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Applying family analyses to electronic health records to facilitate genetic research.

Publication ,  Journal Article
Huang, X; Elston, RC; Rosa, GJ; Mayer, J; Ye, Z; Kitchner, T; Brilliant, MH; Page, D; Hebbring, SJ
Published in: Bioinformatics
February 15, 2018

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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Published In

Bioinformatics

DOI

EISSN

1367-4811

Publication Date

February 15, 2018

Volume

34

Issue

4

Start / End Page

635 / 642

Location

England

Related Subject Headings

  • Pedigree
  • Middle Aged
  • Male
  • Humans
  • Genome, Human
  • Genetic Research
  • Genetic Diseases, Inborn
  • Genetic Association Studies
  • Female
  • Electronic Health Records
 

Citation

APA
Chicago
ICMJE
MLA
NLM
Huang, X., Elston, R. C., Rosa, G. J., Mayer, J., Ye, Z., Kitchner, T., … Hebbring, S. J. (2018). Applying family analyses to electronic health records to facilitate genetic research. Bioinformatics, 34(4), 635–642. https://doi.org/10.1093/bioinformatics/btx569
Huang, Xiayuan, Robert C. Elston, Guilherme J. Rosa, John Mayer, Zhan Ye, Terrie Kitchner, Murray H. Brilliant, David Page, and Scott J. Hebbring. “Applying family analyses to electronic health records to facilitate genetic research.Bioinformatics 34, no. 4 (February 15, 2018): 635–42. https://doi.org/10.1093/bioinformatics/btx569.
Huang X, Elston RC, Rosa GJ, Mayer J, Ye Z, Kitchner T, et al. Applying family analyses to electronic health records to facilitate genetic research. Bioinformatics. 2018 Feb 15;34(4):635–42.
Huang, Xiayuan, et al. “Applying family analyses to electronic health records to facilitate genetic research.Bioinformatics, vol. 34, no. 4, Feb. 2018, pp. 635–42. Pubmed, doi:10.1093/bioinformatics/btx569.
Huang X, Elston RC, Rosa GJ, Mayer J, Ye Z, Kitchner T, Brilliant MH, Page D, Hebbring SJ. Applying family analyses to electronic health records to facilitate genetic research. Bioinformatics. 2018 Feb 15;34(4):635–642.

Published In

Bioinformatics

DOI

EISSN

1367-4811

Publication Date

February 15, 2018

Volume

34

Issue

4

Start / End Page

635 / 642

Location

England

Related Subject Headings

  • Pedigree
  • Middle Aged
  • Male
  • Humans
  • Genome, Human
  • Genetic Research
  • Genetic Diseases, Inborn
  • Genetic Association Studies
  • Female
  • Electronic Health Records