Epistatic and Cross Tissue Analysis for Human Gene Expression Traits

人类基因表达特征的上位性和跨组织分析

• 批准号: 7935037
• 负责人: JUN S LIU
• 金额: $ 30.38万
• 依托单位: HARVARD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-09-17 至 2012-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7935037
• 关键词: Address; Affect; Architecture; Bayesian Method; Binding Sites; Biological Process; Chromosome Mapping; Code; Communities; Complex; DNA; Data; Disease; Gene Expression; Genes; Genetic; Genetic Epistasis; Genetic Variation; Genomics; Genotype; Goals; Human; Individual; Intercistronic Region; Introns; Light; Linear Regressions; Link; Maps; Methods; Modeling; Molecular; Motivation; Mus; Organism; Phenotype; Predisposition; Regulator Genes; Science; Source; Statistical Methods; Structure; System; Tissues; Variant; Vitelliform macular dystrophy; base; cohort; disease phenotype; disorder risk; falls; genome wide association study; human disease; infancy; model development; novel; public health relevance; research study; simulation; success; trait; transcription factor; user friendly software

DESCRIPTION (provided by applicant): Epistatic and cross-tissue analysis for human gene expression traits Genome wide association studies (GWAS) have delivered unprecedented rates of discovery associating variations in DNA with common human diseases. However, how these SNPs affect human diseases are not clear in most cases. Gene expression is the intermediate between SNPs and disease phenotypes. Methods to maximally leverage gene expression and genetic variation information collected in human cohorts over multiple tissues show great promise for characterizing not only the genetic architecture of disease but the molecule networks that define disease. The long-term goal of this application is to develop and implement novel statistical methods to identify networks of genes affecting an individual's susceptibility to complex phenotypes like disease. Here, we propose several model developments that not only enhance our power to detect eQTL in single or multiple tissues, but that identify eQTL and interactions among eQTL in the molecular network contexts that define biological processes associated with disease: (1) A Bayesian modeling approach that simultaneously models the total distribution of all genes and all markers will be developed. The strength of our approach will be its ability to detect epistasis with high power when the marginal effects are weak, addressing a key weakness of all other eQTL mapping methods. (2) A likelihood based approach for inferring causal relationships that also incorporates transcription factor binding site information will be developed. (3) An approach for linking subnetworks in different tissues to diseases will be developed. Also a method to dissect causal/ reactive relationships between tissues will be developed. (4) The proposed methods will be extensively validated via simulations and, more importantly, on multi-tissue mouse and human cohort data we have generated. All methods will be implemented in user-friendly software and made available to the scientific community. PUBLIC HEALTH RELEVANCE: Epistatic and cross-tissue analysis for human gene expression traits Gene expression is the intermediate between SNPs and disease phenotypes. Methods to maximimally leverage gene expression and genetic variation information collected in human cohorts over multiple tissues show great promise for characterizing not only the genetic architecture of disease but the molecule networks that define disease. We propose several model developments that not only enhance our power to detect eQTL in single or multiple tissues, but that identify eQTL and interactions among eQTL in the molecular network contexts that define biological processes associated with disease.

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