Name
Title | ||
|---|---|---|
Applying family analyses to electronic health records to facilitate genetic research. |
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. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1093/bioinformatics/btx569 | BIOINFORMATICS |
Field | DocType | Volume |
Data mining,Computer science,Bioinformatics | Journal | 34 |
Issue | ISSN | Citations |
4 | 1367-4803 | 0 |
PageRank | References | Authors |
0.34 | 2 | 9 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiayuan Huang | 1 | 0 | 0.34 |
| Robert C. Elston | 2 | 89 | 8.57 |
| Guilherme J. Rosa | 3 | 0 | 0.34 |
| John Mayer | 4 | 6 | 1.63 |
| Zhan Ye | 5 | 0 | 1.69 |
| terrie kitchner | 6 | 18 | 1.65 |
| Murray Brilliant | 7 | 2 | 1.05 |
| David Page | 8 | 533 | 61.12 |
| Scott J. Hebbring | 9 | 0 | 1.69 |