Incorporation of Personal Single Nucleotide Polymorphism (SNP) Data into a National Level Electronic Health Record for Disease Risk Assessment, Part 1: An Overview of Requirements

Background: Personalized medicine approaches provide opportunities for predictive and preventive medicine. Using genomic, clinical, environmental, and behavioral data, tracking and management of individual wellness is possible. A prolific way to carry this personalized approach into routine practices can be accomplished by integrating clinical interpretations of genomic variations into electronic medical records (EMRs)/electronic health records (EHRs). Today, various central EHR infrastructures have been constituted in many countries of the world including Turkey. Objective: The objective of this study was to concentrate on incorporating the personal single nucleotide polymorphism (SNP) data into the National Health Information System of Turkey (NHIS-T) for disease risk assessment, and evaluate the performance of various predictive models for prostate cancer cases. We present our work as a miniseries containing three parts: (1) an overview of requirements, (2) the incorporation of SNP into the NHIS-T, and (3) an evaluation of SNP incorporated NHIS-T for prostate cancer. Methods: For the first article of this miniseries, the scientific literature is reviewed and the requirements of SNP data integration into EMRs/EHRs are extracted and presented. Results: In the literature, basic requirements of genomic-enabled EMRs/EHRs are listed as incorporating genotype data and its clinical interpretation into EMRs/EHRs, developing accurate and accessible clinicogenomic interpretation resources (knowledge bases), interpreting and reinterpreting of variant data, and immersing of clinicogenomic information into the medical decision processes. In this section, we have analyzed these requirements under the subtitles of terminology standards, interoperability standards, clinicogenomic knowledge bases, defining clinical significance, and clinicogenomic decision support. Conclusions: In order to integrate structured genotype and phenotype data into any system, there is a need to determine data components, terminology standards, and identifiers of clinicogenomic information. Also, we need to determine interoperability standards to share information between different information systems of stakeholders, and develop decision support capability to interpret genomic variations based on the knowledge bases via different assessment approaches.Publisher's Versio

Farklı nitelikteki biyolojik ağların entegrasyonu ve yerel topolojik özellik vektörleri tabanlı karşılaştırılması

TÜBİTAK EEEAG Proje01.12.2016In this project, we developed a framework for the analysis of integrated genome-scale networks using using directed graphlet signatures. In addition, we developed a novel graph layout algorithm specific for visualizing aligned networks. Analysis of integrated genome-scale networks is a challenging problem due to heterogeneity of high-throughput data. There are several topological measures, such as graphlet counts, for characterization of biological networks. In this project, we present methods for counting small sub-graph patterns in integrated genome-scale networks which are modeled as labeled multidigraphs. We have obtained physical, regulatory, and metabolic interactions between H. sapiens proteins from the Pathway Commons database. The integrated network is filtered for tissue/disease specific proteins by using a large-scale human transcriptional profiling study, resulting in several tissue and disease specific sub-networks. We have applied and extended the idea of graphlet counting in undirected protein-protein interaction (PPI) networks to directed multi-labeled networks and represented each network as a vector of graphlet counts. Graphlet counts are assessed for statistical significance by comparison against a set of randomized networks. We present our results on analysis of differential graphlets between different conditions and on the utility of graphlet count vectors for clustering multiple condition specific networks. Our results show that there are numerous statistically significant graphlets in integrated biological networks and the graphlet signature vector can be used as an effective representation of a multi-labeled network for clustering and systems level analysis of tissue/disease specific networks. In addition, the proposed graph layout algorithm can be used to visualize the similarities and differences between aligned regions of these network

Health-Related Work Loss: Wellness Profiles of Information Technology Employees

Understanding health-related work loss and creating a comprehensive approach requires the identification of lifestyle behavior patterns. An essential part of this process is the examination of different profiles within the target population to develop effective intervention strategies. This study explored the wellness profiles of information technology (IT) employees regarding lifestyle behaviors and health-related work loss. The cross-sectional study surveyed 405 employees (174 women and 231 men) in six cities in Türkiye to examine lifestyle behaviors (exercise, nutrition, stress management, health responsibility, mental development, and interpersonal relations) and health-related work loss (presenteeism and absenteeism). Data analysis was conducted using independent samples t-test, ANOVA, multiple linear regression, and two-step cluster analysis. Regression findings indicated that physical activity, nutrition, and stress management behaviors statistically predict work performance in IT employees (

The Oak Ridge Polycystic Kidney mouse: Modeling ciliopathies of mice and men

The Oak Ridge Polycystic Kidney (ORPK) mouse was described nearly 14 years ago as a model for human recessive polycystic kidney disease. The ORPK mouse arose through integration of a transgene into an intron of the Ift88 gene resulting in a hypomorphic allele (Ift88(Tg737Rpw)). The Ift88(Tg737Rp omega) mutation impairs intraflagellar transport (IFT), a process required for assembly of motile and immotile cilia. Historically, the primary immotile cilium was thought to have minimal importance for human health; however, a rapidly expanding number of human disorders have now been attributed to ciliary defects. Importantly, many of these phenotypes are present and can be analyzed using the ORPK mouse. In this review, we highlight the research conducted using the OPRK mouse and the phenotypes shared with human cilia disorders. Furthermore, we describe an additional follicular dysplasia phenotype in the ORPK mouse, which alongside the ectodermal dysplasias seen in human Ellis-van Creveld and Sensenbrenner's syndromes, suggests an unappreciated role for primary cilia in the skin and hair follicle

Modeling the combined effect of RNA-binding proteins and microRNAs in post-transcriptional regulation

Recent studies show that RNA-binding proteins (RBPs) and microRNAs (miRNAs) function in coordination with each other to control post-transcriptional regulation (PTR). Despite this, the majority of research to date has focused on the regulatory effect of individual RBPs or miRNAs. Here, we mapped both RBP and miRNA binding sites on human 3′UTRs and utilized this collection to better understand PTR. We show that the transcripts that lack competition for HuR binding are destabilized more after HuR depletion. We also confirm this finding for PUM1(2) by measuring genome-wide expression changes following the knockdown of PUM1(2) in HEK293 cells. Next, to find potential cooperative interactions, we identified the pairs of factors whose sites co-localize more often than expected by random chance. Upon examining these results for PUM1(2), we found that transcripts where the sites of PUM1(2) and its interacting miRNA form a stem-loop are more stabilized upon PUM1(2) depletion. Finally, using dinucleotide frequency and counts of regulatory sites as features in a regression model, we achieved an AU-ROC of 0.86 in predicting mRNA half-life in BEAS-2B cells. Altogether, our results suggest that future studies of PTR must consider the combined effects of RBPs and miRNAs, as well as their interactions.No sponso

Incorporation of personal Single Nucleotide Polymorphism (SNP) data into a national level electronic health record for disease risk assessment, part 2: The incorporation of SNP into the national health information system of Turkey

©Timur Beyan, Yeşim Aydin Son.Background: A personalized medicine approach provides opportunities for predictive and preventive medicine. Using genomic, clinical, environmental, and behavioral data, the tracking and management of individual wellness is possible. A prolific way to carry this personalized approach into routine practices can be accomplished by integrating clinical interpretations of genomic variations into electronic medical record (EMR)s/electronic health record (EHR)s systems. Today, various central EHR infrastructures have been constituted in many countries of the world, including Turkey. Objective: As an initial attempt to develop a sophisticated infrastructure, we have concentrated on incorporating the personal single nucleotide polymorphism (SNP) data into the National Health Information System of Turkey (NHIS-T) for disease risk assessment, and evaluated the performance of various predictive models for prostate cancer cases. We present our work as a miniseries containing three parts: (1) an overview of requirements, (2) the incorporation of SNP into the NHIS-T, and (3) an evaluation of SNP data incorporated into the NHIS-T for prostate cancer. Methods: For the second article of this miniseries, we have analyzed the existing NHIS-T and proposed the possible extensional architectures. In light of the literature survey and characteristics of NHIS-T, we have proposed and argued opportunities and obstacles for a SNP incorporated NHIS-T. A prototype with complementary capabilities (knowledge base and end-user applications) for these architectures has been designed and developed. Results: In the proposed architectures, the clinically relevant personal SNP (CR-SNP) and clinicogenomic associations are shared between central repositories and end-users via the NHIS-T infrastructure. To produce these files, we need to develop a national level clinicogenomic knowledge base. Regarding clinicogenomic decision support, we planned to complete interpretation of these associations on the end-user applications. This approach gives us the flexibility to add/update envirobehavioral parameters and family health history that will be monitored or collected by end users. Conclusions: Our results emphasized that even though the existing NHIS-T messaging infrastructure supports the integration of SNP data and clinicogenomic association, it is critical to develop a national level, accredited knowledge base and better end-user systems for the interpretation of genomic, clinical, and envirobehavioral parameters

METU-SNP: An Integrated Software System for SNPComplex Disease Association Analysis

Recently, there has been increasing research to discover genomic biomarkers, haplotypes, and potentially other variables that together contribute to the development of diseases. Single Nucleotide Polymorphisms (SNPs) are the most common form of genomic variations and they can represent an individual's genetic variability in greatest detail. Genome-wide association studies (GWAS) of SNPs, high-dimensional case-control studies, are among the most promising approaches for identifying disease causing variants. METU-SNP software is a Java based integrated desktop application specifically designed for the prioritization of SNP biomarkers and the discovery of genes and pathways related to diseases via analysis of the GWAS case-control data. Outputs of METU-SNP can easily be utilized for the downstream biomarkers research to allow the prediction and the diagnosis of diseases and other personalized medical approaches. Here, we introduce and describe the system functionality and architecture of the METU-SNP. We believe that the METU-SNP will help researchers with the reliable identification of SNPs that are involved in the etiology of complex diseases, ultimately supporting the development of personalized medicine approaches and targeted drug discoveries.Publisher's Versio