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On predicting regulatory genes by analysis of functional networks in C. elegans.

Valba OV, Nechaev SK, Sterken MG, Snoek LB, Kammenga JE, Vasieva OO - BioData Min (2015)

Bottom Line: Integration of different types of connectivity data (such as co-expression and genetic interactions) in one network has proven to benefit 'guilt by association' analysis.Several algorithms were compared in respect to their predictive potential in different network connectivity contexts.The modularity of less continuous genetic interaction network does not correspond to modularity of the dense network comprised by gene co-expression interactions.

View Article: PubMed Central - PubMed

Affiliation: Laboratory of Nematology, Wageningen University, Wageninge, Netherlands.

ABSTRACT

Background: Connectivity networks, which reflect multiple interactions between genes and proteins, possess not only a descriptive but also a predictive value, as new connections can be extrapolated and tested by means of computational analysis. Integration of different types of connectivity data (such as co-expression and genetic interactions) in one network has proven to benefit 'guilt by association' analysis. However predictive values of connectives of different types, that had their specific functional meaning and topological characteristics were not obvious, and have been addressed in this analysis.

Methods: eQTL data for 3 experimental C.elegans age groups were retrieved from WormQTL. WormNet has been used to obtain pair-wise gene interactions. The Shortest Path Function (SPF) has been adopted for statistical validation of the co-expressed gene clusters and for computational prediction of their potential gene expression regulators from a network context. A new SPF-based algorithm has been applied to genetic interactions sub-networks adjacent to the clusters of co-expressed genes for ranking the most likely gene expression regulators causal to eQTLs.

Results: We have demonstrated that known co-expression and genetic interactions between C. elegans genes can be complementary in predicting gene expression regulators. Several algorithms were compared in respect to their predictive potential in different network connectivity contexts. We found that genes associated with eQTLs are highly clustered in a C. elegans co-expression sub-network, and their adjacent genetic interactions provide the optimal functional connectivity environment for application of the new SPF-based algorithm. It was successfully tested in the reverse-prediction analysis on groups of genes with known regulators and applied to co-expressed genes and experimentally observed expression quantitative trait loci (eQTLs).

Conclusions: This analysis demonstrates differences in topology and connectivity of co-expression and genetic interactions sub-networks in WormNet. The modularity of less continuous genetic interaction network does not correspond to modularity of the dense network comprised by gene co-expression interactions. However the genetic interaction network can be used much more efficiently with the SPF method in prediction of potential regulators of gene expression. The developed method can be used for validation of functional significance of suggested eQTLs and a discovery of new regulatory modules.

No MeSH data available.


Related in: MedlinePlus

Candidate regulator prediction for natural variation in gene expression regulation using the SPF method. Genetical genomics (eQTL mapping) identifies loci involved in the regulation of gene expression. In these experiments eQTL-hotspots (trans-bands) can be identified, which indicate loci regulating the expression of many genes. a An outcome of a genetical genomics experiment is represented schematically. The genes for which the expression levels where measured in a recombinant inbred line population are shown on the Y-axis. The x-axis shows the location of the eQTL peak position (potential regulatory loci). The blue locus is an example of an eQTL-hotspot, corresponding to a position of a putative regulator of multiple genes (shown in blue). b By eQTL mapping we have obtained two types of information which can be used to identfy the regulatory gene. Firstly target genes are identified as having an eQTL at a particular genomic location. Secondly, the locus harbouring many eQTL is likely to contain a gene affecting expression of multiple targets. Therefore it is very likely that the candidate gene has a regulatory function, for example, a transcription factor or a receptor. c In many cases eQTL hotspot loci contain > 100 genes [12] and validation is important before pursuing the potential regulator. The SPF method can be used to validate eQTL hotspot by investigating if the genes mapping to the eQTL hotspot share a relationship based on hundreds of experiments categorised in WormNet [34]. A validated group of genes will have more connections in WormNet compared to a random group. Thereby the SPF method can identify false-positive eQTL hotspots, for example caused by experimental variation. D: The identification of potential regulators is laborious [29], and candidates prioritizing is imperative. A validated group of co-regulated genes can be used to predict the most likely regulator by selecting genes on the eQTL hotspot locus with the most direct connections to the target genes (dark orange circles), or indirect connections via other genes (yellow circles)
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Fig1: Candidate regulator prediction for natural variation in gene expression regulation using the SPF method. Genetical genomics (eQTL mapping) identifies loci involved in the regulation of gene expression. In these experiments eQTL-hotspots (trans-bands) can be identified, which indicate loci regulating the expression of many genes. a An outcome of a genetical genomics experiment is represented schematically. The genes for which the expression levels where measured in a recombinant inbred line population are shown on the Y-axis. The x-axis shows the location of the eQTL peak position (potential regulatory loci). The blue locus is an example of an eQTL-hotspot, corresponding to a position of a putative regulator of multiple genes (shown in blue). b By eQTL mapping we have obtained two types of information which can be used to identfy the regulatory gene. Firstly target genes are identified as having an eQTL at a particular genomic location. Secondly, the locus harbouring many eQTL is likely to contain a gene affecting expression of multiple targets. Therefore it is very likely that the candidate gene has a regulatory function, for example, a transcription factor or a receptor. c In many cases eQTL hotspot loci contain > 100 genes [12] and validation is important before pursuing the potential regulator. The SPF method can be used to validate eQTL hotspot by investigating if the genes mapping to the eQTL hotspot share a relationship based on hundreds of experiments categorised in WormNet [34]. A validated group of genes will have more connections in WormNet compared to a random group. Thereby the SPF method can identify false-positive eQTL hotspots, for example caused by experimental variation. D: The identification of potential regulators is laborious [29], and candidates prioritizing is imperative. A validated group of co-regulated genes can be used to predict the most likely regulator by selecting genes on the eQTL hotspot locus with the most direct connections to the target genes (dark orange circles), or indirect connections via other genes (yellow circles)

Mentions: Thus defined, the SPF has a very transparent meaning, since it defines the sum of lengths of reciprocal paths between a pair of genes. If i and j are not linked within the network, the contribution to the SPF from this pair (i,j) equals zero. Whereas, for a directly linked node pair the contribution reaches its maximum (2). So, the SPF can be used to quantitatively characterize the connectivity of a gene cluster within a given network (see Fig. 1).Fig. 1


On predicting regulatory genes by analysis of functional networks in C. elegans.

Valba OV, Nechaev SK, Sterken MG, Snoek LB, Kammenga JE, Vasieva OO - BioData Min (2015)

Candidate regulator prediction for natural variation in gene expression regulation using the SPF method. Genetical genomics (eQTL mapping) identifies loci involved in the regulation of gene expression. In these experiments eQTL-hotspots (trans-bands) can be identified, which indicate loci regulating the expression of many genes. a An outcome of a genetical genomics experiment is represented schematically. The genes for which the expression levels where measured in a recombinant inbred line population are shown on the Y-axis. The x-axis shows the location of the eQTL peak position (potential regulatory loci). The blue locus is an example of an eQTL-hotspot, corresponding to a position of a putative regulator of multiple genes (shown in blue). b By eQTL mapping we have obtained two types of information which can be used to identfy the regulatory gene. Firstly target genes are identified as having an eQTL at a particular genomic location. Secondly, the locus harbouring many eQTL is likely to contain a gene affecting expression of multiple targets. Therefore it is very likely that the candidate gene has a regulatory function, for example, a transcription factor or a receptor. c In many cases eQTL hotspot loci contain > 100 genes [12] and validation is important before pursuing the potential regulator. The SPF method can be used to validate eQTL hotspot by investigating if the genes mapping to the eQTL hotspot share a relationship based on hundreds of experiments categorised in WormNet [34]. A validated group of genes will have more connections in WormNet compared to a random group. Thereby the SPF method can identify false-positive eQTL hotspots, for example caused by experimental variation. D: The identification of potential regulators is laborious [29], and candidates prioritizing is imperative. A validated group of co-regulated genes can be used to predict the most likely regulator by selecting genes on the eQTL hotspot locus with the most direct connections to the target genes (dark orange circles), or indirect connections via other genes (yellow circles)
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

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Fig1: Candidate regulator prediction for natural variation in gene expression regulation using the SPF method. Genetical genomics (eQTL mapping) identifies loci involved in the regulation of gene expression. In these experiments eQTL-hotspots (trans-bands) can be identified, which indicate loci regulating the expression of many genes. a An outcome of a genetical genomics experiment is represented schematically. The genes for which the expression levels where measured in a recombinant inbred line population are shown on the Y-axis. The x-axis shows the location of the eQTL peak position (potential regulatory loci). The blue locus is an example of an eQTL-hotspot, corresponding to a position of a putative regulator of multiple genes (shown in blue). b By eQTL mapping we have obtained two types of information which can be used to identfy the regulatory gene. Firstly target genes are identified as having an eQTL at a particular genomic location. Secondly, the locus harbouring many eQTL is likely to contain a gene affecting expression of multiple targets. Therefore it is very likely that the candidate gene has a regulatory function, for example, a transcription factor or a receptor. c In many cases eQTL hotspot loci contain > 100 genes [12] and validation is important before pursuing the potential regulator. The SPF method can be used to validate eQTL hotspot by investigating if the genes mapping to the eQTL hotspot share a relationship based on hundreds of experiments categorised in WormNet [34]. A validated group of genes will have more connections in WormNet compared to a random group. Thereby the SPF method can identify false-positive eQTL hotspots, for example caused by experimental variation. D: The identification of potential regulators is laborious [29], and candidates prioritizing is imperative. A validated group of co-regulated genes can be used to predict the most likely regulator by selecting genes on the eQTL hotspot locus with the most direct connections to the target genes (dark orange circles), or indirect connections via other genes (yellow circles)
Mentions: Thus defined, the SPF has a very transparent meaning, since it defines the sum of lengths of reciprocal paths between a pair of genes. If i and j are not linked within the network, the contribution to the SPF from this pair (i,j) equals zero. Whereas, for a directly linked node pair the contribution reaches its maximum (2). So, the SPF can be used to quantitatively characterize the connectivity of a gene cluster within a given network (see Fig. 1).Fig. 1

Bottom Line: Integration of different types of connectivity data (such as co-expression and genetic interactions) in one network has proven to benefit 'guilt by association' analysis.Several algorithms were compared in respect to their predictive potential in different network connectivity contexts.The modularity of less continuous genetic interaction network does not correspond to modularity of the dense network comprised by gene co-expression interactions.

View Article: PubMed Central - PubMed

Affiliation: Laboratory of Nematology, Wageningen University, Wageninge, Netherlands.

ABSTRACT

Background: Connectivity networks, which reflect multiple interactions between genes and proteins, possess not only a descriptive but also a predictive value, as new connections can be extrapolated and tested by means of computational analysis. Integration of different types of connectivity data (such as co-expression and genetic interactions) in one network has proven to benefit 'guilt by association' analysis. However predictive values of connectives of different types, that had their specific functional meaning and topological characteristics were not obvious, and have been addressed in this analysis.

Methods: eQTL data for 3 experimental C.elegans age groups were retrieved from WormQTL. WormNet has been used to obtain pair-wise gene interactions. The Shortest Path Function (SPF) has been adopted for statistical validation of the co-expressed gene clusters and for computational prediction of their potential gene expression regulators from a network context. A new SPF-based algorithm has been applied to genetic interactions sub-networks adjacent to the clusters of co-expressed genes for ranking the most likely gene expression regulators causal to eQTLs.

Results: We have demonstrated that known co-expression and genetic interactions between C. elegans genes can be complementary in predicting gene expression regulators. Several algorithms were compared in respect to their predictive potential in different network connectivity contexts. We found that genes associated with eQTLs are highly clustered in a C. elegans co-expression sub-network, and their adjacent genetic interactions provide the optimal functional connectivity environment for application of the new SPF-based algorithm. It was successfully tested in the reverse-prediction analysis on groups of genes with known regulators and applied to co-expressed genes and experimentally observed expression quantitative trait loci (eQTLs).

Conclusions: This analysis demonstrates differences in topology and connectivity of co-expression and genetic interactions sub-networks in WormNet. The modularity of less continuous genetic interaction network does not correspond to modularity of the dense network comprised by gene co-expression interactions. However the genetic interaction network can be used much more efficiently with the SPF method in prediction of potential regulators of gene expression. The developed method can be used for validation of functional significance of suggested eQTLs and a discovery of new regulatory modules.

No MeSH data available.


Related in: MedlinePlus