Identifying trait clusters by linkage profiles: application in genetical genomics

A genetical genomics approach to genome scans increases power for QTL mapping

We describe a method for integrating gene expression information into genome scans and show that this can substantially increase the statistical power of QTL mapping. The method has three stages. First, standard clustering methods identify small (size 5-20) groups of genes with similar expression patterns. Second, each gene group is tested for a causative genetic locus shared with the clinical trait of interest. This is done using an EM algorithm approach that treats genotype at the putative causative locus as an unobserved variable and combines expression information from all of the genes in the group to infer genotype information at the locus. Finally, expression QTL (eQTL) are mapped for each gene group that shares a causative locus with the clinical trait. Such eQTL are candidates for the causative locus. Simulation results show that this method has far superior power to standard QTL mapping techniques in many circumstances. We applied this method to existing data on mouse obesity. Our method identified 27 putative body weight QTL, whereas standard QTL mapping produced only one. Furthermore, most gene groups with body weight QTL included cis genes, so candidate genes could be immediately identified. Eleven body weight QTL produced 16 candidate genes that have been previously associated with body weight or body weight-related traits, thus validating our method. In addition, 15 of the 16 other loci produced 32 candidate genes that have not been associated with body weight. Thus, this method shows great promise for finding new causative loci for complex traits.

Toward the dynamic interactome: it's about time

Dynamic molecular interactions play a central role in regulating the functioning of cells and organisms. The availability of experimentally determined large-scale cellular networks, along with other high-throughput experimental data sets that provide snapshots of biological systems at different times and conditions, is increasingly helpful in elucidating interaction dynamics. Here we review the beginnings of a new subfield within computational biology, one focused on the global inference and analysis of the dynamic interactome. This burgeoning research area, which entails a shift from static to dynamic network analysis, promises to be a major step forward in our ability to model and reason about cellular function and behavior.

Transcript profiling during fiber development identifies pathways in secondary metabolism and cell wall structure that may contribute to cotton fiber quality

A global gene expression profiling study at different stages of fiber development was undertaken on two cotton species cultivated for fiber, Gossypium hirsutum (L.) and G. barbadense (L.). A large proportion of the genome was expressed during both fiber elongation and subsequent secondary cell wall thickening. There was a major shift in abundance of transcripts for gene regulation, cell organization and metabolism between fiber elongation and fiber thickening that was fundamentally similar in both species. Each stage had its own distinctive features represented by specific metabolic and regulatory genes, a number of which have been noted previously. Many of the genes expressed in the fibers were of a similar type and developmental expression to those seen in other fiber-producing plants, indicating a conservation of mechanisms of cell elongation and wall thickening across diverse plant genera. Secondary metabolism and pectin synthesis and modification genes were amongst the most statistically significant differentially expressed categories between the two species during fiber elongation. The gene profiles of the fiber thickening stage, however, were almost identical between the two species, suggesting that their different final fiber quality properties may be established at earlier stages of fiber development. Expression levels of representative phenylpropanoid and pectin modification genes showed high correlations with specific fiber properties in an inter-specific cotton recombinant inbred line (RIL) population, supporting a role in determining fiber quality.