ArticleBioinformatics (Oxford, England)2018
Applying family analyses to electronic health records to facilitate genetic research.
Article in Bioinformatics (Oxford, England), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
7 citing papers in PubMed.
- Identifying family structures from obituaries and matching them to patients in an electronic heath record.Journal of the American Medical Informatics Association : JAMIA · 2025Article
- Enhancing patient representation learning with inferred family pedigrees improves disease risk prediction.Journal of the American Medical Informatics Association : JAMIA · 2025Article
- E-Pedigrees: a large-scale automatic family pedigree prediction application.Bioinformatics (Oxford, England) · 2021Article
- Contribution of Free-Text Comments to the Burden of Documentation: Assessment and Analysis of Vital Sign Comments in Flowsheets.Journal of medical Internet research · 2021Article
- Family member information extraction via neural sequence labeling models with different tag schemes.BMC medical informatics and decision making · 2019Article
- Estimating variance components in population scale family trees.PLoS genetics · 2019Article
- Genomic and Phenomic Research in the 21st Century.Trends in genetics : TIG · 2019Review
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
Motivation: Pedigree analysis is a longstanding and powerful approach to gain insight into the underlying genetic factors in human health, but identifying, recruiting and genotyping families can be difficult, time consuming and costly. Development of high throughput methods to identify families and foster downstream analyses are necessary. Results: This paper describes simple methods that allowed us to identify 173 368 family pedigrees with high probability using basic demographic data available in most electronic health records (EHRs). We further developed and validate a novel statistical method that uses EHR data to identify families more likely to have a major genetic component to their diseases risk. Lastly, we showed that incorporating EHR-linked family data into genetic association testing may provide added power for genetic mapping without additional recruitment or genotyping. The totality of these results suggests that EHR-linked families can enable classical genetic analyses in a high-throughput manner. Availability and implementation: Pseudocode is provided as supplementary information. Contact: HEBBRING.SCOTT@marshfieldresearch.org. Supplementary information: Supplementary data are available at Bioinformatics online.
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Registered trials
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