Machine Learning Analysis of Genetic Modulators of Vaccine Immune Response
Machine Learning Analysis of Genetic Modulators of Vaccine Immune Response
批准号:
7919847
负责人:
Brett McKinney
金额:
$34.63万
依托单位:
依托单位国家:
美国
项目类别:
财政年份:
2009
资助国家:
美国
项目状态:
已结题
起止时间:
2009-09-04 至 2011-08-31
关键词:
AccountingAdverse eventAffectAlgorithmsAnthrax VaccinesAnthrax diseaseAntibody FormationAppearanceArchitectureBioinformaticsBiologicalCenters for Disease Control and Prevention (U.S.)ClassificationClinical DataCollectionCommunitiesComplexComputer softwareCouplingDNA SequenceDataDecision TreesDependencyDevelopmentDiseaseDisease susceptibilityEngineeringGasesGene CombinationsGenesGeneticGenetic EpistasisGenetic ModelsGenetic PolymorphismGoalsHeterogeneityHumanImmune responseImmunityInflammatoryInformation TheoryLearningLeftLogistic RegressionsMachine LearningMeasuresMethodsModelingNoisePathway interactionsPhenotypePredispositionResearchSample SizeSerologicalSimulateSingle Nucleotide PolymorphismSmallpoxSmallpox VaccineSoftware ToolsStatistical MethodsSusceptibility GeneSystemTestingThermodynamicsVaccinationVaccinesVariantabstractingbaseevaporationforestgene environment interactiongene interactiongenetic analysisgenetic associationgenetic variantgenome wide association studygenome-widegenome-wide analysisnovelopen sourceparticlestemuser-friendlyvolunteer
中文摘要
摘要
英文摘要
Abstract
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.
期刊论文(3)
专著(0)
科研奖励(0)
会议论文
DOI:
10.3389/fgene.2011.00109
发表时间:
2011
期刊:
Frontiers in genetics
影响因子:
3.7
作者:
[McKinney BA, Pajewski NM]
通讯作者:
Pajewski NM
DOI:
10.1038/tp.2012.80
发表时间:
2012-08-14
期刊:
Translational psychiatry
影响因子:
6.8
作者:
[Pandey A, Davis NA, White BC, Pajewski NM, Savitz J, Drevets WC, McKinney BA]
通讯作者:
McKinney BA
GENE-GENE INTERACTION NETWORKS IN GENOME WIDE ASSOCIATION STUDIES
-
批准号:8364348
-
项目类别:
-
资助金额:$0.11万
-
财政年份:2011
-
负责人:Brett McKinney
-
依托单位:
Cytokine Signaling Network Response to Smallpox Vaccine
-
批准号:7389130
-
项目类别:
-
资助金额:$7.38万
-
财政年份:2006
-
负责人:Brett McKinney
-
依托单位:
Cytokine Signaling Network Response to Smallpox Vaccine
-
批准号:7491749
-
项目类别:
-
资助金额:$10.68万
-
财政年份:2006
-
负责人:Brett McKinney
-
依托单位:
Cytokine Signaling Network Response to Smallpox Vaccine
-
批准号:7208003
-
项目类别:
-
资助金额:$10.38万
-
财政年份:2006
-
负责人:Brett McKinney
-
依托单位:
Cytokine Signaling Network Response to Smallpox Vaccine
-
批准号:7099789
-
项目类别:
-
资助金额:$2.7万
-
财政年份:2006
-
负责人:Brett McKinney
-
依托单位:
Cytokine Signaling Network Response to Smallpox Vaccine
-
批准号:7612652
-
项目类别:
-
资助金额:$7.02万
-
财政年份:2006
-
负责人:Brett McKinney
-
依托单位:
Cytokine Signaling Network Response to Smallpox Vaccine
-
批准号:8004341
-
项目类别:
-
资助金额:$3.97万
-
财政年份:2006
-
负责人:Brett McKinney
-
依托单位:
海外基金