# Statistical Methods in Trans-Omics Chronic Disease Research

> **NIH NIH R01** · UNIV OF NORTH CAROLINA CHAPEL HILL · 2021 · $305,167

## Abstract

Project Summary
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 specific
aims of this competing renewal application include: (1) derivation of efficient and robust statistics for integrative
association analysis of multiple omics platforms (DNA sequences, RNA expressions, methylation profiles, protein
expressions, metabolomics profiles, 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-specific 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
efficient 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 scientific merit and computational feasibility are demonstrated by preliminary simulation results
and real examples. Efficient, reliable, and user-friendly open-source software with detailed documentation will
be produced and disseminated to the broad scientific community. The proposed work will advance the field of
statistical genomics and facilitate trans-omics precision medicine studies of chronic diseases.

## Key facts

- **NIH application ID:** 10085664
- **Project number:** 5R01HG009974-21
- **Recipient organization:** UNIV OF NORTH CAROLINA CHAPEL HILL
- **Principal Investigator:** DANYU LIN
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $305,167
- **Award type:** 5
- **Project period:** 2000-04-01 → 2023-01-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10085664

## Citation

> US National Institutes of Health, RePORTER application 10085664, Statistical Methods in Trans-Omics Chronic Disease Research (5R01HG009974-21). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10085664. Licensed CC0.

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