Genotype matrix mapping: searching for quantitative trait loci interactions in genetic variation in complex traits.

In order to reveal quantitative trait loci (QTL) interactions and the relationship between various interactions in complex traits, we have developed a new QTL mapping approach, named genotype matrix mapping (GMM), which searches for QTL interactions in genetic variation. The central approach in GMM is the following. (1) Each tested marker is given a virtual matrix, named a genotype matrix (GM), containing intersecting lines and rows equal to the total allele number for that marker in the population analyzed. (2) QTL interactions are then estimated and compared through virtual networks among the GMs. To evaluate the contribution of marker combinations to a quantitative phenotype, the GMM method divides the samples into two non-overlapping subclasses, S(0) and S(1); the former contains the samples that have a specific genotype pattern to be evaluated, and the latter contains samples that do not. Based on this division, the F-measure is calculated as an index of significance. With the GMM method, we extracted significant marker combinations consisting of one to three interacting markers. The results indicated there were multiple QTL interactions affecting the phenotype (flowering date). GMM will be a valuable approach to identify QTL interactions in genetic variation of a complex trait within a variety of organisms.     

Lipid mediated gene delivery in the adult rat brain: quantitative analysis of expression.

Gene transfer into the adult brain is potentially an attractive alternative to commonly employed transgenic approaches. DNA-lipid complexes have been used to obtain brain gene transfer, but data are sparse to indicate to what extent this results in significant expression of functional protein. Here, an expression construct encoding the functional reporter, chloramphenicol-acetyl-transferase (CAT), was complexed to a novel biodegradable lipid, and delivered into the rat brain. CAT-activity was assayed in tissue extracts to allow a precise quantitation of functional enzyme protein. Following bilateral intraventricular (i.c.v.) injection, robust enzyme activity was found in all brain regions studied, peaking at 4 weeks. Other routes of administration, e.g. intra-parenchymal injection or chronic infusion of complexes, resulted in marginal or no activity. Presence of CAT mRNA and plasmid DNA in tissue extracts was confirmed at 4 weeks post i.c.v. administration. In agreement with previous studies, labelled lipid-DNA complexes were mainly found in the ventricular ependyma. Present data support the feasibility of lipid mediated brain gene transfer, and outline some of its anatomical and temporal limitations.     

Cognitive neuroscience: searching for the bottleneck in the brain

People simply cannot do two things at once, as shown by research on the so-called psychological refractory period. A new neuroimaging study has now localized the response-selection bottleneck underlying the psychological refractory period to a frontoparietal network.     

A quantitative comparison of cell-type-specific microarray gene expression profiling methods in the mouse brain

Expression profiling of restricted neural populations using microarrays can facilitate neuronal classification and provide insight into the molecular bases of cellular phenotypes. Due to the formidable heterogeneity of intermixed cell types that make up the brain, isolating cell types prior to microarray processing poses steep technical challenges that have been met in various ways. These methodological differences have the potential to distort cell-type-specific gene expression profiles insofar as they may insufficiently filter out contaminating mRNAs or induce aberrant cellular responses not normally present in vivo. Thus we have compared the repeatability, susceptibility to contamination from off-target cell-types, and evidence for stress-responsive gene expression of five different purification methods--Laser Capture Microdissection (LCM), Translating Ribosome Affinity Purification (TRAP), Immunopanning (PAN), Fluorescence Activated Cell Sorting (FACS), and manual sorting of fluorescently labeled cells (Manual). We found that all methods obtained comparably high levels of repeatability, however, data from LCM and TRAP showed significantly higher levels of contamination than the other methods. While PAN samples showed higher activation of apoptosis-related, stress-related and immediate early genes, samples from FACS and Manual studies, which also require dissociated cells, did not. Given that TRAP targets actively translated mRNAs, whereas other methods target all transcribed mRNAs, observed differences may also reflect translational regulation.     

Dynamic searching in the brain

Cognitive functions rely on the extensive use of information stored in the brain, and the searching for the relevant information for solving some problem is a very complex task. Human cognition largely uses biological search engines, and we assume that to study cognitive function we need to understand the way these brain search engines work. The approach we favor is to study multi-modular network models, able to solve particular problems that involve searching for information. The building blocks of these multimodular networks are the context dependent memory models we have been using for almost 20 years. These models work by associating an output to the Kronecker product of an input and a context. Input, context and output are vectors that represent cognitive variables. Our models constitute a natural extension of the traditional linear associator. We show that coding the information in vectors that are processed through association matrices, allows for a direct contact between these memory models and some procedures that are now classical in the Information Retrieval field. One essential feature of context-dependent models is that they are based on the thematic packing of information, whereby each context points to a particular set of related concepts. The thematic packing can be extended to multimodular networks involving input-output contexts, in order to accomplish more complex tasks. Contexts act as passwords that elicit the appropriate memory to deal with a query. We also show toy versions of several ‘neuromimetic’ devices that solve cognitive tasks as diverse as decision making or word sense disambiguation. The functioning of these multimodular networks can be described as dynamical systems at the level of cognitive variables.     

Antagonistic pleiotropy for life-history traits at the gene expression level

Life-history trade-offs prevent different components of fitness from being maximized simultaneously. Although the existence of trade-offs has been clearly demonstrated, the 'classical' mechanism of adaptive resource allocation that should underlie them has recently received criticism. In this study, we explore the molecular mechanisms of life-history trade-offs by applying a quantitative genomic approach. Analysis of global gene expression in Drosophila melanogaster revealed 34 genes whose expression coincided with the genetic trade-off between larval survival and adult size. The joint expression of these candidate 'trade-off' genes explained 86.3% of the trade-off. Fourteen of these genes have known functions which suggest that the larval survival-adult size trade-off could be the result of resource allocation at the organismal level, but at the level of cellular metabolism the trade-off would reduce to a shift between energy metabolism versus protein biosynthesis, regulated by the RAS signalling pathway.     

signatureSearch: environment for gene expression signature searching and functional interpretation

signatureSearch is an R/Bioconductor package that integrates a suite of existing and novel algorithms into an analysis environment for gene expression signature (GES) searching combined with functional enrichment analysis (FEA) and visualization methods to facilitate the interpretation of the search results. In a typical GES search (GESS), a query GES is searched against a database of GESs obtained from large numbers of measurements, such as different genetic backgrounds, disease states and drug perturbations. Database matches sharing correlated signatures with the query indicate related cellular responses frequently governed by connected mechanisms, such as drugs mimicking the expression responses of a disease. To identify which processes are predominantly modulated in the GESS results, we developed specialized FEA methods combined with drug-target network visualization tools. The provided analysis tools are useful for studying the effects of genetic, chemical and environmental perturbations on biological systems, as well as searching single cell GES databases to identify novel network connections or cell types. The signatureSearch software is unique in that it provides access to an integrated environment for GESS/FEA routines that includes several novel search and enrichment methods, efficient data structures, and access to pre-built GES databases, and allowing users to work with custom databases.     

Associating complex traits with genetic variants: polygenic risk scores,pleiotropy and endophenotypes

Genetica - Genotype–phenotype causal modeling has evolved significantly since Johannsen’s and Wright’s original designs were published. The development of genomewide assays to...     

The expression of the c-fos gene in the brain of mice in the dynamic acquisition of defensive behavioral habits

Levels of c-fos mRNA expression in mouse cerebral cortex and hippocampus at different stages of footshock escape and avoidance learning were studied by Northern hydridization. In the first series of experiments a mouse was presented with 30 electric footshock daily in a chamber where it could escape from the floor by jumping on the safe platform attached to the wall. A large increase in c-fos mRNA level in the cerebral cortex and hippocampus was observed during the first day of training. Mice that were trained for 9 consecutive days and acquired a footshock escape reaction showed no elevation of c-fos expression in the brain as compared to the quiet control group. In the second series of experiments the levels of c-fos expression were compared in individual mice trained to avoid the footshock by jumping on the platform in response to an auditory conditioned stimulus. Mice which acquired avoidance behavior more rapidly had lower c-fos mRNA levels than slow learners. There was no such to difference between the corresponding yoked control groups which consisted of animals matched the rapid and slow learners by the number of footshocks received. It is concluded that achievement of adaptive results in the course of learning leads to a suppression of further c-fos induction by motivational excitation.     

GOfetcher: a database with complex searching facility for gene ontology

MOTIVATION: An important contribution to the Gene Ontology (GO) project is to develop tools that facilitate the creation, maintenance and use of ontologies. Several tools have been created for communicating and using the GO project. However, a limitation with most of these tools is that they suffer from lack of a comprehensive search facility. We developed a web application, GOfetcher, with a very comprehensive search facility for the GO project and a variety of output formats for the results. GOfetcher has three different levels for searching the GO: 'Quick Search', 'Advanced Search' and 'Upload Files' for searching. The application includes a unique search option which generates gene information given a nucleotide or protein accession number which can then be used in generating GO information. The output data in GOfetcher can be saved into several different formats; including spreadsheet, comma-separated values and the extensible markup language (XML) format. The database is available at http://mcbc.usm.edu/gofetcher/.     

Association of immune system gene polymorphisms with quantitative traits pathogenetically important for chronic virus hepatitis

The IL4 C(?590)T, IL4RA Ile50Val, and TNF G(?308)A polymorphisms were tested for association with quantitative traits important for chronic virus hepatitis, including the levels of IL4, IL10, IL12, TNF-α, fibronectin, collagenase, the proteinase inhibitor, macroglobulin, and free and protein-bound (PBO) oxyproline. Allele A of the TNF G(?308)A polymorphism was associated with a lower TNF-α production by mononuclear cells, a higher production of IL4 and IL12, and a lower PBO level. The genotype CT of the IL4 C(?590)T polymorphism was associated with a high PBO level.     

Radiation-induced chromosomal instability and gene expression profiling: searching for clues to initiation and perpetuation

Radiation-induced genomic instability (RIGI) manifests in the progeny of cells surviving ionizing radiation (IR), and can be measured using such endpoints as delayed mutation, micronuclei formation, and chromosomal instability. The frequency of RIGI is relatively high, exceeding the gene mutation rate of IR by orders of magnitude, leading to conjecture that a gene mutation is not the cause of the phenotype. We have started to explore whether differential gene expression patterns are associated with the instability phenotype, in order to shed light on its initiation and perpetuation. Using GM10115 human-hamster hybrid-derived chromosomally stable and radiation-induced unstable clones, gene expression patterns were analyzed using microarray analysis. Two methods were used to find differentially expressed genes, and all candidate genes identified by these methods were under-expressed relative to the chromosomally stable reference sample. Among this set differentially expressed genes identified were two candidates with a relationship to the ubiquitin/proteasome pathway. While follow-up gene expression analyses have confirmed the under-expression of these two genes in some of our chromosomally unstable clones, preliminary functional studies have been unable to demonstrate a link to instability. It is anticipated that as we apply this technology to the study of radiation-induced genomic instability, clues to its onset will be revealed, ultimately contributing to a greater understanding of the mechanisms of radiation carcinogenesis.     

A bayesian mixed regression based prediction of quantitative traits from molecular marker and gene expression data

Both molecular marker and gene expression data were considered alone as well as jointly to serve as additive predictors for two pathogen-activity-phenotypes in real recombinant inbred lines of soybean. For unobserved phenotype prediction, we used a bayesian hierarchical regression modeling, where the number of possible predictors in the model was controlled by different selection strategies tested. Our initial findings were submitted for DREAM5 (the 5th Dialogue on Reverse Engineering Assessment and Methods challenge) and were judged to be the best in sub-challenge B3 wherein both functional genomic and genetic data were used to predict the phenotypes. In this work we further improve upon this previous work by considering various predictor selection strategies and cross-validation was used to measure accuracy of in-data and out-data predictions. The results from various model choices indicate that for this data use of both data types (namely functional genomic and genetic) simultaneously improves out-data prediction accuracy. Adequate goodness-of-fit can be easily achieved with more complex models for both phenotypes, since the number of potential predictors is large and the sample size is not small. We also further studied gene-set enrichment (for continuous phenotype) in the biological process in question and chromosomal enrichment of the gene set. The methodological contribution of this paper is in exploration of variable selection techniques to alleviate the problem of over-fitting. Different strategies based on the nature of covariates were explored and all methods were implemented under the bayesian hierarchical modeling framework with indicator-based covariate selection. All the models based in careful variable selection procedure were found to produce significant results based on permutation test.     

Genome-wide eQTLs and heritability for gene expression traits in unrelated individuals

Background

While the possible sources underlying the so-called ‘missing heritability’ evident in current genome-wide association studies (GWAS) of complex traits have been actively pursued in recent years, resolving this mystery remains a challenging task. Studying heritability of genome-wide gene expression traits can shed light on the goal of understanding the relationship between phenotype and genotype. Here we used microarray gene expression measurements of lymphoblastoid cell lines and genome-wide SNP genotype data from 210 HapMap individuals to examine the heritability of gene expression traits.

Results

Heritability levels for expression of 10,720 genes were estimated by applying variance component model analyses and 1,043 expression quantitative loci (eQTLs) were detected. Our results indicate that gene expression traits display a bimodal distribution of heritability, one peak close to 0% and the other summit approaching 100%. Such a pattern of the within-population variability of gene expression heritability is common among different HapMap populations of unrelated individuals but different from that obtained in the CEU and YRI trio samples. Higher heritability levels are shown by housekeeping genes and genes associated with cis eQTLs. Both cis and trans eQTLs make comparable cumulative contributions to the heritability. Finally, we modelled gene-gene interactions (epistasis) for genes with multiple eQTLs and revealed that epistasis was not prevailing in all genes but made a substantial contribution in explaining total heritability for some genes analysed.

Conclusions

We utilised a mixed effect model analysis for estimating genetic components from population based samples. On basis of analyses of genome-wide gene expression from four HapMap populations, we demonstrated detailed exploitation of the distribution of genetic heritabilities for expression traits from different populations, and highlighted the importance of studying interaction at the gene expression level as an important source of variation underlying missing heritability.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-15-13) contains supplementary material, which is available to authorized users.