Regulatory network construction in
            <i>Arabidopsis</i>
            by using genome-wide gene expression quantitative trait loci

Keurentjes, Joost J. B.; Fu, Jingyuan; Terpstra, Inez; García, Juan Manuel Vicent; Ackerveken, Guido Van den; Snoek, L. Basten; Peeters, Anton J. M.; Vreugdenhil, D.; Koornneef, M.; Jansen, Ritsert C.

doi:10.1073/pnas.0610429104

Cited by 316 publications

(419 citation statements)

References 63 publications

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“…Quantitative trait locus mapping with human insight has enabled the characterization and prediction of numerous pathways. And this has lead initiatives to create an automated algorithm which takes the metabolite and genetic data to predict metabolic pathways [45,[54][55][56][57][58]. However, a direct comparison of computational predictions to empirical pathway evidence showed the none of the current algorithms are sufficient and new approaches need to be developed to enable this automated pathway discovery [59].…”

Section: Quantitative Trait Locus Mapping To Reverse Engineer the Shamentioning

confidence: 99%

Synthetic biology of metabolism: using natural variation to reverse engineer systems

Kliebenstein

2014

Current Opinion in Plant Biology

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A goal of metabolic engineering is to take a plant and introduce new or modify existing pathways in a directed and predictable fashion. However, existing data does not provide the necessary level of information to allow for predictive models to be generated. One avenue to reverse engineer the necessary information is to study the genetic control of natural variation in plant primary and secondary metabolism. These studies are showing that any engineering model will have to incorporate information about 1000s of genes in both the nuclear and organellar genome to optimize the function of the introduced pathway. Further, these genes may interact in an unpredictable fashion complicating any engineering approach as it moves from the one or two gene manipulation to higher order stacking efforts. Finally, metabolic engineering may be influenced by a previously unrecognized potential for a plant to measure the metabolites within it. In combination, these observations from natural variation provide a beginning to help improve current efforts at metabolic engineering. Introduction A significant goal of synthetic biology or biological engineering is to take an existant organism and alter it in a directed and predictive fashion to introduce a new phenotype that had not previously existed in that organism. Some of these efforts start with introducing a new metabolic pathway into an organism to provide a better source of a commercially important chemical or to make new chemicals entirely [1][2][3]. Other efforts are focused at taking agronomically important traits such as defensive chemicals, nitrogen fixation or perennial growth from one species and transferring these traits into other species that do not contain them [4 ,5,6]. However these efforts while exciting frequently run into great difficulty and for this review article, I will focus on one the potential of using information from natural variation studies to facilitate these efforts for metabolic engineering.To predictively engineer a new metabolic pathway into a plant with optimal efficiency requires a base level of knowledge that likely does not exist to any full level. To generate full predictive ability we would need much deeper understanding of the following questions. First, how many genes within the plant need to be manipulated to facilitate the integration of the new pathway into the organisms metabolic and regulatory network. How do these genes interact, are they solely additive or is there evidence of more complex epistasis that complicates predictive capacity? Finally, are these genes solely in the nuclear genome or is there causal variation also in the organellar genomes? Most efforts to answer these questions focus largely on forward or single-gene reverse genetics approaches which are highly time consuming and difficult to scale up to the necessary level.An alternative approach to understanding a system is to use the concept of reverse engineering [7,8]. The goal of reverse engineering is to take a functioning system, say an existing metabolic pathway, ...

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Section: Quantitative Trait Locus Mapping To Reverse Engineer the Shamentioning

confidence: 99%

Synthetic biology of metabolism: using natural variation to reverse engineer systems

Kliebenstein

2014

Current Opinion in Plant Biology

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“…These associations are computed using standard QTL approaches 17 generally using normalized transcript expression levels, derived from tissue-specific or disease-specific microarray experiments using biologic samples from the genotyped population. Quantitative traits have a long history in plant genetics going back to Mendel, with recent initial efforts in "systems genetics" focused on Arabidopsis [18][19][20][21][22] . However, eQTL analysis has been increasingly applied in the interpretation of human disease GWAS (see Cookson et al 17 for examples).…”

Section: Associating Variation With Regulationmentioning

confidence: 99%

“…Early approaches to a posteriori network construction 18,30 use functional annotations to construct putative regulatory networks from the eQTL associations. More recent work (e.g., Aten et al 31 and Kang et al 32 ) construct probabilistic causal networks by inferring regulatory relationships among transcripts from expression probe correlation and the strength of SNP-probe associations.…”

Section: Associating Variation With Regulationmentioning

confidence: 99%

Linking Variants from Genome-Wide Association Analysis to Function via Transcriptional Network Analysis

Martini

Nair

2012

Methods in Molecular Biology

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A current challenge in interpretation of Genome Wide Association studies (GWAS) is to establish the mechanistic links between the measured genotype and observed phenotype. The integration of gene expression with disease GWAS is emerging as an important strategy for deciphering these regulatory mechanisms. For renal disease, the availability of both tissue-and disease-specific expression data makes the strategy a compelling option. In this review, three approaches of integrating Single Nucleotide Polymorphism (SNP) genotypes with transcriptional regulation are discussed: (1) interpreting the functional role of transcripts affected by a SNP, (2) identifying the mechanistic role of non-coding SNPs in regulation, and (3) identifying regulatory candidate SNPs with expression associations. Combining these strategies in an integrative manner should allow the discovery of more extensive regulatory information. Linking genetics to systems biology more directly promises the opportunity to explain how genetic variants contribute to disease in a truly holistic manner.

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“…eQTL studies are already very popular (Brem et al, 2002;Brem and Kruglyak, 2005;Keurentjes et al, 2007) and with rapidly decreasing costs of RNA-seq technologies (Wang et al, 2009;Majewski and Pastinen, 2011) will likely become more popular in the future. These include several major efforts collecting expression from multiple-tissues in humans (Cheung et al, 2005;Stranger et al, 2007;Emilsson et al, 2008;Spielman et al, 2007;Baker, 2012) and mice (Chesler et al, 2005;Bystrykh et al, 2005).…”

Section: Introductionmentioning

confidence: 99%

Efficiently Identifying Significant Associations in Genome-Wide Association Studies

Kostem

Eskin

2013

Lecture Notes in Computer Science

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Over the past several years, genome-wide association studies (GWAS) have implicated hundreds of genes in common disease. More recently, the GWAS approach has been utilized to identify regions of the genome that harbor variation affecting gene expression or expression quantitative trait loci (eQTLs). Unlike GWAS applied to clinical traits, where only a handful of phenotypes are analyzed per study, in eQTL studies, tens of thousands of gene expression levels are measured, and the GWAS approach is applied to each gene expression level. This leads to computing billions of statistical tests and requires substantial computational resources, particularly when applying novel statistical methods such as mixed models. We introduce a novel two-stage testing procedure that identifies all of the significant associations more efficiently than testing all the single nucleotide polymorphisms (SNPs). In the first stage, a small number of informative SNPs, or proxies, across the genome are tested. Based on their observed associations, our approach locates the regions that may contain significant SNPs and only tests additional SNPs from those regions. We show through simulations and analysis of real GWAS datasets that the proposed two-stage procedure increases the computational speed by a factor of 10. Additionally, efficient implementation of our software increases the computational speed relative to the state-of-the-art testing approaches by a factor of 75.

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Regulatory network construction in Arabidopsis by using genome-wide gene expression quantitative trait loci

Cited by 316 publications

References 63 publications

Synthetic biology of metabolism: using natural variation to reverse engineer systems

Synthetic biology of metabolism: using natural variation to reverse engineer systems

Linking Variants from Genome-Wide Association Analysis to Function via Transcriptional Network Analysis

Efficiently Identifying Significant Associations in Genome-Wide Association Studies

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