Systems-biology methods based on co-expression (co-exp) networks are powerful tools for understanding complex diseases. In a co-exp network, nodes represent genes, edges represent significant correlations between pairs of genes, and a module of connected nodes captures possible functional associations among the genes. Co-exp network methods have been successfully applied to understanding genetic architecture of human population and mechanisms of complex diseases, such as Alzheimer's disease and diabetes. Despite their success in biological and medical applications, the current co-exp network methods are unable to deal with genetic heterogeneity in the cohorts of samples of interest. Genetic heterogeneity is inherent in the cohorts of cases and controls in most complex disease studies. Therefore, failure to accommodate genetic heterogeneity will result in incorrect co-exp network structures and consequently lead to erroneous causal relationships between genetic variations and disease phenotypes. However, development of co-exp network methods that are adaptive in the presence of genetic heterogeneity is a challenge since no proper correlation measure that is resilient to genetic heterogeneity currently exists, which seriously limits the power and applicability of co-exp network analysis. To address this challenge, we introduce a new correlation measure of gene expression that is resilient to genetic heterogeneity and propose a novel individual-centric co-exp network approach to honor genetic heterogeneity. Our initial application of these methods to a set of gene expression data of Alzheimer's disease produced an impressive co-exp network module with coherent functions that are associated with the disease. This preliminary result provided the first set of convincing evidence on the validity of the new methods. In the proposed research, we will fully develop our novel co-expression network approach (Aim 1). In order to make the approach robust in the presence of genetic heterogeneity and noise in gene expression data, we will introduce a series of rigorous and unbiased tests for validating statistically and biological significant network modules (Aim 2). Furthermore, we will extend our approach to integrate the results of co-exp network modules with information of genetic variations to support genetics of gene expression studies (Aim 3). We will apply the new methods to Alzheimer's disease, psoriasis and prostate cancer, to examine the validity of our approach and more importantly, to gain deep insights into the genetic bases of these complex diseases that burden a substantial proportion of the human population (Aim 4). Finally, we will develop a software package of our methods, which will be freely available, and a web-based online service to the research community to ease the computational burden in complex disease studies (Aim 5). The proposed research represents a fundamental paradigm shift from conventional analyses of gene expression data and has the potential for significant advancements for the research of complex diseases as well as other population variations.

Public Health Relevance

A popular and successful approach for utilizing the growing amount of genome-wide gene expression profiling data is the co-expression network modeling and analysis. However, genetic heterogeneity, which is inherent in cohorts of disease cases and controls of interest, causes the existing co-expression network methods to fail or to be less effective. The proposed research aims to develop a new correlation measure for gene expression and a novel, individual-centric co-expression network approach to accommodate genetic heterogeneity;and subsequently utilize these methods to further our understandings of three complex diseases (Alzheimer's disease, psoriasis and prostate cancer) and for the development of an online computational service for the convenience of the research community.

Agency
National Institute of Health (NIH)
Institute
National Institute of General Medical Sciences (NIGMS)
Type
Research Project (R01)
Project #
5R01GM100364-03
Application #
8716783
Study Section
Biodata Management and Analysis Study Section (BDMA)
Program Officer
Brazhnik, Paul
Project Start
2012-09-01
Project End
2016-07-31
Budget Start
2014-08-01
Budget End
2015-07-31
Support Year
3
Fiscal Year
2014
Total Cost
Indirect Cost
Name
Washington University
Department
Biostatistics & Other Math Sci
Type
Biomed Engr/Col Engr/Engr Sta
DUNS #
City
Saint Louis
State
MO
Country
United States
Zip Code
63130
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