An integrative genomics approach to infer causal associations between gene expression and disease

By Eric E. Schadt, J. Lamb, Xia Yang, Jun Zhu, Steve Edwards, D. GuhaThakurta, S. Sieberts, S. Monks, M. Reitman, Chunsheng Zhang and 11 more. Nature Genetics, 2005

1,092 citations recorded by the source corpus.

Subfields: Genetics, Molecular Biology, Physiology

View Paper: https://www.semanticscholar.org/paper/76638b1bb15097926b6ecb57a7f25e44bd179a3d

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Genetics

1. Oliver Stegle
2. Gibran Hemani
3. Peter M. Visscher
4. Barbara Rakitsch
5. George Davey Smith
6. Michael E. Goddard
7. K. Tilling
8. Lin Song

In Molecular Biology

1. Oliver Stegle
2. Peter Langfelder
3. Steve Horvath
4. Barbara Rakitsch
5. Lin Song
6. Jeremy K. Nicholson
7. Debora Marks
8. Christopher Sander

In Physiology

1. Jeremy K. Nicholson
2. Steve Horvath
3. Joseph Loscalzo
4. David S. Wishart
5. Manolis Kellis
6. Jason Ernst

Citations in the corpus

• Genome-wide genetic association of complex traits in heterogeneous stock mice (2006, background): “Integration of expression, sequence and other functional data with mapping information from inbred strains has been shown to aid QTL identificatio”
• Weighted Gene Coexpression Network Analysis: State of the Art (2010, background): “Gene networks provide the potential to identify hundreds of genes that derive disease and that could serve as points for therapeutic interventions (Chen et al., 2008; Schadt et al., 2005).”
• Review of Causal Discovery Methods Based on Graphical Models (2019, background): “Some work infers causal associations between gene expression and disease (Schadt et al., 2005)–luckily, the causal direction between gene expression and disease is known.”
• Orienting the causal relationship between imprecisely measured traits using GWAS summary data (2017, methodology): “This forms the basis of a number of methods such as genetical genomics [17], the regression-based causal inference test (CIT) [4, 18], a structural equation modelling (SEM) implementation in the NEO software [5], and various other methods including Bayesian approaches [6].”
• A Bayesian Framework to Account for Complex Non-Genetic Factors in Gene Expression Levels Greatly Increases Power in eQTL Studies (2010, methodology): “The mouse expression data [3] consisted of 23,698 expression measurements for 111 F2 mouse lines, and genotypes at 137 genetic markers.”
• The Emerging Paradigm of Network Medicine in the Study of Human Disease (2012, methodology): “Adapted from Schadt et al, 55 with permission.”
• Molecular networks as sensors and drivers of common human diseases (2009, background): “However, given that variations in DNA do not on their own directly impact on physiological states associated with disease, there is the potential to enhance our understanding of GWAS data by layering in a hierarchy of phenotypes that define the molecular and physiological states associated with dise”
• Metabolic phenotyping in health and disease. (2008, background): “Three susceptibility genes for obesity were identified by an eQTL approach that predicted transcriptional responses to a single gene perturbation in mice (Schadt et al., 2005).”
• Modelling local gene networks increases power to detect trans-acting genetic effects on gene expression (2016, methodology): “Following the approach taken in [65], we employ a standard correlation test to assess dependence between genes and test if the p value is larger than a pre-defined threshold to assess independence of gene y A and gene y B .”