Integrating large-scale functional genomic data to dissect the complexity of yeast regulatory networks

Zhu, Jun; Zhang, Bin; Smith, Erin N.; Drees, Becky; Brem, Rachel B.; Kruglyak, Leonid; Bumgarner, Roger E.; Schadt, Eric E.

doi:10.1038/ng.167

Cited by 515 publications

(622 citation statements)

References 36 publications

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“…Classic biochemical methods for placing genes in pathways cannot keep pace with the rapidly increasing amount of genomic information. To address this problem, we and others have been developing methods to infer networks from large-scale functional genomics data (1)(2)(3)(4)(5). The overall goals of such methods are to generate predictions of systems behavior and testable hypotheses of gene-to-gene influences.…”

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confidence: 99%

“…Prior knowledge, such as known transcription factor (TF) gene interactions, can be used in some cases to constrain directed edges in networks, but in many systems, such knowledge is incomplete. Hence, additional global data are often needed to construct predictive networks.One successful approach has been to use DNA variations that are correlated with given gene-expression values (expression QTLs) to infer directionality of edges in networks (4,5,11,12). An alternate approach is to infer networks from time-series data.…”

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“…One successful approach has been to use DNA variations that are correlated with given gene-expression values (expression QTLs) to infer directionality of edges in networks (4,5,11,12). An alternate approach is to infer networks from time-series data.…”

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Construction of regulatory networks using expression time-series data of a genotyped population

Yeung

Dombek²,

et al. 2011

Proc. Natl. Acad. Sci. U.S.A.

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The inference of regulatory and biochemical networks from largescale genomics data is a basic problem in molecular biology. The goal is to generate testable hypotheses of gene-to-gene influences and subsequently to design bench experiments to confirm these network predictions. Coexpression of genes in large-scale geneexpression data implies coregulation and potential gene-gene interactions, but provide little information about the direction of influences. Here, we use both time-series data and genetics data to infer directionality of edges in regulatory networks: time-series data contain information about the chronological order of regulatory events and genetics data allow us to map DNA variations to variations at the RNA level. We generate microarray data measuring time-dependent gene-expression levels in 95 genotyped yeast segregants subjected to a drug perturbation. We develop a Bayesian model averaging regression algorithm that incorporates external information from diverse data types to infer regulatory networks from the time-series and genetics data. Our algorithm is capable of generating feedback loops. We show that our inferred network recovers existing and novel regulatory relationships. Following network construction, we generate independent microarray data on selected deletion mutants to prospectively test network predictions. We demonstrate the potential of our network to discover de novo transcription-factor binding sites. Applying our construction method to previously published data demonstrates that our method is competitive with leading network construction algorithms in the literature.L arge-scale sequencing has provided a wealth of data on the presence, absence, and variation of genes within and between species. However, functional annotation is unavailable for many genes and the majority of genes within most species are not placed within regulatory or biochemical pathways. Classic biochemical methods for placing genes in pathways cannot keep pace with the rapidly increasing amount of genomic information. To address this problem, we and others have been developing methods to infer networks from large-scale functional genomics data (1-5). The overall goals of such methods are to generate predictions of systems behavior and testable hypotheses of gene-to-gene influences. Predictions of systems behavior can be useful even in the absence of detailed mechanistic understanding. For example, the predicted response to the inhibition of a given gene can guide the selection of drug targets (6). The generation of testable hypotheses provides a path to more rapidly gain mechanistic understanding as it focuses bench experiments on subsets of potential gene-to-gene influences. Moreover, network construction and experimental work can be used in an iterative process to converge on underlying mechanisms (7,8).At present, the data most used in network construction methods are from large-scale gene-expression studies. Coexpression of genes across a wide variety of experimental conditions implies coregulation (9, ...

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Construction of regulatory networks using expression time-series data of a genotyped population

Yeung

Dombek²,

et al. 2011

Proc. Natl. Acad. Sci. U.S.A.

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“…By first clustering transcripts with similar expression into groups, sparse partial leastsquares regression framework has been proposed to select markers associated with each cluster of genes (Chun and Keles, 2009). Adaptive multi-task least absolute shrinkage and selection operator (LASSO; Zhu et al, 2008) has been developed for detecting eQTLs that takes into account related expression traits simultaneously while incorporating many regulatory features. On the other hand, the graph-guided fused LASSO considers regulatory networks over multiple expression traits within an association analysis, but previous knowledge on genomic locations is not incorporated.…”

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Multiple-trait genome-wide association study based on principal component analysis for residual covariance matrix

Gao

Jiang

et al. 2014

Heredity

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Given the drawbacks of implementing multivariate analysis for mapping multiple traits in genome-wide association study (GWAS), principal component analysis (PCA) has been widely used to generate independent 'super traits' from the original multivariate phenotypic traits for the univariate analysis. However, parameter estimates in this framework may not be the same as those from the joint analysis of all traits, leading to spurious linkage results. In this paper, we propose to perform the PCA for residual covariance matrix instead of the phenotypical covariance matrix, based on which multiple traits are transformed to a group of pseudo principal components. The PCA for residual covariance matrix allows analyzing each pseudo principal component separately. In addition, all parameter estimates are equivalent to those obtained from the joint multivariate analysis under a linear transformation. However, a fast least absolute shrinkage and selection operator (LASSO) for estimating the sparse oversaturated genetic model greatly reduces the computational costs of this procedure. Extensive simulations show statistical and computational efficiencies of the proposed method. We illustrate this method in a GWAS for 20 slaughtering traits and meat quality traits in beef cattle. Heredity (2014) 113, 526-532; doi:10.1038/hdy.2014.57; published online 2 July 2014 INTRODUCTIONWith the advance of high-throughput genotyping technology, the paradigm of mapping quantitative trait locus (QTL) based on the linkage analysis of sparse genetic markers has gradually shifted to genome-wide association studies (GWAS) based on thousands and thousands of single-nucleotide polymorphisms (SNPs). On the other hand, association studies tend to involve more than one quantitative traits or complex diseases located in different regions of chromosomes, allowing the investigation of common genetic risk factors underlying multiple traits. Although these traits could be analyzed separately with univariate genetic model, statistical methods and algorithms have been developed for simultaneously analyzing multiple

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“…In these cases, methods based on statistical inference are an alternative way to identify key molecular relationships linked to disease phenotypes. A number of successes using this approach have been reported in the past several years [5][6][7][8][9][10][11][12][13] . At one level simple pair-wise analysis of alterations in human diseases, be it from DNA to phenotype in genome-wide association studies or from mRNA to phenotype in gene expression profiling studies, may be useful in providing essential lists of altered components.…”

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Developing Predictive Molecular Maps of Human Disease through Community-based Modeling

et al. 2011

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Integrating large-scale functional genomic data to dissect the complexity of yeast regulatory networks

Cited by 515 publications

References 36 publications

Construction of regulatory networks using expression time-series data of a genotyped population

Construction of regulatory networks using expression time-series data of a genotyped population

Multiple-trait genome-wide association study based on principal component analysis for residual covariance matrix

Developing Predictive Molecular Maps of Human Disease through Community-based Modeling

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