title: High-confidence discovery of genetic network regulators in expression quantitative trait loci data.
abstract: Expression QTL (eQTL) studies involve the collection of microarray gene expression data and genetic marker data from segregating individuals in a population to search for genetic determinants of differential gene expression. Previous studies have found large numbers of trans-regulated genes (regulated by unlinked genetic loci) that link to a single locus or eQTL "hotspot," and it would be desirable to find the mechanism of coregulation for these gene groups. However, many difficulties exist with current network reconstruction algorithms such as low power and high computational cost. A common observation for biological networks is that they have a scale-free or power-law architecture. In such an architecture, highly influential nodes exist that have many connections to other nodes. If we assume that this type of architecture applies to genetic networks, then we can simplify the problem of genetic network reconstruction by focusing on discovery of the key regulatory genes at the top of the network. We introduce the concept of "shielding" in which a specific gene expression variable (the shielder) renders a set of other gene expression variables (the shielded genes) independent of the eQTL. We iteratively build networks from the eQTL to the shielder down using tests of conditional independence. We have proposed a novel test for controlling the shielder false-positive rate at a predetermined level by requiring a threshold number of shielded genes per shielder. Using simulation, we have demonstrated that we can control the shielder false-positive rate as well as obtain high shielder and edge specificity. In addition, we have shown our method to be robust to violation of the latent variable assumption, an important feature in the practical application of our method. We have applied our method to a yeast expression QTL data set in which microarray and marker data were collected from the progeny of a backcross of two species of Saccharomyces cerevisiae (Brem et al. 2002). Seven genetic networks have been discovered, and bioinformatic analysis of the discovered regulators and corresponding regulated genes has generated plausible hypotheses for mechanisms of regulation that can be tested in future experiments.
authors.affiliation: Department of Biostatistics, Section on Statistical Genetics, University of Alabama, Birmingham, Alabama 35294-0022, USA. cduarte@uab.edu
authors.forename: Christine W
authors.initials: CW
authors.lastname: Duarte
authors.forename: Zhao-Bang
authors.initials: ZB
authors.lastname: Zeng
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D002874:Chromosome Mapping; D019295:Computational Biology; D003198:Computer Simulation; D020869:Gene Expression Profiling / Q000706:statistics & numerical data; D053263:Gene Regulatory Networks; D005809:Genes, Regulator / Q000235:genetics*; D005819:Genetic Markers / Q000235:genetics; D008957:Models, Genetic; D040641:Quantitative Trait Loci / Q000235:genetics*; D012441:Saccharomyces cerevisiae / Q000235:genetics*
chemical_list: D005819:Genetic Markers