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Machine Learning Analysis of Genetic Modulators of Vaccine Immune Response

疫苗免疫反应基因调节剂的机器学习分析

基本信息

批准号：
7919847
负责人：
Brett McKinney
金额：
$ 34.63万
依托单位：
UNIVERSITY OF TULSA
依托单位国家：
美国
项目类别：
财政年份：
2009
资助国家：
美国
起止时间：
2009-09-04 至 2011-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/7919847
关键词：
Accounting Adverse event Affect Algorithms Anthrax Vaccines Anthrax disease Antibody Formation Appearance Architecture Bioinformatics Biological Centers for Disease Control and Prevention (U.S.)Classification Clinical Data Collection Communities Complex Computer software Coupling DNA Sequence Data Decision Trees Dependency Development Disease Disease susceptibility Engineering Gases Gene Combinations Genes Genetic Genetic Epistasis Genetic Models Genetic Polymorphism Goals Heterogeneity Human Immune response Immunity Inflammatory Information Theory Learning Left Logistic Regressions Machine Learning Measures Methods Modeling Noise Pathway interactions Phenotype Predisposition Research Sample Size Serological Simulate Single Nucleotide Polymorphism Smallpox Smallpox Vaccine Software Tools Statistical Methods Susceptibility Gene System Testing Thermodynamics Vaccination Vaccines Variant abstracting base evaporation forest gene environment interaction gene interaction genetic analysis genetic association genetic variant genome wide association study genome-wide genome-wide analysis novel open source particle stem user-friendly volunteer

项目摘要

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.

摘要疫苗免疫应答遗传调节因子的机器学习分析。该提案描述了一种机器学习策略的发展，以确定相互作用的易感性多基因生物学终点中的基因座，重点关注天花和炭疽疫苗相关不良事件 (AEs)和血清学抗体应答的变化。天花疫苗接种后出现的AE 源于炎症通路的过度刺激，可能受到多种相互作用的遗传因素的影响。因素其中一些基因间的相互作用可能是上位性的，对任何一个基因都没有明显的边际效应。单一变量。需要分析方法来测试全基因组数据中的关联，以解释遗传变异之间的条件依赖性，同时仍然考虑具有高边际效应我们介绍了一种机器学习特征选择和优化方法，蒸发冷却（EC）是一种基于信息论和冷却过程统计热力学的新型冷却技术，通过蒸发相互作用的粒子系统。EC学习者的目标是识别易感性或保护性基因在全基因组DNA序列数据。这种新颖的滤波方法，不包括关于基因相互作用结构或相互作用顺序的假设，已被证明，确定一系列疾病易感性模型，包括边际主效应和纯相互作用方面的影响.在全基因组序列数据中表征多因子表型的遗传基础也是由于存在大量的噪声变量，或者与表型无关。因此，EC算法蒸发（即，删除）噪声变量，留下一个富集与给定表型相关的变体的最小集合。我们建议推进这一点描述和解释单基因、基因-基因和基因-环境相互作用的方法，可能调节复杂的表型，如疫苗相关AE和人体免疫应答。这战略将在人工数据的帮助下制定，这些数据是在真实的环境中观察到的各种条件下模拟的数据，该策略将在单核苷酸多态性（SNP）和志愿者的临床数据上进行测试来自NIAID/NIH赞助的评估Aventis Pasteur天花疫苗的试验，对照组申办的评价吸附炭疽疫苗的试验。