Figure 1.
The Data Simulation Scheme with Genetic and Network Constraints
(A) A segregating population (an F2 intercross in this case) is simulated using the QTL Cartographer software suite (Rqtl, Rcross, and Zmapqtl). The QTL model for a trait is defined using the Rqtl program, and the heritability of the QTL is defined using the Rcross program.
(B) The traits simulated by Rcross are used as the head nodes in the simulated network. The remaining traits are simulated based on the values of the head nodes according to the DAG structure and the set of conditional probability density functions associated with this structure.
(C) After traits for all nodes in the network are simulated, they are scanned for QTLs using the Zmapqtl program. The traits and the associated QTL are then input into the network reconstruction program.
Figure 2.
Reconstruction Accuracy Based on 100-Sample Datasets Generated Using Parameters Similar to BXD Data
All accuracies are based on directed graphs unless indicated otherwise.
(A) Accuracy of reconstructions with and without genetic information used as prior information.
(B) Accuracy of reconstructions for the top-layer subnetwork, as defined in the text.
Figure 3.
The Accuracy of Reconstruction of the Synthetic Network, Reconstructed with and without Genetic Information
The genetics information not only helps to infer the direction of the relationships between nodes (solid lines), but also increases the power to detect relationships when direction is ignored, as with the association networks (dashed lines).
Figure 4.
Reconstruction accuracy with the genetic (dotted and solid lines) and without the genetic (dashed lines) information, using varying numbers of samples, and based on an overall genetic signal similar to that found in the BXD network, but with weaker interactions (see text for details)
(A) Reconstruction accuracy for the entire network.
(B) Reconstruction accuracy for the subnetwork comprising only the top layer of the network. The dotted lines reflect reconstructions that utilized cis QTL information as the only source of genetic information, whereas the solid lines reflect reconstructions that utilized all available genetic information.
Figure 5.
Reconstruction accuracy with the genetic (dotted and solid lines, as described in the Figure 4 legend) and without the genetic (dashed lines) information, using varying numbers of samples, and based on reduced heritability and a weak overall correlation structure compared with what we observed in the BXD network (see text for details)
(A) Accuracies of networks reconstructed with and without genetic information.
(B) Accuracies of subnetworks consisting only of those nodes in the top layer of the network.
(C) Accuracies of networks in which a true edge was counted as correct if the corresponding nodes were connected either directly or by a path involving two edges in the reconstructed network. It is clear the genetic data significantly enhance reconstruction accuracy.