A Review of the Role of Electronic Health Record in Genomic Research

Krishnamoorthy, Parasuram; Gupta, Divyansh; Chatterjee, Saurav; Huston, Jessica; Ryan, John

doi:10.1007/s12265-014-9586-0

Cited by 19 publications

(23 citation statements)

References 37 publications

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“…The value of EHRs in genomics research is becoming widely recognised (e.g. [39, 40]). The focus to date has largely been on genetic associations with International Classification of Disease (ICD-9 or ICD-10) codes which are available in most EHR systems but successful GWAS of several liver biochemistry measures in 3294 samples from the eMERGE network have recently been described [41].…”

Section: Discussionmentioning

confidence: 99%

Exploration of haplotype research consortium imputation for genome-wide association studies in 20,032 Generation Scotland participants

et al. 2017

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BackgroundThe Generation Scotland: Scottish Family Health Study (GS:SFHS) is a family-based population cohort with DNA, biological samples, socio-demographic, psychological and clinical data from approximately 24,000 adult volunteers across Scotland. Although data collection was cross-sectional, GS:SFHS became a prospective cohort due to of the ability to link to routine Electronic Health Record (EHR) data. Over 20,000 participants were selected for genotyping using a large genome-wide array.MethodsGS:SFHS was analysed using genome-wide association studies (GWAS) to test the effects of a large spectrum of variants, imputed using the Haplotype Research Consortium (HRC) dataset, on medically relevant traits measured directly or obtained from EHRs. The HRC dataset is the largest available haplotype reference panel for imputation of variants in populations of European ancestry and allows investigation of variants with low minor allele frequencies within the entire GS:SFHS genotyped cohort.ResultsGenome-wide associations were run on 20,032 individuals using both genotyped and HRC imputed data. We present results for a range of well-studied quantitative traits obtained from clinic visits and for serum urate measures obtained from data linkage to EHRs collected by the Scottish National Health Service. Results replicated known associations and additionally reveal novel findings, mainly with rare variants, validating the use of the HRC imputation panel. For example, we identified two new associations with fasting glucose at variants near to Y_RNA and WDR4 and four new associations with heart rate at SNPs within CSMD1 and ASPH, upstream of HTR1F and between PROKR2 and GPCPD1. All were driven by rare variants (minor allele frequencies in the range of 0.08–1%). Proof of principle for use of EHRs was verification of the highly significant association of urate levels with the well-established urate transporter SLC2A9.ConclusionsGS:SFHS provides genetic data on over 20,000 participants alongside a range of phenotypes as well as linkage to National Health Service laboratory and clinical records. We have shown that the combination of deeper genotype imputation and extended phenotype availability make GS:SFHS an attractive resource to carry out association studies to gain insight into the genetic architecture of complex traits.Electronic supplementary materialThe online version of this article (doi:10.1186/s13073-017-0414-4) contains supplementary material, which is available to authorized users.

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Section: Discussionmentioning

confidence: 99%

Exploration of haplotype research consortium imputation for genome-wide association studies in 20,032 Generation Scotland participants

et al. 2017

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“…eMERGE was created to evaluate the possibility of using data from routine clinical care retrieved from electronic medical records (EMRs) to characterize disease phenotypes in genome wide association studies (GWAS) [61]. Within this initiative, a variety of methods and best practices were developed for utilizing EHRs as a tool for genomic research [62,63]. Before widely exploiting this data for research, several authors worked to prove the benefits of EHR-derived phenotyping.…”

Section: B Secondary Use Of Patient Data For Genomic Research and Tomentioning

confidence: 99%

Secondary Use and Analysis of Big Data Collected for Patient Care

Martín-Sánchez¹,

Aguiar-Pulido²,

Lopez-Campos³

et al. 2017

Yearb Med Inform

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SummaryObjectives: To identify common methodological challenges and review relevant initiatives related to the re-use of patient data collected in routine clinical care, as well as to analyze the economic benefits derived from the secondary use of this data. Through the use of several examples, this article aims to provide a glimpse into the different areas of application, namely clinical research, genomic research, study of environmental factors, and population and health services research. This paper describes some of the informatics methods and Big Data resources developed in this context, such as electronic phenotyping, clinical research networks, biorepositories, screening data banks, and wide association studies. Lastly, some of the potential limitations of these approaches are discussed, focusing on confounding factors and data quality. Methods: A series of literature searches in main bibliographic databases have been conducted in order to assess the extent to which existing patient data has been repurposed for research. This contribution from the IMIA working group on "Data mining and Big Data analytics" focuses on the literature published during the last two years, covering the timeframe since the working group's last survey. Results and Conclusions: Although most of the examples of secondary use of patient data lie in the arena of clinical and health services research, we have started to witness other important applications, particularly in the area of genomic research and the study of health effects of environmental factors. Further research is needed to characterize the economic impact of secondary use across the broad spectrum of translational research.

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“…However, the precise extraction of detailed disease and therapeutic-response phenotype information contained in EHRs is not an easy task; EHR-driven phenotyping has yielded multiple challenges [129, 130]. GWAS and massively parallel DNA sequencing strategies detect hundreds of thousands of loci per individual, affecting both normal variation and susceptibility to disease [131], and can shed light on the genetic architecture of complex traits.…”

Section: Big Data and Innovative Therapeutic Directionsmentioning

confidence: 99%

Linking Genes to Cardiovascular Diseases: Gene Action and Gene–Environment Interactions

Pasipoularides

2015

J. of Cardiovasc. Trans. Res.

121

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A unique myocardial characteristic is its ability to grow/remodel in order to adapt; this is determined partly by genes and partly by the environment and the milieu intérieur. In the “post-genomic” era, a need is emerging to elucidate the physiologic functions of myocardial genes, as well as potential adaptive and maladaptive modulations induced by environmental/epigenetic factors. Genome sequencing and analysis advances have become exponential lately, with escalation of our knowledge concerning sometimes controversial genetic underpinnings of cardiovascular diseases. Current technologies can identify candidate genes variously involved in diverse normal/abnormal morphomechanical phenotypes, and offer insights into multiple genetic factors implicated in complex cardiovascular syndromes. The expression profiles of thousands of genes are regularly ascertained under diverse conditions. Global analyses of gene expression levels are useful for cataloging genes and correlated phenotypes, and for elucidating the role of genes in maladies. Comparative expression of gene networks coupled to complex disorders can contribute insights as to how “modifier genes” influence the expressed phenotypes. Increasingly, a more comprehensive and detailed systematic understanding of genetic abnormalities underlying, for example, various genetic cardiomyopathies is emerging. Implementing genomic findings in cardiology practice may well lead directly to better diagnosing and therapeutics. There is currently evolving a strong appreciation for the value of studying gene anomalies, and doing so in a non-disjointed, cohesive manner. However, it is challenging for many—practitioners and investigators—to comprehend, interpret, and utilize the clinically increasingly accessible and affordable cardiovascular genomics studies. This survey addresses the need for fundamental understanding in this vital area.

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A Review of the Role of Electronic Health Record in Genomic Research

Cited by 19 publications

References 37 publications

Exploration of haplotype research consortium imputation for genome-wide association studies in 20,032 Generation Scotland participants

Exploration of haplotype research consortium imputation for genome-wide association studies in 20,032 Generation Scotland participants

Secondary Use and Analysis of Big Data Collected for Patient Care

Linking Genes to Cardiovascular Diseases: Gene Action and Gene–Environment Interactions

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