25,424 research outputs found
Application of new probabilistic graphical models in the genetic regulatory networks studies
This paper introduces two new probabilistic graphical models for
reconstruction of genetic regulatory networks using DNA microarray data. One is
an Independence Graph (IG) model with either a forward or a backward search
algorithm and the other one is a Gaussian Network (GN) model with a novel
greedy search method. The performances of both models were evaluated on four
MAPK pathways in yeast and three simulated data sets. Generally, an IG model
provides a sparse graph but a GN model produces a dense graph where more
information about gene-gene interactions is preserved. Additionally, we found
two key limitations in the prediction of genetic regulatory networks using DNA
microarray data, the first is the sufficiency of sample size and the second is
the complexity of network structures may not be captured without additional
data at the protein level. Those limitations are present in all prediction
methods which used only DNA microarray data.Comment: 38 pages, 3 figure
Genetic Influences on Brain Gene Expression in Rats Selected for Tameness and Aggression
Inter-individual differences in many behaviors are partly due to genetic
differences, but the identification of the genes and variants that influence
behavior remains challenging. Here, we studied an F2 intercross of two outbred
lines of rats selected for tame and aggressive behavior towards humans for more
than 64 generations. By using a mapping approach that is able to identify
genetic loci segregating within the lines, we identified four times more loci
influencing tameness and aggression than by an approach that assumes fixation
of causative alleles, suggesting that many causative loci were not driven to
fixation by the selection. We used RNA sequencing in 150 F2 animals to identify
hundreds of loci that influence brain gene expression. Several of these loci
colocalize with tameness loci and may reflect the same genetic variants.
Through analyses of correlations between allele effects on behavior and gene
expression, differential expression between the tame and aggressive rat
selection lines, and correlations between gene expression and tameness in F2
animals, we identify the genes Gltscr2, Lgi4, Zfp40 and Slc17a7 as candidate
contributors to the strikingly different behavior of the tame and aggressive
animals
How to understand the cell by breaking it: network analysis of gene perturbation screens
Modern high-throughput gene perturbation screens are key technologies at the
forefront of genetic research. Combined with rich phenotypic descriptors they
enable researchers to observe detailed cellular reactions to experimental
perturbations on a genome-wide scale. This review surveys the current
state-of-the-art in analyzing perturbation screens from a network point of
view. We describe approaches to make the step from the parts list to the wiring
diagram by using phenotypes for network inference and integrating them with
complementary data sources. The first part of the review describes methods to
analyze one- or low-dimensional phenotypes like viability or reporter activity;
the second part concentrates on high-dimensional phenotypes showing global
changes in cell morphology, transcriptome or proteome.Comment: Review based on ISMB 2009 tutorial; after two rounds of revisio
Genetic factors regulating lung vasculature and immune cell functions associate with resistance to pneumococcal infection
Streptococcus pneumoniae is an important human pathogen responsible for high mortality and morbidity worldwide. The susceptibility to pneumococcal infections is controlled by as yet unknown genetic factors. To elucidate these factors could help to develop new medical treatments and tools to identify those most at risk. In recent years genome wide association studies (GWAS) in mice and humans have proved successful in identification of causal genes involved in many complex diseases for example diabetes, systemic lupus or cholesterol metabolism. In this study a GWAS approach was used to map genetic loci associated with susceptibility to pneumococcal infection in 26 inbred mouse strains. As a result four candidate QTLs were identified on chromosomes 7, 13, 18 and 19. Interestingly, the QTL on chromosome 7 was located within S. pneumoniae resistance QTL (Spir1) identified previously in a linkage study of BALB/cOlaHsd and CBA/CaOlaHsd F2 intercrosses. We showed that only a limited number of genes encoded within the QTLs carried phenotype-associated polymorphisms (22 genes out of several hundred located within the QTLs). These candidate genes are known to regulate TGFb signalling, smooth muscle and immune cells functions. Interestingly, our pulmonary histopathology and gene expression data demonstrated, lung vasculature plays an important role in resistance to pneumococcal infection. Therefore we concluded that the cumulative effect of these candidate genes on vasculature and immune cells functions as contributory factors in the observed differences in susceptibility to pneumococcal infection. We also propose that TGFbmediated regulation of fibroblast differentiation plays an important role in development of invasive pneumococcal disease.This work was supported by the European Union-funded Pneumopath Project HEALTH-F3-2009-222983. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Peer-reviewedPublisher Versio
Detection of regulator genes and eQTLs in gene networks
Genetic differences between individuals associated to quantitative phenotypic
traits, including disease states, are usually found in non-coding genomic
regions. These genetic variants are often also associated to differences in
expression levels of nearby genes (they are "expression quantitative trait
loci" or eQTLs for short) and presumably play a gene regulatory role, affecting
the status of molecular networks of interacting genes, proteins and
metabolites. Computational systems biology approaches to reconstruct causal
gene networks from large-scale omics data have therefore become essential to
understand the structure of networks controlled by eQTLs together with other
regulatory genes, and to generate detailed hypotheses about the molecular
mechanisms that lead from genotype to phenotype. Here we review the main
analytical methods and softwares to identify eQTLs and their associated genes,
to reconstruct co-expression networks and modules, to reconstruct causal
Bayesian gene and module networks, and to validate predicted networks in
silico.Comment: minor revision with typos corrected; review article; 24 pages, 2
figure
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