| Title |
A Simple and Computationally Efficient Approach to Multifactor Dimensionality Reduction Analysis of Gene-Gene Interactions for Quantitative Traits
|
|---|---|
| Published in |
PLOS ONE, June 2013
|
| DOI | 10.1371/journal.pone.0066545 |
| Pubmed ID | |
| Authors |
Jiang Gui, Jason H. Moore, Scott M. Williams, Peter Andrews, Hans L. Hillege, Pim van der Harst, Gerjan Navis, Wiek H. Van Gilst, Folkert W. Asselbergs, Diane Gilbert-Diamond |
| Abstract |
We present an extension of the two-class multifactor dimensionality reduction (MDR) algorithm that enables detection and characterization of epistatic SNP-SNP interactions in the context of a quantitative trait. The proposed Quantitative MDR (QMDR) method handles continuous data by modifying MDR's constructive induction algorithm to use a T-test. QMDR replaces the balanced accuracy metric with a T-test statistic as the score to determine the best interaction model. We used a simulation to identify the empirical distribution of QMDR's testing score. We then applied QMDR to genetic data from the ongoing prospective Prevention of Renal and Vascular End-Stage Disease (PREVEND) study. |
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Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 3 | 43% |
| Canada | 1 | 14% |
| Unknown | 3 | 43% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 4 | 57% |
| Scientists | 3 | 43% |
Mendeley readers
The data shown below were compiled from readership statistics for 52 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 52 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 15 | 29% |
| Researcher | 11 | 21% |
| Student > Master | 7 | 13% |
| Student > Postgraduate | 4 | 8% |
| Professor | 4 | 8% |
| Other | 6 | 12% |
| Unknown | 5 | 10% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 14 | 27% |
| Medicine and Dentistry | 11 | 21% |
| Biochemistry, Genetics and Molecular Biology | 10 | 19% |
| Computer Science | 4 | 8% |
| Psychology | 2 | 4% |
| Other | 5 | 10% |
| Unknown | 6 | 12% |