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Principal Investigator  
Principal Investigator's Name: Ye Li
Institution: University of Kentucky
Department: Statistics
Country:
Proposed Analysis: Multifactor dimensionality reduction (MDR) was developed by Ritchie et al in 2001 to identify gene-gene or gene-environment interactions for large dimensional data; different combinations of variable values are labeled as high or low risk to characterize each subject in terms of an optimal gene-gene or gene-environment interaction. Extensions of MDR have been pursued, such as quantitative MDR (Gui et al 2013), aggregated MDR (Dai et al 2013), and aggregated quantitative MDR (Crouch 2016). Our work—“MDR with P Risk Scores” considers a situation where multiple interactions of a particular order (e.g., two way) may be considered simultaneously but not necessarily collapsed into a single risk score as in aggregated or aggregated quantitative MDR. Rather, we obtain P (>1) risk scores and use them to predict the continuous outcome for each subject. In Crouch 2016, AQMDR and QMDR were used to perform exhaustive searches for significant two-way and three-way gene-gene interactions, with restrictions on interactions to those involving APOE4, which demonstrated associations with counts of tau and Abeta, found in cerebrospinal fluid, respectively. We will use the two-way and three-way significant interactions found in Crouch 2016 to construct P aggregated risk scores for each patient in two ways. One is to search for a set of P risk scores for each subject to predict the outcome when P is specified a priori, such as 2 or 3. The other is, without an a priori specification of P, search for the set of P risk scores to use in order to minimize the mean square error of prediction. Acknowledgment: some text and/or ideas for this project are derived from text and/or ideas developed by Rebeca Crouch and Richard Charnigo for another project.
Additional Investigators  
Investigator's Name: Richard Charnigo
Proposed Analysis: Dr.Richard Charnigo will be responsible as Ye Li's faculty advisor and collaborator in this project. The proposed analysis and key words are the same as Ye Li's.