It is now recognized that many visual diseases are influenced by complex interactions between multiple different genetic variants. As a result, our ability to predict susceptibility to visual diseases will depend critically on the computational, mathematical and statistical modeling methods and software that are available for making sense of high-dimensional genetic data. We propose here a bioinformatics research project to develop network modeling approaches for identifying combinations of genetic biomarkers associated with visual disease endpoints. Our working hypothesis is that a systems-based bioinformatics approach using network modeling will play a very important role in confronting the complexity of the relationship between genomic variation and visual diseases. We will first develop and evaluate modeling methods to infer large-scale genetic interaction networks from genome-wide association studies (AIM 1). We will then apply the modeling methods developed in AIM 1 to the inference of genetic interaction networks from genome-wide association data in subjects with and without visual diseases (AIM 2). Next, we will utilize the inferred genetic interaction networks to guide the development of predictive genetic models of visual diseases (AIM 3). Finally, all network modeling methods will be released to the vision research community as part of a popular user-friendly, freely available and open-source software package (AIM 4). We anticipate that the network modeling methods and software developed and distributed as part of this project will play an important role in the development of the genetic tests that will be necessary to identify those at risk for visual diseases.

Public Health Relevance

The network modeling methods and software developed and distributed as part of this bioinformatics research project will play an important role in the development of the genetic tests that will be necessary to identify those at risk for common diseases such as glaucoma and age-related macular degeneration.

Agency
National Institute of Health (NIH)
Institute
National Eye Institute (NEI)
Type
Research Project (R01)
Project #
7R01EY022300-04
Application #
9031889
Study Section
Special Emphasis Panel (ZEY1-VSN (01))
Program Officer
Chin, Hemin R
Project Start
2015-03-10
Project End
2016-06-30
Budget Start
2015-03-10
Budget End
2016-06-30
Support Year
4
Fiscal Year
2014
Total Cost
$162,000
Indirect Cost
$60,750
Name
University of Pennsylvania
Department
Internal Medicine/Medicine
Type
Schools of Dentistry
DUNS #
042250712
City
Philadelphia
State
PA
Country
United States
Zip Code
19104
Urbanowicz, Ryan J; Olson, Randal S; Schmitt, Peter et al. (2018) Benchmarking relief-based feature selection methods for bioinformatics data mining. J Biomed Inform 85:168-188
Urbanowicz, Ryan J; Meeker, Melissa; La Cava, William et al. (2018) Relief-based feature selection: Introduction and review. J Biomed Inform 85:189-203
Huang, Minjun; Graham, Britney E; Zhang, Ge et al. (2016) Evolutionary triangulation: informing genetic association studies with evolutionary evidence. BioData Min 9:12
Frost, H Robert; Shen, Li; Saykin, Andrew J et al. (2016) Identifying significant gene-environment interactions using a combination of screening testing and hierarchical false discovery rate control. Genet Epidemiol 40:544-557
Greene, Anna C; Giffin, Kristine A; Greene, Casey S et al. (2016) Adapting bioinformatics curricula for big data. Brief Bioinform 17:43-50
Chen, Xue-Wen; Gao, Jean X (2016) Big Data Bioinformatics. Methods 111:1-2
Darabos, Christian; Qiu, Jingya; Moore, Jason H (2016) AN INTEGRATED NETWORK APPROACH TO IDENTIFYING BIOLOGICAL PATHWAYS AND ENVIRONMENTAL EXPOSURE INTERACTIONS IN COMPLEX DISEASES. Pac Symp Biocomput 21:9-20
Qiu, Jingya; Moore, Jason H; Darabos, Christian (2016) Studying the Genetics of Complex Disease With Ancestry-Specific Human Phenotype Networks: The Case of Type 2 Diabetes in East Asian Populations. Genet Epidemiol 40:293-303
Li, Jing; Malley, James D; Andrew, Angeline S et al. (2016) Detecting gene-gene interactions using a permutation-based random forest method. BioData Min 9:14
Moore, Jason H (2015) Epistasis analysis using ReliefF. Methods Mol Biol 1253:315-25

Showing the most recent 10 out of 35 publications