Typical of current large-scale biomedical data is the feature of small number of observed samples and the widely observed sample heterogeneity. Identifying differentially expressed genes related to the sample phenotye (e.g., cancer disease development) and predicting sample phenotype based on the gene expressions are some central research questions in the microarray data analysis. Most existing statistical methods have ignored sample heterogeneity and thus loss power. This project proposes to develop novel statistical methods that explicitly address the small sample size and sampe heterogeneity issues, and can be applied very generally. The usefulness of these methods will be shown with the large-scale biomedical data originating from the lung and kidney transplant research projects. The transplant projects aimed to improve the molecular diagnosis and therapy of lung/kidney allograft rejection by identifying molecular biomarkers to predict the allograft rejection for critical early treatment and rapid, noninvasive, and economical testing.
The specific aims are 1) Develop novel statistical methods for differential gene expression detection that explicitly model sample heterogeneity. 2) Develop novel statistical methods for classifying high-dimensional biomedical data and incorporating sample heterogeneity. 3) Develop novel statistical methods for jointly analyzing a set of genes (e.g., genes in a pathway). 4) Use the developed models and methods to answer research questions relevant to public health in the lung and kidney transplant projects;and implement and validate the proposed methods in user-friendly and well-documented software, and distribute them to the scientific community at no charge. It is very important to identify new biomarkers of allograft rejection in lung and kidney transplant recipients. The rapid and reliable detection and prediction of rejection in easily obtainable body fluids may allow the rapid advancement of clinical interventional trials. We propose to study novel methods for analyzing the large-scale biomedical data to realize their full potential of molecular diagnosis and prognosis of transplant rejection prediction for critical early treatment.

Agency
National Institute of Health (NIH)
Institute
National Institute of General Medical Sciences (NIGMS)
Type
Research Project (R01)
Project #
5R01GM083345-02
Application #
7666186
Study Section
Biodata Management and Analysis Study Section (BDMA)
Program Officer
Lyster, Peter
Project Start
2008-08-01
Project End
2012-05-31
Budget Start
2009-06-01
Budget End
2010-05-31
Support Year
2
Fiscal Year
2009
Total Cost
$256,073
Indirect Cost
Name
University of Minnesota Twin Cities
Department
Biostatistics & Other Math Sci
Type
Schools of Public Health
DUNS #
555917996
City
Minneapolis
State
MN
Country
United States
Zip Code
55455
Guo, Bin; Wu, Baolin (2018) Statistical methods to detect novel genetic variants using publicly available GWAS summary data. Comput Biol Chem 74:76-79
Guo, Bin; Wu, Baolin (2018) Integrate multiple traits to detect novel trait-gene association using GWAS summary data with an adaptive test approach. Bioinformatics :
Guo, Bin; Wu, Baolin (2018) Powerful and efficient SNP-set association tests across multiple phenotypes using GWAS summary data. Bioinformatics :
Guo, Bin; Wu, Baolin (2018) Reader reaction on the fast small-sample kernel independence test for microbiome community-level association analysis. Biometrics 74:1120-1124
Wu, Baolin; Pankow, James S (2018) Fast and Accurate Genome-Wide Association Test of Multiple Quantitative Traits. Comput Math Methods Med 2018:2564531
Wu, Baolin; Pankow, James S (2017) Genome-wide association test of multiple continuous traits using imputed SNPs. Stat Interface 10:379-386
Wu, Baolin; Guan, Weihua; Pankow, James S (2016) On Efficient and Accurate Calculation of Significance P-Values for Sequence Kernel Association Testing of Variant Set. Ann Hum Genet 80:123-35
Wu, Baolin; Pankow, James S (2016) On Sample Size and Power Calculation for Variant Set-Based Association Tests. Ann Hum Genet 80:136-43
Wu, Baolin; Pankow, James S (2016) Sequence Kernel Association Test of Multiple Continuous Phenotypes. Genet Epidemiol 40:91-100
Wu, Baolin; Guan, Weihua (2015) Reader reaction on the generalized Kruskal-Wallis test for genetic association studies incorporating group uncertainty. Biometrics 71:556-7

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