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.

National Institute of Health (NIH)
National Human Genome Research Institute (NHGRI)
Research Project (R01)
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Biostatistical Methods and Research Design Study Section (BMRD)
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Chadwick, Lisa
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University of North Carolina Chapel Hill
Biostatistics & Other Math Sci
Schools of Public Health
Chapel Hill
United States
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