Machine Learning Analysis of Genetic Modulators of Vaccine Immune Response. This proposal describes the development of a machine-learning strategy to identify interacting susceptibility loci in polygenic biological endpoints, with a focus on smallpox and anthrax vaccine-related adverse events (AEs) and variation in serologic antibody response. The appearance of AEs following smallpox vaccination stems from excess stimulation of inflammatory pathways and is likely affected by multiple, interacting genetic factors. Some of these gene-gene interactions may be epistatic, having no distinct marginal effect for any single variant. Analytical approaches are needed for testing association in genome-wide data to account for conditional dependencies between genetic variants while still accounting for co-occurring variants with high marginal effects. We have introduced a machine-learning feature selection and optimization method called Evaporative Cooling (EC), which is based on information theory and the statistical thermodynamics of cooling a system of interacting particles by evaporation. The objective of the EC learner is the identification of susceptibility or protective genes in genome-wide DNA sequence data. This novel filter method, which includes no assumptions regarding gene interaction architecture or interaction order, has been shown to identify a spectrum of disease susceptibility models, including marginal main effects and pure interaction effects. Characterizing the genetic basis of multifactorial phenotypes in genome-wide sequence data is also computationally challenging due to the presence of a large number of noise variants, or variants that are irrelevant to the phenotype. Thus, the EC algorithm evaporates (i.e., removes) noise variants, leaving behind a minimal collection of variants enriched for relevance to the given phenotype. We propose to advance this method to characterize and interpret singe-gene, gene-gene and gene-environment interactions all of which may modulate complex phenotypes such as vaccine-associated AEs and human immune response. This strategy will be developed with the aid of artificial data, simulated under a variety of conditions observed in real data, and the strategy will be tested on single nucleotide polymorphism (SNP) and clinical data from volunteers from a NIAID/NIH-sponsored trial to evaluate the Aventis Pasteur Smallpox Vaccine and a Center for Disease Control sponsored trial to evaluate Anthrax Vaccine Adsorbed.
Susceptibility to common complex disorders is likely due to a combination of gene-gene and gene-environment interactions as well as single-gene associations. In this proposal, we will develop a new bioinformatics strategy for identifying networks of single- nucleotide polymorphisms (SNPs) that influence susceptibility to complex disorders from genome-wide data. The proposed strategy will be developed to identify networks associated with adverse events and the human immune response following smallpox and anthrax vaccination.
| Davis, Nicholas A; Lareau, Caleb A; White, Bill C et al. (2013) Encore: Genetic Association Interaction Network centrality pipeline and application to SLE exome data. Genet Epidemiol 37:614-21 |
| Pandey, A; Davis, N A; White, B C et al. (2012) Epistasis network centrality analysis yields pathway replication across two GWAS cohorts for bipolar disorder. Transl Psychiatry 2:e154 |
| Davis, Nicholas A; Pandey, Ahwan; McKinney, B A (2011) Real-world comparison of CPU and GPU implementations of SNPrank: a network analysis tool for GWAS. Bioinformatics 27:284-5 |
| Davis, N A; Crowe Jr, J E; Pajewski, N M et al. (2010) Surfing a genetic association interaction network to identify modulators of antibody response to smallpox vaccine. Genes Immun 11:630-6 |
