Multivariate Analysis of Candidate Blood Pressure Response Genes in Hypertensives

高血压候选血压反应基因的多变量分析

• 批准号: 7939919
• 负责人: Andrei Rodin
• 金额: $ 7.5万
• 依托单位: UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-30 至 2012-09-29
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7939919
• 关键词: Accounting; Algorithms; Antihypertensive Agents; Attention; Biological; Blood Pressure; Candidate Disease Gene; Data; Data Analyses; Data Set; Decision Trees; Dependency; Engineering; Environmental Risk Factor; Generations; Genes; Genetic; Genetic Epistasis; Genetic Heterogeneity; Genetic Variation; Genotype; Goals; Guidelines; Hybrids; Hydrochlorothiazide; Hypertension; Individual; Joints; Kidney; Knowledge; Literature; Measures; Methodology; Methods; Modeling; Multivariate Analysis; Other Genetics; Pathway interactions; Pharmaceutical Preparations; Phenotype; Physiological; Plant Roots; Probability; Renin-Angiotensin-Aldosterone System; Role; Sampling; Series; Simulate; Sodium; Staging; Statistical Methods; System; Systems Biology; Testing; Thiazide Diuretics; Tubular formation; Variant; complex biological systems; computer based statistical methods; computer science; data mining; follow-up; genetic analysis; genetic epidemiology; heuristics; improved; innovation; insight; interest; network models; reconstruction; response; thiazide; tool

DESCRIPTION (provided by applicant): The primary goal of this project is to reverse-engineer the interplay of gene variation (in candidate genes influencing renal tubular sodium transport and involved in renin-angiotensin-aldosterone system) and other variables contributing to the interindividual differences in blood pressure (BP) response to a thiazide diuretic. We will apply "pathway", or "systems biology", data analysis methods to the association study of BP response to a hydrochlorothiazide in 585 hypertensive individuals. Previously, they have been genotyped for genetic variation in 16 candidate genes, and measured for a number of intermediate phenotypes and other variables. A series of univariate analyses have been carried out, and a small number of statistically significant predictors of blood pressure response (and certain other intermediate phenotypes of interest) have been identified. However, the scope of these analyses was very limited. We intend to follow up with the multivariate analysis in order to gain the systemic understanding of the dataset and the underlying biological pathways. We intend to apply innovative multivariate analysis methods, predominantly Bayesian Network (BN) modeling and boosted classifiers / clusterers, to the data. Such data mining methods have been very successful when applied to the similar datasets in other domains. Finally, we will use this dataset to confirm, in the context of genetic epidemiology, the practical utility of BN modeling with respect to sensitivity and robustness. We will also investigate various technical aspects of BN reconstruction, as applied to the genetic data.

描述（由申请人提供）：