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Applying family analyses to electronic health records to facilitate genetic research.
Huang, Xiayuan; Elston, Robert C; Rosa, Guilherme J; Mayer, John; Ye, Zhan; Kitchner, Terrie; Brilliant, Murray H; Page, David; Hebbring, Scott J.
Afiliación
  • Huang X; Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI 53792, USA.
  • Elston RC; Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH 44106, USA.
  • Rosa GJ; Department of Animal Science, University of Wisconsin-Madison, Madison, WI 53706, USA.
  • Mayer J; Biomedical Informatics Research Center.
  • Ye Z; Biomedical Informatics Research Center.
  • Kitchner T; Center for Human Genetics, Marshfield Clinic Research Institute, Marshfield, WI 54449, USA.
  • Brilliant MH; Center for Human Genetics, Marshfield Clinic Research Institute, Marshfield, WI 54449, USA.
  • Page D; Department of Medical Genetics.
  • Hebbring SJ; Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI 53792, USA.
Bioinformatics ; 34(4): 635-642, 2018 02 15.
Article en En | MEDLINE | ID: mdl-28968884
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.
Asunto(s)

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Contexto en salud: 1_ASSA2030 Problema de salud: 1_sistemas_informacao_saude Asunto principal: Linaje / Genoma Humano / Investigación Genética / Registros Electrónicos de Salud Tipo de estudio: Prognostic_studies Límite: Female / Humans / Male / Middle aged Idioma: En Revista: Bioinformatics Asunto de la revista: INFORMATICA MEDICA Año: 2018 Tipo del documento: Article País de afiliación: Estados Unidos

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Contexto en salud: 1_ASSA2030 Problema de salud: 1_sistemas_informacao_saude Asunto principal: Linaje / Genoma Humano / Investigación Genética / Registros Electrónicos de Salud Tipo de estudio: Prognostic_studies Límite: Female / Humans / Male / Middle aged Idioma: En Revista: Bioinformatics Asunto de la revista: INFORMATICA MEDICA Año: 2018 Tipo del documento: Article País de afiliación: Estados Unidos
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