Figure 1

Overview of the network approaches based on seed genes and differential expression. The gene network is constructed from STRING database and represented by an undirected graph consisting of nodes (genes) and weighed edges (links between gene pairs with associated scores). (a) For the seed-based strategy, the score vector for all seeds and other genes within the genome is initialized with the entries corresponding to the seed genes assigned with equal scores whose sum is equal to 1. The vector is iteratively updated by a random walk process over the network until it reaches convergence. Candidate genes are ranked by their scores in the converged vector, which can be interpreted as the steady-state probabilities of staying at the nodes representing the candidate genes. A high probability for the candidate corresponds to a higher similarity to the seeds. As a computationally efficient alternative, a heat kernel diffusion matrix can be used to approximate the distances between all pairs of genes. The candidate genes are then scored according to their average distances to the seeds based on the kernel matrix. (b) The DE-based method does not rely on the definition of seeds but uses a trait-related microarray expression profile to obtain the DE levels of the genes. DE levels were then mapped onto the network. For each candidate gene, the score is calculated as a weighted average of the DE levels of the gene and its network neighbors with the weights derived from the network distances between genes. In this study, the candidate genes within each QTL were scored using two different strategies, and the top 10% ranked by each method was chosen as prioritized genes (winners).