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POEM: Identifying Joint Additive Effects on Regulatory Circuits.

Botzman M, Nachshon A, Brodt A, Gat-Viks I - Front Genet (2016)

Bottom Line: POEM is specifically designed to achieve high performance in the case of additive joint effects.Our study reveals widespread additive, trans-acting pairwise effects on gene modules, characterizes their organizational principles, and highlights high-order interconnections between modules within the immune signaling network.These analyses elucidate the central role of additive pairwise effect in regulatory circuits, and provide computational tools for future investigations into the interplay between eQTLs.

View Article: PubMed Central - PubMed

Affiliation: Department of Cell Research and Immunology, The George S. Wise Faculty of Life Sciences, Tel Aviv University Tel Aviv, Israel.

ABSTRACT

Motivation: Expression Quantitative Trait Locus (eQTL) mapping tackles the problem of identifying variation in DNA sequence that have an effect on the transcriptional regulatory network. Major computational efforts are aimed at characterizing the joint effects of several eQTLs acting in concert to govern the expression of the same genes. Yet, progress toward a comprehensive prediction of such joint effects is limited. For example, existing eQTL methods commonly discover interacting loci affecting the expression levels of a module of co-regulated genes. Such "modularization" approaches, however, are focused on epistatic relations and thus have limited utility for the case of additive (non-epistatic) effects.

Results: Here we present POEM (Pairwise effect On Expression Modules), a methodology for identifying pairwise eQTL effects on gene modules. POEM is specifically designed to achieve high performance in the case of additive joint effects. We applied POEM to transcription profiles measured in bone marrow-derived dendritic cells across a population of genotyped mice. Our study reveals widespread additive, trans-acting pairwise effects on gene modules, characterizes their organizational principles, and highlights high-order interconnections between modules within the immune signaling network. These analyses elucidate the central role of additive pairwise effect in regulatory circuits, and provide computational tools for future investigations into the interplay between eQTLs.

Availability: The software described in this article is available at csgi.tau.ac.il/POEM/.

No MeSH data available.


Overview of the POEM algorithm. POEM takes as input a collection of expression traits from a certain population of genotyped individuals. The procedure is initiated with a non-conditioned scan (top right). The analysis then consists of two iterative stages: learning primary eQTLs after conditioning on the secondary eQTLs and vice versa (middle). The two steps are repeated k times. POEM relies on grouping of the expression traits based on their co-association to the primary and secondary eQTLs. Significant overlaps between the resulting primary and secondary groups are referred to as “poeModules” (bottom). Such poeModules are interpreted as promising pairwise effects that act on the same group of traits.
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Figure 1: Overview of the POEM algorithm. POEM takes as input a collection of expression traits from a certain population of genotyped individuals. The procedure is initiated with a non-conditioned scan (top right). The analysis then consists of two iterative stages: learning primary eQTLs after conditioning on the secondary eQTLs and vice versa (middle). The two steps are repeated k times. POEM relies on grouping of the expression traits based on their co-association to the primary and secondary eQTLs. Significant overlaps between the resulting primary and secondary groups are referred to as “poeModules” (bottom). Such poeModules are interpreted as promising pairwise effects that act on the same group of traits.

Mentions: POEM identifies pairwise effects using the RBSR approach (see above) and further extends RBSR in two ways (Figure 1). First, we expect that grouping of traits can enhance the identification of eQTLs. In accordance, POEM constructs the map of eQTLs on the basis of co-association groups that were generated by the InVamod algorithm. Secondly, a major challenge may arise from an erroneous identification of each eQTL in the presence of the confounding effect of another eQTL, since the pairwise effect may lead to an increased marginal variance. For example, in the RBSR method, the scan for the primary eQTLs is conducted without removing the confounding effect of the secondary eQTLs, and this may blur the primary signals. Consequently, the scan for the secondary eQTLs may also be blurred since it relies on the former inaccuracies in the primary eQTLs. To tackle this challenge, we iterate between two steps. In stage 1—learning primary eQTLs—POEM applies a 1-locus scan that is conditioned on the secondary eQTL map, thereby providing a refined collection of primary eQTLs. In stage 2—learning secondary eQTLs—POEM applies a 1-locus scan that is conditioned on the primary eQTL map, thus providing a refined collection of secondary eQTLs. Both steps involve grouping of the traits to ensure the robustness of the learned model. We initiate the process with a standard 1-locus scan and then repeat the iterative process k times. POEM is implemented in Perl and is publicly available in csgi.tau.ac.il/POEM/. An outline of the POEM algorithm is in Supplementary Figure 1.


POEM: Identifying Joint Additive Effects on Regulatory Circuits.

Botzman M, Nachshon A, Brodt A, Gat-Viks I - Front Genet (2016)

Overview of the POEM algorithm. POEM takes as input a collection of expression traits from a certain population of genotyped individuals. The procedure is initiated with a non-conditioned scan (top right). The analysis then consists of two iterative stages: learning primary eQTLs after conditioning on the secondary eQTLs and vice versa (middle). The two steps are repeated k times. POEM relies on grouping of the expression traits based on their co-association to the primary and secondary eQTLs. Significant overlaps between the resulting primary and secondary groups are referred to as “poeModules” (bottom). Such poeModules are interpreted as promising pairwise effects that act on the same group of traits.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4835676&req=5

Figure 1: Overview of the POEM algorithm. POEM takes as input a collection of expression traits from a certain population of genotyped individuals. The procedure is initiated with a non-conditioned scan (top right). The analysis then consists of two iterative stages: learning primary eQTLs after conditioning on the secondary eQTLs and vice versa (middle). The two steps are repeated k times. POEM relies on grouping of the expression traits based on their co-association to the primary and secondary eQTLs. Significant overlaps between the resulting primary and secondary groups are referred to as “poeModules” (bottom). Such poeModules are interpreted as promising pairwise effects that act on the same group of traits.
Mentions: POEM identifies pairwise effects using the RBSR approach (see above) and further extends RBSR in two ways (Figure 1). First, we expect that grouping of traits can enhance the identification of eQTLs. In accordance, POEM constructs the map of eQTLs on the basis of co-association groups that were generated by the InVamod algorithm. Secondly, a major challenge may arise from an erroneous identification of each eQTL in the presence of the confounding effect of another eQTL, since the pairwise effect may lead to an increased marginal variance. For example, in the RBSR method, the scan for the primary eQTLs is conducted without removing the confounding effect of the secondary eQTLs, and this may blur the primary signals. Consequently, the scan for the secondary eQTLs may also be blurred since it relies on the former inaccuracies in the primary eQTLs. To tackle this challenge, we iterate between two steps. In stage 1—learning primary eQTLs—POEM applies a 1-locus scan that is conditioned on the secondary eQTL map, thereby providing a refined collection of primary eQTLs. In stage 2—learning secondary eQTLs—POEM applies a 1-locus scan that is conditioned on the primary eQTL map, thus providing a refined collection of secondary eQTLs. Both steps involve grouping of the traits to ensure the robustness of the learned model. We initiate the process with a standard 1-locus scan and then repeat the iterative process k times. POEM is implemented in Perl and is publicly available in csgi.tau.ac.il/POEM/. An outline of the POEM algorithm is in Supplementary Figure 1.

Bottom Line: POEM is specifically designed to achieve high performance in the case of additive joint effects.Our study reveals widespread additive, trans-acting pairwise effects on gene modules, characterizes their organizational principles, and highlights high-order interconnections between modules within the immune signaling network.These analyses elucidate the central role of additive pairwise effect in regulatory circuits, and provide computational tools for future investigations into the interplay between eQTLs.

View Article: PubMed Central - PubMed

Affiliation: Department of Cell Research and Immunology, The George S. Wise Faculty of Life Sciences, Tel Aviv University Tel Aviv, Israel.

ABSTRACT

Motivation: Expression Quantitative Trait Locus (eQTL) mapping tackles the problem of identifying variation in DNA sequence that have an effect on the transcriptional regulatory network. Major computational efforts are aimed at characterizing the joint effects of several eQTLs acting in concert to govern the expression of the same genes. Yet, progress toward a comprehensive prediction of such joint effects is limited. For example, existing eQTL methods commonly discover interacting loci affecting the expression levels of a module of co-regulated genes. Such "modularization" approaches, however, are focused on epistatic relations and thus have limited utility for the case of additive (non-epistatic) effects.

Results: Here we present POEM (Pairwise effect On Expression Modules), a methodology for identifying pairwise eQTL effects on gene modules. POEM is specifically designed to achieve high performance in the case of additive joint effects. We applied POEM to transcription profiles measured in bone marrow-derived dendritic cells across a population of genotyped mice. Our study reveals widespread additive, trans-acting pairwise effects on gene modules, characterizes their organizational principles, and highlights high-order interconnections between modules within the immune signaling network. These analyses elucidate the central role of additive pairwise effect in regulatory circuits, and provide computational tools for future investigations into the interplay between eQTLs.

Availability: The software described in this article is available at csgi.tau.ac.il/POEM/.

No MeSH data available.