Book contents
- Frontmatter
- Contents
- Contributors
- Introduction
- Part A Horizontal Meta-Analysis
- Part B Vertical Integrative Analysis (General Methods)
- Part C Vertical Integrative Analysis (Methods Specialized to Particular Data Types)
- 12 eQTL and Directed Graphical Model
- 13 MicroRNAs: Target Prediction and Involvement in Gene Regulatory Networks
- 14 Integration of Cancer Omics Data into a Whole-Cell Pathway Model for Patient-Specific Interpretation
- 15 Analyzing Combinations of Somatic Mutations in Cancer Genomes
- 16 A Mass-Action-Based Model for Gene Expression Regulation in Dynamic Systems
- 17 From Transcription Factor Binding and Histone Modification to Gene Expression: Integrative Quantitative Models
- 18 Data Integration on Noncoding RNA Studies
- 19 Drug-Pathway Association Analysis: Integration of High-Dimensional Transcriptional and Drug Sensitivity Profile
- Index
- Color plates
12 - eQTL and Directed Graphical Model
from Part C - Vertical Integrative Analysis (Methods Specialized to Particular Data Types)
Published online by Cambridge University Press: 05 September 2015
- Frontmatter
- Contents
- Contributors
- Introduction
- Part A Horizontal Meta-Analysis
- Part B Vertical Integrative Analysis (General Methods)
- Part C Vertical Integrative Analysis (Methods Specialized to Particular Data Types)
- 12 eQTL and Directed Graphical Model
- 13 MicroRNAs: Target Prediction and Involvement in Gene Regulatory Networks
- 14 Integration of Cancer Omics Data into a Whole-Cell Pathway Model for Patient-Specific Interpretation
- 15 Analyzing Combinations of Somatic Mutations in Cancer Genomes
- 16 A Mass-Action-Based Model for Gene Expression Regulation in Dynamic Systems
- 17 From Transcription Factor Binding and Histone Modification to Gene Expression: Integrative Quantitative Models
- 18 Data Integration on Noncoding RNA Studies
- 19 Drug-Pathway Association Analysis: Integration of High-Dimensional Transcriptional and Drug Sensitivity Profile
- Index
- Color plates
Summary
Abstract
Gene expression quantitative trait loci (eQTL) are genetic loci that are associated with gene expression traits. The study of the eQTL, or the genetic basis of gene expression variation, not only improves our understanding of gene expression regulation but also brings insights on the functional roles of genetic variations that influence phenotypic outcomes, such as complex human diseases. In contrast to genome-wide association studies, where the signal-to-noise ratio is often low, the eQTLs often have stronger influence on gene expression variation, and hundreds or thousands of eQTLs may be recovered. We conjecture that one of the major applications of eQTL findings is to construct directed graphical models of gene expression data. In this chapter, we review the methods for eQTL mapping, constructing directed graphical models, and the approaches to construct directed graphical models using eQTL data.
Introduction
The expression of a gene may be associated with the genotype of one or more genetic loci, and such loci are often referred to as gene expression quantitative trait loci (eQTLs). An eQTL study is an integrated study of genetic variants and gene expression across a group of samples. In many eQTL studies, phenotype data (e.g., disease status or drug response) are also collected, and it is of great interest to use eQTLresults to inform or guide the phenotype study.Apromising approach toward this goal is to construct a directed gene-gene network using eQTL data. In this chapter, we provide reviews and discussions on constructing directed graphical models using eQTL data.
It has been well appreciated that a gene network perspective is crucial to understanding the molecular basis of complex traits, such as many human diseases (Barabási et al., 2011; Marbach et al., 2012). Gene networks can be studied by undirected or directed graphs. For example, a protein-protein interaction graph, where two proteins are connected if they interact with each other, is an undirected graph. A biological pathway often corresponds to a directed graph. The meaning of a directed edge within a pathway depends on the nature of the pathway. In a gene regulation pathway, an edge A → B indicates A regulates B. In a signaling pathway, an edge A → B indicates signal is transmitted from A to B. Pathway-level analysis is a crucial step to understanding the molecular basis of complex traits, including many human diseases.
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- Integrating Omics Data , pp. 271 - 290Publisher: Cambridge University PressPrint publication year: 2015