Although family studies were the basis for genetic risk prediction before the advent of modern molecular markers, they have been much less developed for risk prediction of complex diseases using high-dimensional data. Family studies offer many ideal features for large-scale risk prediction research. It provides robust protection against confounding bias when dealing with samples from multiple ethnic groups (i.e., population stratification). Aside from that, Family studies could take into account family information (i.e., genotype and phenotype information from family members) for improved risk prediction. Despite these advantages, they have been used infrequently in recent risk prediction research. The goals of this application are to develop a statistical genetic approach for high-dimensional family-based risk prediction, and to build a family-based risk prediction model by applying the proposed approach to the International Consortium of Orofacial Clefts genome-wide association study dataset. The central hypothesis is that the proposed approach, which considers a large number of genetic and environmental predictors, family information and population substructure, will outperform an existing generalized estimating equations based genotype scoring approach (GEE-GS), and will lead to a robust and accurate family-based risk prediction model for orofacial clefts. The proposed research will be initiated by an early-stage new investigator, who has assembled a research team of senior scientists, including Robert C. Elston, Jeffrey C. Murray and Brian Schutte. The team has developed novel statistical genetic approaches for risk prediction research, and has been active in orofacial clefts genetic and clinical research. In the proposed research project, the research team will turn its attention to family-based orofacial clefts risk prediction. The planned specific aims are to: 1) Develop a robust clustered likelihood ratio approach for high-dimensional family-based risk prediction and compare its performance with the GEE-GS approach through extensive simulation studies;and 2) Build a high-dimensional orofacial clefts risk prediction model by simultaneously considering a large number of genetic and environmental predictors, their interactions, and family information. If successful, the new approach will facilitate high-dimensional family- based risk prediction studies in general. The orofacial clefts risk prediction study will also lead to a novel risk prediction model that can be further replicated and evaluated through application to independent populations.

Public Health Relevance

Risk prediction capitalizing on emerging genetic findings, environmental risk factors and family information holds great promise for improved healthcare and personalized medicine. The proposed research by a new early-stage investigator will develop a quantitative method for high-dimensional family-based risk prediction, and will then use it to form a novel orofacial clefts risk prediction model. The success of the project will advance high-dimensional family-based risk prediction research in general and benefit translational research aimed at developing more effective and affordable prediction and prevention strategies for orofacial clefts.

Agency
National Institute of Health (NIH)
Institute
National Institute of Dental & Craniofacial Research (NIDCR)
Type
Small Research Grants (R03)
Project #
1R03DE022379-01
Application #
8227059
Study Section
Special Emphasis Panel (ZDE1-MH (20))
Program Officer
Harris, Emily L
Project Start
2012-05-01
Project End
2014-04-30
Budget Start
2012-05-01
Budget End
2013-04-30
Support Year
1
Fiscal Year
2012
Total Cost
$224,857
Indirect Cost
$74,857
Name
Michigan State University
Department
Public Health & Prev Medicine
Type
Schools of Medicine
DUNS #
193247145
City
East Lansing
State
MI
Country
United States
Zip Code
48824
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