Network-Based Analysis of eQTL Data to Prioritize Driver Mutations

Maeyer, Dries De; Weytjens, Bram; Raedt, Luc De; Marchal, Kathleen

doi:10.1093/gbe/evw010

Cited by 11 publications

(7 citation statements)

References 64 publications

(92 reference statements)

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“…Methods that use network models to represent molecular a priori knowledge on the organism/trait of interest (31) have been particularly successful to perform association analysis in clonal systems (32)(33)(34)(35). In the context of outbred populations, network-based methods have been applied for gene prioritization (36) or to increase the reliability of eQTL association mapping itself (37) but not yet for integrative association analysis.…”

Section: Significancementioning

confidence: 99%

Network-based integration of systems genetics data reveals pathways associated with lignocellulosic biomass accumulation and processing

Mizrachi

Verbeke

Christie

et al. 2017

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

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As a consequence of their remarkable adaptability, fast growth, and superior wood properties, eucalypt tree plantations have emerged as key renewable feedstocks (over 20 million ha globally) for the production of pulp, paper, bioenergy, and other lignocellulosic products. However, most biomass properties such as growth, wood density, and wood chemistry are complex traits that are hard to improve in long-lived perennials. Systems genetics, a process of harnessing multiple levels of component trait information (e.g., transcript, protein, and metabolite variation) in populations that vary in complex traits, has proven effective for dissecting the genetics and biology of such traits. We have applied a network-based data integration (NBDI) method for a systems-level analysis of genes, processes and pathways underlying biomass and bioenergy-related traits using a segregating Eucalyptus hybrid population. We show that the integrative approach can link biologically meaningful sets of genes to complex traits and at the same time reveal the molecular basis of trait variation. Gene sets identified for related woody biomass traits were found to share regulatory loci, cluster in network neighborhoods, and exhibit enrichment for molecular functions such as xylan metabolism and cell wall development. These findings offer a framework for identifying the molecular underpinnings of complex biomass and bioprocessing-related traits. A more thorough understanding of the molecular basis of plant biomass traits should provide additional opportunities for the establishment of a sustainable bio-based economy.systems genetics | lignocellulosic biomass | cell wall | bioenergy | network-based data integration

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Section: Significancementioning

confidence: 99%

Network-based integration of systems genetics data reveals pathways associated with lignocellulosic biomass accumulation and processing

Mizrachi

Verbeke

Christie

et al. 2017

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

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“…This search is driven by the interaction network and based on a decision theoretic subnetwork inference problem ( De Maeyer etal. 2015 ; De Maeyer etal. 2016 ).…”

Section: Resultsmentioning

confidence: 99%

Network-Based Identification of Adaptive Pathways in Evolved Ethanol-Tolerant Bacterial Populations

Swings

Weytjens

Schalck

et al. 2017

Molecular Biology and Evolution

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Efficient production of ethanol for use as a renewable fuel requires organisms with a high level of ethanol tolerance. However, this trait is complex and increased tolerance therefore requires mutations in multiple genes and pathways. Here, we use experimental evolution for a system-level analysis of adaptation of Escherichia coli to high ethanol stress. As adaptation to extreme stress often results in complex mutational data sets consisting of both causal and noncausal passenger mutations, identifying the true adaptive mutations in these settings is not trivial. Therefore, we developed a novel method named IAMBEE (Identification of Adaptive Mutations in Bacterial Evolution Experiments). IAMBEE exploits the temporal profile of the acquisition of mutations during evolution in combination with the functional implications of each mutation at the protein level. These data are mapped to a genome-wide interaction network to search for adaptive mutations at the level of pathways. The 16 evolved populations in our data set together harbored 2,286 mutated genes with 4,470 unique mutations. Analysis by IAMBEE significantly reduced this number and resulted in identification of 90 mutated genes and 345 unique mutations that are most likely to be adaptive. Moreover, IAMBEE not only enabled the identification of previously known pathways involved in ethanol tolerance, but also identified novel systems such as the AcrAB-TolC efflux pump and fatty acids biosynthesis and even allowed to gain insight into the temporal profile of adaptation to ethanol stress. Furthermore, this method offers a solid framework for identifying the molecular underpinnings of other complex traits as well.

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“…IAMBEE starts from the genotype information obtained from populations that independently acquired the same adaptive phenotype. The algorithm underlying IAMBEE-web is based on a probabilistic pathfinding approach (19,24,25). It uses a topology weighted network prior for the organism of interest to search for network neighborhoods that are affected in multiple parallel evolved populations (Figure 1).…”

Section: Methodsmentioning

confidence: 99%

IAMBEE: a web-service for the identification of adaptive pathways from parallel evolved clonal populations

Pérez-Romero

Weytjens

Decap

et al. 2019

Nucleic Acids Research

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IAMBEE is a web server designed for the Identification of Adaptive Mutations in Bacterial Evolution Experiments (IAMBEE). Input data consist of genotype information obtained from independently evolved clonal populations or strains that show the same adapted behavior (phenotype). To distinguish adaptive from passenger mutations, IAMBEE searches for neighborhoods in an organism-specific interaction network that are recurrently mutated in the adapted populations. This search for recurrently mutated network neighborhoods, as proxies for pathways is driven by additional information on the functional impact of the observed genetic changes and their dynamics during adaptive evolution. In addition, the search explicitly accounts for the differences in mutation rate between the independently evolved populations. Using this approach, IAMBEE allows exploiting parallel evolution to identify adaptive pathways. The web-server is freely available at http://bioinformatics.intec.ugent.be/iambee/ with no login requirement.

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Network-Based Analysis of eQTL Data to Prioritize Driver Mutations

Cited by 11 publications

References 64 publications

Network-based integration of systems genetics data reveals pathways associated with lignocellulosic biomass accumulation and processing

Network-based integration of systems genetics data reveals pathways associated with lignocellulosic biomass accumulation and processing

Network-Based Identification of Adaptive Pathways in Evolved Ethanol-Tolerant Bacterial Populations

IAMBEE: a web-service for the identification of adaptive pathways from parallel evolved clonal populations

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