Genome-wide association studies (GWAS) have been moderately successful in identifying common variants that are associated with phenotypic differences. However, the greater part of the heritable component in any given complex trait has yet to be explained. New technologies which allow for the characterization of rare variants, structural variants, and expression data are providing new insights into trait association. Unfortunately, the data is being created faster than the field is able to analyze it. Current common-variant analytical methods are not powered to manage sequence data;therefore, new methods designed to manage high-throughput data are necessary. These new methods should also be capable of analyzing interactions (epistasis and gene-environment) and prepared to incorporate other """"""""-omic"""""""" data as it increasingly becomes available. A single method's ability to perform these complex tasks will enable the researcher to paint a complete picture of a trait that incorporates many forms of genetic and environmental information. Identifying gene-environment interactions are of particular importance since environment is one of few modifiable variables. One approach for developing this analytical tool is to use known biological information in a two step-analysis. The first step uses knowledge-based biology and predicted function as guides to collapse rare variants into weighted bins. This is necessary to decrease the computational load of sequence data, as well as increase the power of detecting an association among rare variants. The binned variants along with common variants can then be tested for association immediately (exit this pipeline) or be packaged for Biofilter. In the second step, Biofilter creates and assesses potential interactions (gene-gene or gene-environment). These interaction models are then tested in genome-wide data for statistically significant association. In both steps, the biological information is derived from a systematic integration multiple public databases of gene groupings and sets of disease-related genes to produce multi-SNP models that have an established biological foundation. The advantages of incorporating prior knowledge are: reduced search space, increased power to identify associations, and inference of relevant biology for any statistically significant result. The first goal of this project is to develop BioBn, an algorithm that will use domain-knowledge to guide the collapsing and binning of rare variants. The second goal is to compare this method to other published collapsing methods using simulated data. The third goal is to create a pipeline for data to be collapsed and evaluated by Biofilter, specifically to test for gene-environment interactions using individuals in an Age-relatd Macular Degeneration study.

Public Health Relevance

This project was developed to meet the demands of increasing sequence data and address the issues of missing heritability. BioBin is a novel method to collapse and bin rare variants based on biological information which can then be analyzed using Biofilter to determine associations between gene-environment interactions.

Agency
National Institute of Health (NIH)
Institute
National Institute on Aging (NIA)
Type
Individual Predoctoral NRSA for M.D./Ph.D. Fellowships (ADAMHA) (F30)
Project #
1F30AG041570-01
Application #
8255001
Study Section
Special Emphasis Panel (ZRG1-F08-E (20))
Program Officer
Chen, Wen G
Project Start
2011-09-16
Project End
2016-09-15
Budget Start
2011-09-16
Budget End
2012-09-15
Support Year
1
Fiscal Year
2011
Total Cost
$38,687
Indirect Cost
Name
Vanderbilt University Medical Center
Department
Physiology
Type
Schools of Medicine
DUNS #
004413456
City
Nashville
State
TN
Country
United States
Zip Code
37212
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Moore, Carrie B; Wallace, John R; Frase, Alex T et al. (2013) BioBin: a bioinformatics tool for automating the binning of rare variants using publicly available biological knowledge. BMC Med Genomics 6 Suppl 2:S6
Moore, Carrie B; Wallace, John R; Wolfe, Daniel J et al. (2013) Low frequency variants, collapsed based on biological knowledge, uncover complexity of population stratification in 1000 genomes project data. PLoS Genet 9:e1003959
Moore, Carrie B; Wallace, John R; Frase, Alex T et al. (2013) Using BioBin to explore rare variant population stratification. Pac Symp Biocomput :332-43
Pendergrass, Sarah A; Verma, Shefali S; Holzinger, Emily R et al. (2013) Next-generation analysis of cataracts: determining knowledge driven gene-gene interactions using Biofilter, and gene-environment interactions using the PhenX Toolkit. Pac Symp Biocomput :147-58
Pendergrass, Sarah A; Frase, Alex; Wallace, John et al. (2013) Genomic analyses with biofilter 2.0: knowledge driven filtering, annotation, and model development. BioData Min 6:25