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Bioinformatics. 2018 Feb 15;34(4):635-642. doi: 10.1093/bioinformatics/btx569.

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

Author information

1
Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI 53792, USA.
2
Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH 44106, USA.
3
Department of Animal Science, University of Wisconsin-Madison, Madison, WI 53706, USA.
4
Biomedical Informatics Research Center.
5
Center for Human Genetics, Marshfield Clinic Research Institute, Marshfield, WI 54449, USA.
6
Department of Medical Genetics.
7
Department of Computer Sciences, University of Wisconsin-Madison, Madison, WI 53706, USA.

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.

PMID:
28968884
PMCID:
PMC5860602
DOI:
10.1093/bioinformatics/btx569
[Indexed for MEDLINE]
Free PMC Article

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