Analysis of sample set enrichment scores: assaying the enrichment of sets of genes for individual samples in genome-wide expression profiles

MOTIVATION: Gene expression profiling experiments in cell lines and animal models characterized by specific genetic or molecular perturbations have yielded sets of genes annotated by the perturbation. These gene sets can serve as a reference base for interrogating other expression datasets. For example, a new dataset in which a specific pathway gene set appears to be enriched, in terms of multiple genes in that set evidencing expression changes, can then be annotated by that reference pathway. We introduce in this paper a formal statistical method to measure the enrichment of each sample in an expression dataset. This allows us to assay the natural variation of pathway activity in observed gene expression data sets from clinical cancer and other studies. RESULTS: Validation of the method and illustrations of biological insights gleaned are demonstrated on cell line data, mouse models, and cancer-related datasets. Using oncogenic pathway signatures, we show that gene sets built from a model system are indeed enriched in the model system. We employ ASSESS for the use of molecular classification by pathways. This provides an accurate classifier that can be interpreted at the level of pathways instead of individual genes. Finally, ASSESS can be used for cross-platform expression models where data on the same type of cancer are integrated over different platforms into a space of enrichment scores. AVAILABILITY: Versions are available in Octave and Java (with a graphical user interface). Software can be downloaded at http://people.genome.duke.edu/assess.     

Sources of variance in baseline gene expression in the rodent liver

The use of gene expression profiling in both clinical and laboratory settings would be enhanced by better characterization of variation due to individual, environmental, and technical factors. Analysis of microarray data from untreated or vehicle-treated animals within the control arm of toxicogenomics studies has yielded useful information on baseline fluctuations in liver gene expression in the rodent. Here, studies which highlight contributions of different factors to gene expression variability in the rodent liver are discussed including a large meta-analysis of rat liver, which identified genes that vary in control animals in the absence of chemical treatment. Genes and their pathways that are the most and least variable were identified in a number of these studies. Life stage, fasting, sex, diet, circadian rhythm and liver lobe source can profoundly influence gene expression in the liver. Recognition of biological and technical factors that contribute to variability of background gene expression can help the investigator in the design of an experiment that maximizes sensitivity and reduces the influence of confounders that may lead to misinterpretation of genomic changes. The factors that contribute to variability in liver gene expression in rodents are likely analogous to those contributing to human interindividual variability in drug response and chemical toxicity. Identification of batteries of genes that are altered in a variety of background conditions could be used to predict responses to drugs and chemicals in appropriate models of the human liver.     

Temporal resolutions in species distribution models of highly mobile marine animals: Recommendations for ecologists and managers

While ecologists have long recognized the influence of spatial resolution on species distribution models (SDMs), they have given relatively little attention to the influence of temporal resolution. Considering temporal resolutions is critical in distribution modelling of highly mobile marine animals, as they interact with dynamic oceanographic processes that vary at time‐scales from seconds to decades. We guide ecologists in selecting temporal resolutions that best match ecological questions and ecosystems, and managers in applying these models. We group the temporal resolutions of environmental variables used in SDMs into three classes: instantaneous, contemporaneous and climatological. We posit that animal associations with fine‐scale and ephemeral features are best modelled with instantaneous covariates. Associations with large scale and persistent oceanographic features are best modelled with climatological covariates. Associations with mesoscale features are best modelled with instantaneous or contemporaneous covariates if ephemeral processes are present or interannual variability occurs, and climatological covariates if seasonal processes dominate and interannual variability is weak.     

Introduction of T cell receptor (TCR)-alpha cDNA has differential effects on TCR-gamma delta/CD3 expression by PEER and Lyon-1 cells

A TCR heterodimer composed of a TCR gamma-chain and a TCR delta-chain was found to be expressed in association with CD3 by a small population of human peripheral blood T cells, thymocytes, and certain leukemic T cell lines. The leukemic T cell lines PEER and Lyon-1 express such a TCR-gamma delta/CD3 complex at the cell surface. In addition, PEER and Lyon-1 cells transcribe a productively rearranged TCR-beta gene. Introduction of TCR alpha-chain cDNA of human or murine origin resulted in cell surface expression of a TCR-alpha beta/CD3 complex on PEER and Lyon-1 cells. The expression of the TCR-gamma delta/CD3 complex on PEER cells was not affected by introduction of TCR-alpha cDNA. In contrast, introduction of a TCR-alpha cDNA and expression of the TCR-alpha beta/CD3 complex in Lyon-1 cells resulted in the disappearance of the TCR-gamma delta/CD3 complex. These data were confirmed by indirect immunofluorescence, at the protein level and by gene expression analysis. Triggering of the TCR-alpha beta/CD3 complexes by anti-CD3 mAb or anti-TCR mAb resulted in increased internal Ca2+ levels, indicating that these receptors were functional in signal transduction. These results indicate that, besides TCR gene rearrangements, membrane expression of TCR-alpha beta heterodimers may be important in regulating TCR-gamma delta cell surface expression.     

Emergence of bimodal cell population responses from the interplay between analog single-cell signaling and protein expression noise

ABSTRACT: BACKGROUND: Cell-to-cell variability in protein expression can be large, and its propagation through signaling networks affects biological outcomes. Here, we apply deterministic and probabilistic models and biochemical measurements to study how network topologies and cell-to-cell protein abundance variations interact to shape signaling responses. RESULTS: We observe bimodal distributions of extracellular signal-regulated kinase (ERK) responses to epidermal growth factor (EGF) stimulation, which are generally thought to indicate bistable or ultrasensitive signaling behavior in single cells. Surprisingly, we find that a simple MAPK/ERK-cascade model with negative feedback that displays graded, analog ERK responses at a single cell level can explain the experimentally observed bimodality at the cell population level. Model analysis suggests that a conversion of graded input--output responses in single cells to digital responses at the population level is caused by a broad distribution of ERK pathway activation thresholds brought about by cell-to-cell variability in protein expression. CONCLUSIONS: Our results show that bimodal signaling response distributions do not necessarily imply digital (ultrasensitive or bistable) single cell signaling, and the interplay between protein expression noise and network topologies can bring about digital population responses from analog single cell dose responses. Thus, cells can retain the benefits of robustness arising from negative feedback, while simultaneously generating population-level on/off responses that are thought to be critical for regulating cell fate decisions.     

A theory of optimal differential gene expression

We investigate a model of optimal regulation, intended to describe large-scale differential gene expression. Relations between the optimal expression patterns and the function of genes are deduced from an optimality principle: the regulators have to maximise a fitness function which they influence directly via a cost term, and indirectly via their control on important cell variables, such as metabolic fluxes. According to the model, the optimal linear response to small perturbations reflects the regulators' functions, namely their linear influences on the cell variables. The optimal behaviour can be realised by a linear feedback mechanism. Known or assumed properties of response coefficients lead to predictions about regulation patterns. A symmetry relation predicted for deletion experiments is verified with gene expression data. Where the optimality assumption is valid, our results justify the use of expression data for functional annotation and for pathway reconstruction and suggest the use of linear factor models for the analysis of gene expression data.     

Identification of genes that influence gurken expression

Axial patterning in Drosophila relies on the deployment of patterning proteins at specific regions within the developing oocyte. This process involves transport of mRNAs from the nurse cells to the oocyte, localization of mRNAs within the oocyte, and translational regulation of these mRNAs to restrict the final distribution of the proteins. Despite extensive analysis, the events of deployment are not fully understood and it seems certain that many contributing factors remain to be identified. We describe the development and application of a sensitized genetic screen to reveal such additional factors. Overexpression of Imp, a factor implicated in regulation of gurken mRNA, causes a weak dorsalization that can be enhanced by reducing the level of other factors acting in the same pathway. A collection of deficiency mutants was screened using this assay, leading to the identification of 5 genes that are candidates to contribute to axial patterning. Three of the genes were characterized in greater detail. The mushroom body expressed gene was implicated in axial patterning, with overexpression leading to a range of patterning abnormalities that can be explained by a more primary defect in organization of the cytoskeleton. Two mitotic cell cycle control factors, cyclin E and E2f1, were also implicated, raising the possibility that a mitotic cell cycle checkpoint may impinge on grk expression, much as meiotic checkpoints can alter expression of this gene.     

Semiparametric regression of multidimensional genetic pathway data: least-squares kernel machines and linear mixed models

We consider a semiparametric regression model that relates a normal outcome to covariates and a genetic pathway, where the covariate effects are modeled parametrically and the pathway effect of multiple gene expressions is modeled parametrically or nonparametrically using least-squares kernel machines (LSKMs). This unified framework allows a flexible function for the joint effect of multiple genes within a pathway by specifying a kernel function and allows for the possibility that each gene expression effect might be nonlinear and the genes within the same pathway are likely to interact with each other in a complicated way. This semiparametric model also makes it possible to test for the overall genetic pathway effect. We show that the LSKM semiparametric regression can be formulated using a linear mixed model. Estimation and inference hence can proceed within the linear mixed model framework using standard mixed model software. Both the regression coefficients of the covariate effects and the LSKM estimator of the genetic pathway effect can be obtained using the best linear unbiased predictor in the corresponding linear mixed model formulation. The smoothing parameter and the kernel parameter can be estimated as variance components using restricted maximum likelihood. A score test is developed to test for the genetic pathway effect. Model/variable selection within the LSKM framework is discussed. The methods are illustrated using a prostate cancer data set and evaluated using simulations.     

Does higher concordance in monozygotic twins than in dizygotic twins suggest a genetic component?

It is widely regarded that twins can be used as a natural experiment to subject hypotheses to empirical testing regarding the contributions of genetic factors to phenotypic variability in human traits, especially behavioral traits. In genetic epidemiology, a higher concordance rate in monozygotic (MZ) twins than in dizygotic (DZ) twins is often taken as prima facie evidence for a genetic component. While twins studies have been used to estimate the contributions of genetic factors to phenotypic variability in human traits, the corresponding methodology that allows the estimation entails several crucial assumptions. The most critical is that MZ and DZ twins are equally similar environmentally. Although MZ twins are genetically more similar than DZ twins, they are often environmentally more similar. This paper demonstrates that, even in the complete absence of any genetic factor and of any biases, the greater environmental similarity alone in MZ twins can result in higher concordance rate in MZ twins than in DZ twins. This is especially true when there are multiple environmental factors, which may have multiple exposure levels and/or interact strongly, although each of them may be of low risk. This may serve as a sobering antidote to the uncritical reliance on twin studies without examining the validity of the underlying assumptions.     

Accommodating pathway information in expression quantitative trait locus analysis

The availability of high-throughput genotyping technologies and microarray assays has allowed researchers to consider pursuing investigations whose ultimate goal is the identification of genetic variations that influence levels of gene expression, e.g., "expression quantitative trait locus" or "eQTL" mapping studies. However, the large number of genes whose expression levels can be tested for association with genetic variations in such studies can create both statistical and biological interpretive problems. We consider the integrated analysis of eQTL mapping data that incorporates pathway, function, and disease process information. The goal of this analysis is to determine if compelling patterns emerge from the data that are consistent with the notion that perturbations in the molecular physiologic environment induced by genetic variations implicate the expression patterns of multiple genes via genetic network relationships or feedback mechanisms. We apply available genetic network and pathway analysis software, as well as a novel regression analysis technique, to carry out the proposed studies. We also consider extensions of the proposed strategies and areas of future research.     

Birnbaum–Saunders frailty regression models: Diagnostics and application to medical data

In survival models, some covariates affecting the lifetime could not be observed or measured. These covariates may correspond to environmental or genetic factors and be considered as a random effect related to a frailty of the individuals explaining their survival times. We propose a methodology based on a Birnbaum–Saunders frailty regression model, which can be applied to censored or uncensored data. Maximum‐likelihood methods are used to estimate the model parameters and to derive local influence techniques. Diagnostic tools are important in regression to detect anomalies, as departures from error assumptions and presence of outliers and influential cases. Normal curvatures for local influence under different perturbations are computed and two types of residuals are introduced. Two examples with uncensored and censored real‐world data illustrate the proposed methodology. Comparison with classical frailty models is carried out in these examples, which shows the superiority of the proposed model.     

Recent developments in statistical methods for detecting genetic loci affecting phenotypic variability

ABSTRACT: A number of recent works have introduced statistical methods for detecting genetic loci that affect phenotypic variability, which we refer to as variability-controlling quantitative trait loci (vQTL). These are genetic variants whose allelic state predicts how much phenotype values will vary about their expected means. Such loci are of great potential interest in both human and non-human genetic studies, one reason being that a detected vQTL could represent a previously undetected interaction with other genes or environmental factors. The simultaneous publication of these new methods in different journals has in many cases precluded opportunity for comparison. We survey some of these methods, the respective trade-offs they imply, and the connections between them. The methods fall into three main groups: classical non-parametric, fully parametric, and semi-parametric two-stage approximations. Choosing between alternatives involves balancing the need for robustness, flexibility, and speed. For each method, we identify important assumptions and limitations, including those of practical importance, such as their scope for including covariates and random effects. We show in simulations that both parametric methods and their semi-parametric approximations can give elevated false positive rates when they ignore mean-variance relationships intrinsic to the data generation process. We conclude that choice of method depends on the trait distribution, the need to include non-genetic covariates, and the population size and structure, coupled with a critical evaluation of how these fit with the assumptions of the statistical model.     

Strain-based genetic differences regulate the efficiency of systemic gene delivery as well as expression.

We have characterized the impact of strain-based genetic differences on the efficiency of the intravenous cationic liposome-DNA complex (CLDC)-based gene transfer and expression in mice. We also investigated what steps in the gene delivery and expression pathway appeared responsible for these strain-related differences and whether such differences could be compensated for either by agents that alter host pathways important in CLDC-mediated gene transfer and expression, or by changes in CLDC formulation. We found that different mouse strains can exhibit different expression levels and/or differences in the amount of plasmid DNA delivered to the organs where the DNA is expressed. Furthermore, drug pretreatment or reformulation of the CLDC could improve DNA delivery and/or gene expression in a strain-specific fashion. We conclude that genetic factors critically modify both the tissue deposition and the expression of genetic materials delivered by CLDC. Because manipulation of either the host or the CLDC could at least partially compensate for these strain-related differences, such strategies may be required to effectively use non-viral gene transfer approaches in genetically diverse populations.     

Comparison of Statistical Tests for Association between Rare Variants and Binary Traits

Genome-wide association studies have found thousands of common genetic variants associated with a wide variety of diseases and other complex traits. However, a large portion of the predicted genetic contribution to many traits remains unknown. One plausible explanation is that some of the missing variation is due to the effects of rare variants. Nonetheless, the statistical analysis of rare variants is challenging. A commonly used method is to contrast, within the same region (gene), the frequency of minor alleles at rare variants between cases and controls. However, this strategy is most useful under the assumption that the tested variants have similar effects. We previously proposed a method that can accommodate heterogeneous effects in the analysis of quantitative traits. Here we extend this method to include binary traits that can accommodate covariates. We use simulations for a variety of causal and covariate impact scenarios to compare the performance of the proposed method to standard logistic regression, C-alpha, SKAT, and EREC. We found that i) logistic regression methods perform well when the heterogeneity of the effects is not extreme and ii) SKAT and EREC have good performance under all tested scenarios but they can be computationally intensive. Consequently, it would be more computationally desirable to use a two-step strategy by (i) selecting promising genes by faster methods and ii) analyzing selected genes using SKAT/EREC. To select promising genes one can use (1) regression methods when effect heterogeneity is assumed to be low and the covariates explain a non-negligible part of trait variability, (2) C-alpha when heterogeneity is assumed to be large and covariates explain a small fraction of trait's variability and (3) the proposed trend and heterogeneity test when the heterogeneity is assumed to be non-trivial and the covariates explain a large fraction of trait variability.     

Analysis of melanoma onset: assessing familial aggregation by using estimating equations and fitting variance components via Bayesian random effects models.

We investigate whether relative contributions of genetic and shared environmental factors are associated with an increased risk in melanoma. Data from the Queensland Familial Melanoma Project comprising 15,907 subjects arising from 1912 families were analyzed to estimate the additive genetic, common and unique environmental contributions to variation in the age at onset of melanoma. Two complementary approaches for analyzing correlated time-to-onset family data were considered: the generalized estimating equations (GEE) method in which one can estimate relationship-specific dependence simultaneously with regression coefficients that describe the average population response to changing covariates; and a subject-specific Bayesian mixed model in which heterogeneity in regression parameters is explicitly modeled and the different components of variation may be estimated directly. The proportional hazards and Weibull models were utilized, as both produce natural frameworks for estimating relative risks while adjusting for simultaneous effects of other covariates. A simple Markov Chain Monte Carlo method for covariate imputation of missing data was used and the actual implementation of the Bayesian model was based on Gibbs sampling using the free ware package BUGS. In addition, we also used a Bayesian model to investigate the relative contribution of genetic and environmental effects on the expression of naevi and freckles, which are known risk factors for melanoma.     

Genome and stresses: Reactions against aggressions,behavior of transposable elements

The action of stresses on the genome can be considered as responses of cells or organisms to external aggressions. Stress factors are of environmental origin (climatic or trophic) or of genomic nature (introduction of foreign genetic material, for example). In both cases, important perturbations can occur and modify hereditary potentialities, creating new combinations compatible with survival; such a situation may increase the variability of the genome, and allow evolutive processes to take place. The behavior of transposable elements under stress conditions is thus of particular interest, since these sequences are sources of mutations and therefore of genetic variability; they may play an important role in population adaptation. The survey of the available experimental results suggests that, although some examples of mutations and transposable elements movements induced by external factors are clearly described, environmental injuries or introduction of foreign material into a genome are not systematically followed by drastic genomic changes.