Genome-wide association studies (GWAS) and next-generation sequencing are now commonplace despite a lack of comprehensive bioinformatics approaches for relating genotype to phenotype. The common method of analysis is to employ parametric statistics and then adjust for the large number of tests performed to limit false-positives. This agnostic approach is preferred by some because no assumptions are made about which genes or genomic regions might be important. The goal of our proposed research program continuation is to develop and evaluate a bioinformatics approach that analyzes genetic associations in the context of expert knowledge about biochemical pathways, gene function and experimental results using gene set enrichment (GSE) methods. An important challenge for success in this domain is the quality of the expert knowledge that is available in public databases such as Gene Ontology (GO). We first propose to develop and evaluate a novel Data-driven Ontology Refinement Algorithm (DORA) for improving the quality of genetic and genomic annotations (AIM 1). Improving the quality of annotations will in turn improve GSE results. We will then develop a comprehensive bioinformatics approach to the analysis of high-throughput genetic association results that considers functional DNA elements, genes, and gene function as important contexts. We will first determine whether considering data from the Encyclopedia of DNA Elements (ENCODE) database improves GSE analysis at the level of gene regions (AIM 2). Next we will determine whether using GO annotations refined by our novel DORA algorithm (DORA-GO) improves GSE analysis at the gene set level above and beyond that provided by GO (AIM 3). We will determine the validity of these methods by assessing the replication of the results in independent data (AIM 4).
AIMS 1 -4 will be accomplished using several large population-based genetic studies of pre-clinical cardiovascular disease (CVD) as measured by left ventricular mass (LVM). Our working hypothesis is that we will obtain more replicated and hence more real genetic associations using our novel bioinformatics methods that embrace, rather than ignore, prior biological knowledge.

Public Health Relevance

The bioinformatics methods and software developed and distributed as part of this research project will play an important role in advancing our ability to fully exploit genome-wide association data for common, complex diseases.

Agency
National Institute of Health (NIH)
Institute
National Library of Medicine (NLM)
Type
Research Project (R01)
Project #
5R01LM010098-08
Application #
8913769
Study Section
Special Emphasis Panel (ZLM1-ZH-C (01))
Program Officer
Ye, Jane
Project Start
2009-09-30
Project End
2018-09-29
Budget Start
2016-09-30
Budget End
2017-09-29
Support Year
8
Fiscal Year
2016
Total Cost
$337,041
Indirect Cost
$75,766
Name
University of Pennsylvania
Department
Type
Schools of Medicine
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
Manduchi, Elisabetta; Williams, Scott M; Chesi, Alessandra et al. (2018) Leveraging epigenomics and contactomics data to investigate SNP pairs in GWAS. Hum Genet 137:413-425
Urbanowicz, Ryan J; Meeker, Melissa; La Cava, William et al. (2018) Relief-based feature selection: Introduction and review. J Biomed Inform 85:189-203
Chernikova, Diana A; Madan, Juliette C; Housman, Molly L et al. (2018) The premature infant gut microbiome during the first 6 weeks of life differs based on gestational maturity at birth. Pediatr Res 84:71-79
Piette, Elizabeth R; Moore, Jason H (2018) Improving machine learning reproducibility in genetic association studies with proportional instance cross validation (PICV). BioData Min 11:6
Tragante, Vinicius; Hemerich, Daiane; Alshabeeb, Mohammad et al. (2018) Druggability of Coronary Artery Disease Risk Loci. Circ Genom Precis Med 11:e001977
Teumer, Alexander; Gambaro, Giovanni; Corre, Tanguy et al. (2018) Negative effect of vitamin D on kidney function: a Mendelian randomization study. Nephrol Dial Transplant 33:2139-2145
Beaulieu-Jones, Brett K; Lavage, Daniel R; Snyder, John W et al. (2018) Characterizing and Managing Missing Structured Data in Electronic Health Records: Data Analysis. JMIR Med Inform 6:e11
Manduchi, Elisabetta; Chesi, Alessandra; Hall, Molly A et al. (2018) Leveraging putative enhancer-promoter interactions to investigate two-way epistasis in Type 2 Diabetes GWAS. Pac Symp Biocomput 23:548-558
Vajravelu, Ravy K; Scott, Frank I; Mamtani, Ronac et al. (2018) Medication class enrichment analysis: a novel algorithm to analyze multiple pharmacologic exposures simultaneously using electronic health record data. J Am Med Inform Assoc 25:780-789

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