The broad, long-term objectives of this research are the development of novel and high-impact statistical methods for medical studies of chronic diseases, with a focus on trans-omics precision medicine research. The speci?c aims of this competing renewal application include: (1) derivation of ef?cient and robust statistics for integrative association analysis of multiple omics platforms (DNA sequences, RNA expressions, methylation pro?les, protein expressions, metabolomics pro?les, etc.) with arbitrary patterns of missing data and with detection limits for quantitative measurements; (2) exploration of statistical learning approaches for handling multiple types of high- dimensional omics variables with structural associations and with substantial missing data; and (3) construction of a multivariate regression model of the effects of somatic mutations on gene expressions in cancer tumors for discovery of subject-speci?c driver mutations, leveraging gene interaction network information and accounting for inter-tumor heterogeneity in mutational effects. All these aims have been motivated by the investigators' applied research experience in trans-omics studies of cancer and cardiovascular diseases. The proposed solutions are based on likelihood and other sound statistical principles. The theoretical properties of the new statistical methods will be rigorously investigated through innovative use of advanced mathematical arguments. Computationally ef?cient and numerically stable algorithms will be developed to implement the inference procedures. The new methods will be evaluated extensively with simulation studies that mimic real data and applied to several ongoing trans-omics precision medicine projects, most of which are carried out at the University of North Carolina at Chapel Hill. Their scienti?c merit and computational feasibility are demonstrated by preliminary simulation results and real examples. Ef?cient, reliable, and user-friendly open-source software with detailed documentation will be produced and disseminated to the broad scienti?c community. The proposed work will advance the ?eld of statistical genomics and facilitate trans-omics precision medicine studies of chronic diseases.

Public Health Relevance

The proposed research intends to develop novel and high-impact statistical methods for integrative analysis of trans-omics data from ongoing precision medicine studies of chronic diseases. The goal is to facilitate the creation of a new era of medicine in which each patient receives individualized care that matches their genetic code.

Agency
National Institute of Health (NIH)
Institute
National Human Genome Research Institute (NHGRI)
Type
Research Project (R01)
Project #
5R01HG009974-21
Application #
10085664
Study Section
Biostatistical Methods and Research Design Study Section (BMRD)
Program Officer
Chadwick, Lisa
Project Start
2000-04-01
Project End
2023-01-31
Budget Start
2021-02-01
Budget End
2022-01-31
Support Year
21
Fiscal Year
2021
Total Cost
Indirect Cost
Name
University of North Carolina Chapel Hill
Department
Biostatistics & Other Math Sci
Type
Schools of Public Health
DUNS #
608195277
City
Chapel Hill
State
NC
Country
United States
Zip Code
27599
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Sun, Wei; Bunn, Paul; Jin, Chong et al. (2018) The association between copy number aberration, DNA methylation and gene expression in tumor samples. Nucleic Acids Res 46:3009-3018