Mapping of complex traits to gather information pertaining to genetic human disorders is seeing a tremendous and exciting era. Researchers everywhere have surfaced with novel designs, linkage and association methods to aid in the identification of the genes responsible for these diseases. However, success in such research has been largely restricted to simple, rare Mendelian diseases. The broad expanse of common and complex human disorders remains uncharted territory, despite past attempts for deriving information from such realms. In this proposal we offer a possible resolution to this problem. Our long term objective is to develop a novel and applicable method for breaking the barriers set between the limited ground of Mendelian diseases and important findings to common and complex diseases.
Specific aims i nclude: 1) Providing a new and realistically applicable method to address the important problems posed in the mapping of complex traits. The proposed method embodies two-stages: a) selecting a subset of markers that contain important susceptibility information regarding the traits under study and b) carrying out a detailed analysis and network construction on the selected markers, by clustering markers through evaluation of the interactive effects from the markers on the traits of interests. 2) Procuring a method appropriate and powerful for the variety of study designs and data types implemented by the scientific community. We propose to study a limited number of extensions in depth, which would accommodate researchers who conduct studies on human genetic disorders in their own study designs and data forms. 3) Developing user-friendly computational packages and tools based on the algorithm proposed for convenient usage of our methods in current and future genetic mapping of complex diseases. This project amounts to great importance in the convenience and overall accessibility of our proposed algorithm to present and future scientists. The proposed aims intend to remove the barriers current researchers face in approaching non-Mendelian diseases for information, provide routine guidelines for future investigators, and promote the quality of public welfare. ? ? ?

Agency
National Institute of Health (NIH)
Institute
National Institute of General Medical Sciences (NIGMS)
Type
Research Project (R01)
Project #
5R01GM070789-04
Application #
7350130
Study Section
Mammalian Genetics Study Section (MGN)
Program Officer
Anderson, Richard A
Project Start
2005-02-01
Project End
2010-01-31
Budget Start
2008-02-01
Budget End
2009-01-31
Support Year
4
Fiscal Year
2008
Total Cost
$152,630
Indirect Cost
Name
Columbia University (N.Y.)
Department
Biostatistics & Other Math Sci
Type
Other Domestic Higher Education
DUNS #
049179401
City
New York
State
NY
Country
United States
Zip Code
10027
Zhou, Hui; Zheng, Tian (2013) Bayesian hierarchical graph-structured model for pathway analysis using gene expression data. Stat Appl Genet Mol Biol 12:393-412
Wang, Haitian; Lo, Shaw-Hwa; Zheng, Tian et al. (2012) Interaction-based feature selection and classification for high-dimensional biological data. Bioinformatics 28:2834-42
Zheng, Tian; Gastwirth, Joseph L (2010) On Bootstrap Tests of Symmetry About an Unknown Median. J Data Sci 8:413-427
SalicrĂș, Miquel; Vives, Sergi; Zheng, Tian (2009) Inferential clustering approach for microarray experiments with replicated measurements. IEEE/ACM Trans Comput Biol Bioinform 6:594-604
An, Ping; Mukherjee, Odity; Chanda, Pritam et al. (2009) The challenge of detecting epistasis (G x G interactions): Genetic Analysis Workshop 16. Genet Epidemiol 33 Suppl 1:S58-67
Zheng, Tian; Lo, Shaw-Hwa (2008) Comment: Quantifying the Fraction of Missing Information for Hypothesis Testing in Statistical and Genetic Studies. Stat Sci 23:318-320
Iossifov, Ivan; Zheng, Tian; Baron, Miron et al. (2008) Genetic-linkage mapping of complex hereditary disorders to a whole-genome molecular-interaction network. Genome Res 18:1150-62
Lo, Shaw-Hwa; Chernoff, Herman; Cong, Lei et al. (2008) Discovering interactions among BRCA1 and other candidate genes associated with sporadic breast cancer. Proc Natl Acad Sci U S A 105:12387-92
Yan, Xin; Zheng, Tian (2008) Selecting informative genes for discriminant analysis using multigene expression profiles. BMC Genomics 9 Suppl 2:S14
Woo, Jung Hoon; Zheng, Tian; Kim, Ju Han (2007) Identifying Genomic Regulators of Set-Wise Co-Expression. Proc IEEE Int Symp Bioinformatics Bioeng 2007:433-439

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