Cargando…
Extracting research-quality phenotypes from electronic health records to support precision medicine
The convergence of two rapidly developing technologies - high-throughput genotyping and electronic health records (EHRs) - gives scientists an unprecedented opportunity to utilize routine healthcare data to accelerate genomic discovery. Institutions and healthcare systems have been building EHR-link...
Autores principales: | , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2015
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4416392/ https://www.ncbi.nlm.nih.gov/pubmed/25937834 http://dx.doi.org/10.1186/s13073-015-0166-y |
_version_ | 1782369233831723008 |
---|---|
author | Wei, Wei-Qi Denny, Joshua C |
author_facet | Wei, Wei-Qi Denny, Joshua C |
author_sort | Wei, Wei-Qi |
collection | PubMed |
description | The convergence of two rapidly developing technologies - high-throughput genotyping and electronic health records (EHRs) - gives scientists an unprecedented opportunity to utilize routine healthcare data to accelerate genomic discovery. Institutions and healthcare systems have been building EHR-linked DNA biobanks to enable such a vision. However, the precise extraction of detailed disease and drug-response phenotype information hidden in EHRs is not an easy task. EHR-based studies have successfully replicated known associations, made new discoveries for diseases and drug response traits, rapidly contributed cases and controls to large meta-analyses, and demonstrated the potential of EHRs for broad-based phenome-wide association studies. In this review, we summarize the advantages and challenges of repurposing EHR data for genetic research. We also highlight recent notable studies and novel approaches to provide an overview of advanced EHR-based phenotyping. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s13073-015-0166-y) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-4416392 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2015 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-44163922015-05-02 Extracting research-quality phenotypes from electronic health records to support precision medicine Wei, Wei-Qi Denny, Joshua C Genome Med Review The convergence of two rapidly developing technologies - high-throughput genotyping and electronic health records (EHRs) - gives scientists an unprecedented opportunity to utilize routine healthcare data to accelerate genomic discovery. Institutions and healthcare systems have been building EHR-linked DNA biobanks to enable such a vision. However, the precise extraction of detailed disease and drug-response phenotype information hidden in EHRs is not an easy task. EHR-based studies have successfully replicated known associations, made new discoveries for diseases and drug response traits, rapidly contributed cases and controls to large meta-analyses, and demonstrated the potential of EHRs for broad-based phenome-wide association studies. In this review, we summarize the advantages and challenges of repurposing EHR data for genetic research. We also highlight recent notable studies and novel approaches to provide an overview of advanced EHR-based phenotyping. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s13073-015-0166-y) contains supplementary material, which is available to authorized users. BioMed Central 2015-04-30 /pmc/articles/PMC4416392/ /pubmed/25937834 http://dx.doi.org/10.1186/s13073-015-0166-y Text en © Wei and Denny; licensee BioMed Central. 2015 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Review Wei, Wei-Qi Denny, Joshua C Extracting research-quality phenotypes from electronic health records to support precision medicine |
title | Extracting research-quality phenotypes from electronic health records to support precision medicine |
title_full | Extracting research-quality phenotypes from electronic health records to support precision medicine |
title_fullStr | Extracting research-quality phenotypes from electronic health records to support precision medicine |
title_full_unstemmed | Extracting research-quality phenotypes from electronic health records to support precision medicine |
title_short | Extracting research-quality phenotypes from electronic health records to support precision medicine |
title_sort | extracting research-quality phenotypes from electronic health records to support precision medicine |
topic | Review |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4416392/ https://www.ncbi.nlm.nih.gov/pubmed/25937834 http://dx.doi.org/10.1186/s13073-015-0166-y |
work_keys_str_mv | AT weiweiqi extractingresearchqualityphenotypesfromelectronichealthrecordstosupportprecisionmedicine AT dennyjoshuac extractingresearchqualityphenotypesfromelectronichealthrecordstosupportprecisionmedicine |