 1661 KiB  
Article
Genome-Wide Polygenic Risk Score for Predicting High Risk Glaucoma Individuals of Han Chinese Ancestry
by Yu-Jer Hsiao, Hao-Kai Chuang, Sheng-Chu Chi, Yung-Yu Wang, Pin-Hsuan Chiang, Pai-Chi Teng, Tung-Mei Kuang, Aliaksandr A. Yarmishyn, Tai-Chi Lin, De-Kuang Hwang, Shih-Jen Chen, Shih-Hwa Chiou, Mei-Ju Chen, Ai-Ru Hsieh and Chih-Chien Hsu
J. Pers. Med. 2021, 11(11), 1169; https://doi.org/10.3390/jpm11111169 - 9 Nov 2021
Cited by 4 | Viewed by 2457
Abstract
Glaucoma is a progressive and irreversible blindness-causing disease. However, the underlying genetic factors and molecular mechanisms remain poorly understood. Previous genome-wide association studies (GWAS) have made tremendous progress on the SNP-based disease association and characterization. However, most of them were conducted for Europeans. [...] Read more.
Glaucoma is a progressive and irreversible blindness-causing disease. However, the underlying genetic factors and molecular mechanisms remain poorly understood. Previous genome-wide association studies (GWAS) have made tremendous progress on the SNP-based disease association and characterization. However, most of them were conducted for Europeans. Since differential genetic characteristics among ethnic groups were evident in glaucoma, it is worthwhile to complete its genetic landscape from the larger cohorts of Asian individuals. Here, we present a GWAS based on the Taiwan Biobank. Among 1013 glaucoma patients and 36,562 controls, we identified a total of 138 independent glaucoma-associated SNPs at the significance level of p < 1 × 10−5. After clumping genetically linked SNPs (LD clumping), 134 independent SNPs with p < 10−4 were recruited to construct a Polygenic Risk Score (PRS). The model achieved an area under the receiver operating characteristic curve (AUC) of 0.8387 (95% CI = [0.8269–0.8506]), and those within the top PRS quantile had a 45.48-fold increased risk of glaucoma compared with those within the lowest quantile. The PRS model was validated with an independent cohort that achieved an AUC of 0.7283, thereby showing the effectiveness of our polygenic risk score in predicting individuals in the Han Chinese population with higher glaucoma risks. Full article
(This article belongs to the Special Issue Data Resources and Approaches for Precision Medicine Research: New Developments and Applications)
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