Genome-wide association studies (GWAS) are commonplace despite the lack of a comprehensive bioinformatics approach to the analysis of the data. The common method of analysis is to employ parametric statistics and then adjust for the large number of tests performed to limit false-positives (i.e. type 1 errors). This agnostic approach is preferred by some because no assumptions are made about which genes or genomic regions might be important. This logic suggests that the data should tell us where the important genetic variants are. The goal of our proposed research program is to specifically compare this agnostic approach with a bioinformatics approach that selects associated SNPs based on expert knowledge about biochemical pathways and gene function. We propose to develop a bioinformatics approach for selecting SNPs from a GWAS using knowledge about the biology of the genes being studied and the molecular pathology of disease (AIM 1). We will modify and extend the Exploratory Visual Analysis (EVA) database and software that was originally designed for microarray studies with pilot funding from the NLM BISTI program. We will then use this bioinformatics approach along with an agnostic statistical approach for detecting SNPs associated with plasma levels of tissue plasminogen activator (t-PA) and plasminogen activator inhibitor one (PAI-1) in a large population-based sample of Caucasians (n=2000) from the PREVEND study in Groningen, The Netherlands (AIM 2). Those SNPs identified by both methods in the PREVEND study will be evaluated first for replication in an independent population-based sample of Caucasians (n=2000) from the Rotterdam Study in the Netherlands and then for validation in a population-based sample of Blacks (n=2000) from the HeART Study in Ghana, Africa (AIM 3). Finally, we will specifically compare how many and which SNPs replicate and validate using the statistical approach and the bioinformatics approach (AIM 4). Our working hypothesis is that we will obtain more validated and hence more real SNPs using the bioinformatics approach.

Public Health Relevance

The technology to measure information about the human genome is advancing at a rapid pace. Despite these advance, the computational methods for analyzing the data have not kept pace. We will develop new computer algorithms and software that can be used to identify genetic biomarkers of common human diseases and then compare this approach with an analysis strategy that is based only on statistical methods.

National Institute of Health (NIH)
National Library of Medicine (NLM)
Research Project (R01)
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Biomedical Library and Informatics Review Committee (BLR)
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Ye, Jane
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Dartmouth College
Schools of Medicine
United States
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Payne, Joshua L; Moore, Jason H; Wagner, Andreas (2014) Robustness, evolvability, and the logic of genetic regulation. Artif Life 20:111-26
Darabos, Christian; White, Marquitta J; Graham, Britney E et al. (2014) The multiscale backbone of the human phenotype network based on biological pathways. BioData Min 7:1
Davis, Matthew A; Gilbert-Diamond, Diane; Karagas, Margaret R et al. (2014) A dietary-wide association study (DWAS) of environmental metal exposure in US children and adults. PLoS One 9:e104768
Frost, H Robert; Moore, Jason H (2014) Optimization of gene set annotations via entropy minimization over variable clusters (EMVC). Bioinformatics 30:1698-706
Pechenick, Dov A; Payne, Joshua L; Moore, Jason H (2014) Phenotypic robustness and the assortativity signature of human transcription factor networks. PLoS Comput Biol 10:e1003780
Yan, Jingwen; Du, Lei; Kim, Sungeun et al. (2014) Transcriptome-guided amyloid imaging genetic analysis via a novel structured sparse learning algorithm. Bioinformatics 30:i564-71
Penrod, Nadia M; Moore, Jason H (2014) Influence networks based on coexpression improve drug target discovery for the development of novel cancer therapeutics. BMC Syst Biol 8:12
Hu, Ting; Banzhaf, Wolfgang; Moore, Jason H (2014) The effects of recombination on phenotypic exploration and robustness in evolution. Artif Life 20:457-70
Gorlov, Ivan P; Moore, Jason H; Peng, Bo et al. (2014) SNP characteristics predict replication success in association studies. Hum Genet 133:1477-86
Leusink, M; Onland-Moret, N C; Asselbergs, F W et al. (2014) Cholesteryl ester transfer protein polymorphisms, statin use, and their impact on cholesterol levels and cardiovascular events. Clin Pharmacol Ther 95:314-20

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