Novel Integration of Hospital Electronic Medical Records and Gene Expression Measurements to Identify Genetic Markers of Maturation

Chen, David P.; Weber, Susan C.; Constantinou, Philip S.; Ferris, Todd; Lowe, Henry J.; Butte, Atul J.

doi:10.1142/9789812776136_0025

Cited by 13 publications

(11 citation statements)

References 13 publications

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“…In fact, recent gene expression studies have used GenAge to focus on ageing-associated genes (Chen et al , 2008; Hardman & Ashcroft, 2008). Because researchers may have disparate opinions regarding the relevance of different model systems to understand human ageing, an important tool to investigate the human dataset as a whole is GenAge’s browser (http://genomics.senescence.info/genes/browser.php).…”

Section: Genage: the Ageing Gene Databasementioning

confidence: 99%

The Human Ageing Genomic Resources: online databases and tools for biogerontologists

et al. 2009

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SummaryAging is a complex, challenging phenomenon that requires multiple, interdisciplinary approaches to unravel its puzzles. To assist basic research on aging, we developed the Human Ageing Genomic Resources (HAGR). This work provides an overview of the databases and tools in HAGR and describes how the gerontology research community can employ them. Several recent changes and improvements to HAGR are also presented. The two centrepieces in HAGR are GenAge and AnAge. GenAge is a gene database featuring genes associated with aging and longevity in model organisms, a curated database of genes potentially associated with human aging, and a list of genes tested for their association with human longevity. A myriad of biological data and information is included for hundreds of genes, making GenAge a reference for research that reflects our current understanding of the genetic basis of aging. GenAge can also serve as a platform for the systems biology of aging, and tools for the visualization of protein-protein interactions are also included. AnAge is a database of aging in animals, featuring over 4000 species, primarily assembled as a resource for comparative and evolutionary studies of aging. Longevity records, developmental and reproductive traits, taxonomic information, basic metabolic characteristics, and key observations related to aging are included in AnAge. Software is also available to aid researchers in the form of Perl modules to automate numerous tasks and as an SPSS script to analyse demographic mortality data. The HAGR are available online at http://genomics.senescence.info.

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Section: Genage: the Ageing Gene Databasementioning

confidence: 99%

The Human Ageing Genomic Resources: online databases and tools for biogerontologists

et al. 2009

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“…The ability to computationally predict chronological age can help shed light on these differences so that we can have a better understanding of diseases that may modulate aging. For example, one can examine how disease affects the aging process by looking for perturbations in the rate of aging among patient groups stratified by ICD-9-CM codes [39]. While translational bioinformatics most often focuses on taking knowledge gained from molecular data into the clinical setting, reverse translational bioinformatics enables the use of clinical data to shed light on physiological processes.…”

Section: Discussionmentioning

confidence: 99%

Validating pathophysiological models of aging using clinical electronic medical records

Chen

Morgan

Butte

2010

Journal of Biomedical Informatics

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Bioinformatics methods that leverage the vast amounts of clinical data promises to provide insights into underlying molecular mechanisms that help explain human physiological processes. One of these processes is adolescent development. The utility of predictive aging models generated from cross-sectional cohorts and their applicability to separate populations, including the clinical population, has yet to be completely explored. In order to address this, we built regression models predictive of adolescent chronological age from 2001–2002 National Health and Nutrition Examination Survey (NHANES) data and validated them against independent 2003–2004 NHANES data and clinical data from an academic tertiary-care pediatric hospital. The results indicate distinct differences between male and female models with both alkaline phosphatase and creatinine as predictive biomarkers for all genders, hematocrit and mean cell volume for males, and total serum globulin for females. We also suggest that the models are generalizable, are clinically relevant, and imply underlying molecular and clinical differences between males and females that may affect prediction accuracy. The integration of both epidemiological and clinical data promises to create more robust models that shed new light on physiological processes.

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“…Rzhetsky et al used billing codes from the EHRs of 1.5 million patients to analyze disease co-occurrence in 161 conditions as a proxy for possible genetic overlap [68]. Chen et al compared laboratory measurements and age with gene expression data to identify rates of change that correlated with genes known to be involved in aging [69]. A study at Geisinger Clinic evaluated SNPs in the 9p21 region that are known to be associated to cardiovascular disease and early myocardial infarction [70].…”

Section: Examples Of Genetic Discovery Using Ehrsmentioning

confidence: 99%

Chapter 13: Mining Electronic Health Records in the Genomics Era

2012

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The combination of improved genomic analysis methods, decreasing genotyping costs, and increasing computing resources has led to an explosion of clinical genomic knowledge in the last decade. Similarly, healthcare systems are increasingly adopting robust electronic health record (EHR) systems that not only can improve health care, but also contain a vast repository of disease and treatment data that could be mined for genomic research. Indeed, institutions are creating EHR-linked DNA biobanks to enable genomic and pharmacogenomic research, using EHR data for phenotypic information. However, EHRs are designed primarily for clinical care, not research, so reuse of clinical EHR data for research purposes can be challenging. Difficulties in use of EHR data include: data availability, missing data, incorrect data, and vast quantities of unstructured narrative text data. Structured information includes billing codes, most laboratory reports, and other variables such as physiologic measurements and demographic information. Significant information, however, remains locked within EHR narrative text documents, including clinical notes and certain categories of test results, such as pathology and radiology reports. For relatively rare observations, combinations of simple free-text searches and billing codes may prove adequate when followed by manual chart review. However, to extract the large cohorts necessary for genome-wide association studies, natural language processing methods to process narrative text data may be needed. Combinations of structured and unstructured textual data can be mined to generate high-validity collections of cases and controls for a given condition. Once high-quality cases and controls are identified, EHR-derived cases can be used for genomic discovery and validation. Since EHR data includes a broad sampling of clinically-relevant phenotypic information, it may enable multiple genomic investigations upon a single set of genotyped individuals. This chapter reviews several examples of phenotype extraction and their application to genetic research, demonstrating a viable future for genomic discovery using EHR-linked data.

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Novel Integration of Hospital Electronic Medical Records and Gene Expression Measurements to Identify Genetic Markers of Maturation

Cited by 13 publications

References 13 publications

The Human Ageing Genomic Resources: online databases and tools for biogerontologists

The Human Ageing Genomic Resources: online databases and tools for biogerontologists

Validating pathophysiological models of aging using clinical electronic medical records

Chapter 13: Mining Electronic Health Records in the Genomics Era

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