Chen, L. S., Emmert-Streib, F. & Storey, J. D. Harnessing naturally randomized transcription to infer regulatory relationships among genes. Genome Biol. 8, R219

Department of Biostatistics, University of Washington, 1705 NE Pacific St, Seattle, WA 98195, USA.
Genome biology (Impact Factor: 10.81). 02/2007; 8(10):R219. DOI: 10.1186/gb-2007-8-10-r219
Source: PubMed


We develop an approach utilizing randomized genotypes to rigorously infer causal regulatory relationships among genes at the transcriptional level, based on experiments in which genotyping and expression profiling are performed. This approach can be used to build transcriptional regulatory networks and to identify putative regulators of genes. We apply the method to an experiment in yeast, in which genes known to be in the same processes and functions are recovered in the resulting transcriptional regulatory network.

Download full-text


Available from: Frank Emmert-Streib, Feb 11, 2015
7 Reads
  • Source
    • "However, the application of the two-sample approach is dependent on an additional, generally unverifiable assumption that the independent cohorts are drawn from the same underlying population. Building off the ‘Causality Equivalence Theorem’ presented by Chen [52], another recently suggested method to infer causal indirect effects of genotype on outcome relies on a series of models to statistically test necessary conditional independencies between covariates [53]. However, the application of this approach was deemed inappropriate given the associations between RBC folate and methylation level conditional on MTHFR genotype were likely influenced by common unmeasured causes of folate and methylation levels. "
    [Show abstract] [Hide abstract]
    ABSTRACT: Investigation of the biological mechanism by which folate acts to affect fetal development can inform appraisal of expected benefits and risk management. This research is ethically imperative given the ubiquity of folic acid fortified products in the US. Considering that folate is an essential component in the one-carbon metabolism pathway that provides methyl groups for DNA methylation, epigenetic modifications provide a putative molecular mechanism mediating the effect of folic acid supplementation on neonatal and pediatric outcomes. In this study we use a Mendelian Randomization (Mendelian Randomization) approach to assess the effect of red blood cell (RBC) folate on genome-wide DNA methylation in cord blood. Site-specific CpG methylation within the proximal promoter regions of approximately 14,500 genes was analyzed using the Illumina Infinium Human Methylation27 Bead Chip for 50 infants from the Epigenetic Birth Cohort at Brigham and Women's Hospital in Boston. Using methylenetetrahydrofolate reductase genotype as the instrument, the Mendelian Randomization approach identified 7 CpG loci with a significant (mostly positive) association between RBC folate and methylation level. Among the genes in closest proximity to this significant subset of CpG loci, several enriched biologic processes were involved in nucleic acid transport and metabolic processing. Compared to the standard ordinary least squares regression method, our estimates were demonstrated to be more robust to unmeasured confounding. To the authors' knowledge, this is the largest genome-wide analysis of the effects of folate on methylation pattern, and the first to employ Mendelian Randomization to assess the effects of an exposure on epigenetic modifications. These results can help guide future analyses of the causal effects of periconceptional folate levels on candidate pathways.
    BMC Bioinformatics 12/2013; 14(1):353. DOI:10.1186/1471-2105-14-353 · 2.58 Impact Factor
  • Source
    • "Theoretical evidence in the form of " Causality Equivalence Theorem " has been proposed by Chen et al. [21] to establish causal relationship. According to the theorem, under the assumption that í µí±‹ is randomized, the following conditions are needed to establish a causal relation: C1: í µí±‹ and í µí±€ are associated, C2: í µí±‹ and í µí±Œ are associated, C3: í µí±‹ is independent of í µí±Œ | í µí±€. "
    [Show abstract] [Hide abstract]
    ABSTRACT: Background. The genome-wide association studies (GWAS) have been successful during the last few years. A key challenge is that the interpretation of the results is not straightforward, especially for transacting SNPs. Integration of transcriptome data into GWAS may provide clues elucidating the mechanisms by which a genetic variant leads to a disease. Methods. Here, we developed a novel mediation analysis approach to identify new expression quantitative trait loci (eQTL) driving CYP2D6 activity by combining genotype, gene expression, and enzyme activity data. Results. 389,573 and 1,214,416 SNP-transcript-CYP2D6 activity trios are found strongly associated (P < 10−5, FDR = 16.6% and 11.7%) for two different genotype platforms, namely, Affymetrix and Illumina, respectively. The majority of eQTLs are trans-SNPs. A single polymorphism leads to widespread downstream changes in the expression of distant genes by affecting major regulators or transcription factors (TFs), which would be visible as an eQTL hotspot and can lead to large and consistent biological effects. Overlapped eQTL hotspots with the mediators lead to the discovery of 64 TFs. Conclusions. Our mediation analysis is a powerful approach in identifying the trans-QTL-phenotype associations. It improves our understanding of the functional genetic variations for the liver metabolism mechanisms.
    10/2013; 2013:493019. DOI:10.1155/2013/493019
  • Source
    • "Integration of genetic information with genomic, proteomic, and metabolomic data has been used to infer causal relationships among phenotypes (Schadt et al. 2005; Li et al. 2006; Kulp and Jagalur 2006; Chen et al. 2007; Zhu et al. 2004, 2007, 2008; Aten et al. 2008; Liu et al. 2008; Chaibub Neto et al. 2008, 2009; Winrow et al. 2009; Millstein et al. 2009). Current approaches for causal inference in systems genetics can be classified into whole network scoring methods (Zhu et al. 2004, 2007, 2008; Li et al. 2006; Liu et al. 2008; Chaibub Neto et al. 2008, 2010; Winrow et al. 2009; Hageman et al. 2011) or pairwise methods, which focus on the inference of causal relationships among pairs of phenotypes (Schadt et al. 2005; Li et al. 2006; Kulp and Jagalur 2006; Chen et al. 2007; Aten et al. 2008; Millstein et al. 2009; Li et al. 2010; Duarte and Zeng 2011). In this article we develop a pairwise approach for causal inference among pairs of phenotypes. "
    [Show abstract] [Hide abstract]
    ABSTRACT: Current efforts in systems genetics have focused on the development of statistical approaches that aim to disentangle causal relationships among molecular phenotypes in segregating populations. Reverse engineering of transcriptional networks plays a key role in the understanding of gene regulation. However, transcriptional regulation is only one possible mechanism, as methylation, phosphorylation, direct protein-protein interaction, transcription factor binding, etc., can also contribute to gene regulation. These additional modes of regulation can be interpreted as unobserved variables in the transcriptional gene network, and can potentially impact its reconstruction accuracy. We develop tests of causal direction for a pair of phenotypes that may be embedded in a more complicated but unobserved network by extending Vuong's selection tests for misspecified models. Our tests provide a significance level, which is unavailable for the widely used AIC and BIC criteria. We evaluate the performance of our tests against the AIC, BIC and a recently published causality inference test in simulation studies. We compare the precision of causal calls using biologically validated causal relationships extracted from a database of 247 knockout experiments in yeast. Our model selection tests are more precise, showing greatly reduced false positive rates compared to the alternative approaches. In practice, this is a useful feature since follow up studies tend to be time consuming and expensive and, hence, it is important for the experimentalist to have causal predictions with low false positive rates.
    Genetics 01/2013; 193(3). DOI:10.1534/genetics.112.147124 · 5.96 Impact Factor
Show more