A fundamental challenge in life sciences is the characterization of genetic factors that underlie phenotypic differences. Thanks to the advanced sequencing technologies, an enormous amount of genetic variants have been identified and cataloged. Such data hold great potential to understand how genes affect phenotypes and contribute to the susceptibility to environmental stimulus. However, the existing computational methods for analyzing and interpreting the high?throughput genetic data are still in their infancy. We propose to systematically investigate the computational and statistical principles in modeling and discovering genetic basis of complex phenotypes. The proposed research provides answers to the following fundamental questions in genetic association study: (1) How to effectively and efficiently assess statistical significance of the findings? (2) How to account for the relatedness between samples in genetic association study? (3) How to accurately capture possible interactions between multiple genetic factors and their joint contribution to phenotypic variation? In particular, we will develop data structures and efficient algorithms for accurate and robust significance assessment that account for local population structure and joint effect of multiple genetic factors. The proposed computational tools will be integrated into software packages under common application framework adopted by the broad scientific community.

Public Health Relevance

A fundamental challenge in life sciences is the characterization of genetic factors that underlie phenotypic differences. Existing methods are not able to adequately address the complexity of high throughput data. Innovative computational models and methods developed in this project will enable scientists more effectively analyze the research data, thus further understanding of human diseases and speed the development diagnostic tools, cures, and therapies.

Agency
National Institute of Health (NIH)
Institute
National Institute of General Medical Sciences (NIGMS)
Type
Research Project (R01)
Project #
3R01GM115833-02S1
Application #
9272965
Study Section
Biodata Management and Analysis Study Section (BDMA)
Program Officer
Ravichandran, Veerasamy
Project Start
2015-09-15
Project End
2018-06-30
Budget Start
2016-07-01
Budget End
2017-06-30
Support Year
2
Fiscal Year
2016
Total Cost
$85,370
Indirect Cost
Name
University of California Los Angeles
Department
Biostatistics & Other Math Sci
Type
Schools of Engineering
DUNS #
092530369
City
Los Angeles
State
CA
Country
United States
Zip Code
90095
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