Microarray-based cancer prediction using single genes

Background

Although numerous methods of using microarray data analysis for cancer classification have been proposed, most utilize many genes to achieve accurate classification. This can hamper interpretability of the models and ease of translation to other assay platforms. We explored the use of single genes to construct classification models. We first identified the genes with the most powerful univariate class discrimination ability and then constructed simple classification rules for class prediction using the single genes.

Results

We applied our model development algorithm to eleven cancer gene expression datasets and compared classification accuracy to that for standard methods including Diagonal Linear Discriminant Analysis, k-Nearest Neighbor, Support Vector Machine and Random Forest. The single gene classifiers provided classification accuracy comparable to or better than those obtained by existing methods in most cases. We analyzed the factors that determined when simple single gene classification is effective and when more complex modeling is warranted.

Conclusions

For most of the datasets examined, the single-gene classification methods appear to work as well as more standard methods, suggesting that simple models could perform well in microarray-based cancer prediction.

A serum protein profile predictive of the resistance to neoadjuvant chemotherapy in advanced breast cancers

Prediction of the responses to neoadjuvant chemotherapy (NACT) can improve the treatment of patients with advanced breast cancer. Genes and proteins predictive of chemoresistance have been extensively studied in breast cancer tissues. However, noninvasive serum biomarkers capable of such prediction have been rarely exploited. Here, we performed profiling of N-glycosylated proteins in serum from fifteen advanced breast cancer patients (ten patients sensitive to and five patients resistant to NACT) to discover serum biomarkers of chemoresistance using a label-free liquid chromatography-tandem MS method. By performing a series of statistical analyses of the proteomic data, we selected thirteen biomarker candidates and tested their differential serum levels by Western blotting in 13 independent samples (eight patients sensitive to and five patients resistant to NACT). Among the candidates, we then selected the final set of six potential serum biomarkers (AHSG, APOB, C3, C9, CP, and ORM1) whose differential expression was confirmed in the independent samples. Finally, we demonstrated that a multivariate classification model using the six proteins could predict responses to NACT and further predict relapse-free survival of patients. In summary, global N-glycoproteome profile in serum revealed a protein pattern predictive of the responses to NACT, which can be further validated in large clinical studies.

Mixed Methods for the Interpretation of Longitudinal Gerontologic Data: Insights From Philosophical Hermeneutics

This article's main objective is to demonstrate that data analysis, including quantitative data analysis, is a process of interpretation involving basic hermeneutic principles that philosophers have identified in the interpretive process as applied to other, mainly literary, creations. Such principles include a version of the hermeneutic circle, an insistence on interpretive presuppositions, and a resistance to reducing the discovery of truth to the application of inductive methods. The importance of interpretation becomes especially evident when qualitative and quantitative methods are combined in a single clinical research project and when the data being analyzed are longitudinal. Study objectives will be accomplished by showing that three major hermeneutic principles make practical methodological contributions to an insightful, illustrative mixed methods analysis of a qualitative study of changes in functional disability over time embedded in the Precipitating Events Project-a major longitudinal, quantitative study of functional disability among older persons. Mixed methods, especially as shaped by hermeneutic insights such as the importance of empathetic understanding, are potentially valuable resources for scientific investigations of the experience of aging: a practical aim of this article is to articulate and demonstrate this contention.

Designing phenotyping studies for genetically engineered mice

A phenotyping study records physiologic or morphologic changes in an experimental animal resulting from an intervention. In mice, this intervention is most frequently genetic, but it may be any type of experimental manipulation. Accurate representation of the human condition under study is essential if the model is to yield useful conclusions. In this review, general approaches to the design of phenotyping studies are considered. These approaches take into account major sources of reduced model validity, such as unexpected phenotypic variation in mice, evolutionary divergence between mice and humans, unanticipated sources of variation, and common design errors. As poor design is the most common reason why studies fail to yield enduring results, emphasis is placed on reduction of bias, sampling, controlled study design, and appropriate statistical analysis.

Automatic quantification of muscle volumes in magnetic resonance imaging scans of the lower extremities

Muscle volume measurements are essential for an array of diseases ranging from peripheral arterial disease, muscular dystrophies, neurological conditions to sport injuries and aging. In the clinical setting, muscle volume is not routinely measured due to the lack of standardized ways for its repeatable quantification. In this paper, we present magnetic resonance muscle quantification (MRMQ), a method for the automatic quantification of thigh muscle volume in magnetic resonance imaging (MRI) scans. MRMQ integrates a thigh segmentation and nonuniform image gradient correction step, followed by feature extraction and classification. The classification step leverages prior probabilities, introducing prior knowledge to a maximum a posteriori classifier. MRMQ was validated on 344 slices taken from 60 MRI scans. Experiments for the fully automatic detection of muscle volume in MRI scans demonstrated an averaged accuracy, sensitivity and specificity for leave-one-out cross-validation of 88.3%, 93.6% and 87.2%, respectively.

Statistical analysis of genetic interactions

Many common human diseases and complex traits are highly heritable and influenced by multiple genetic and environmental factors. Although genome-wide association studies (GWAS) have successfully identified many disease-associated variants, these genetic variants explain only a small proportion of the heritability of most complex diseases. Genetic interactions (gene-gene and gene-environment) substantially contribute to complex traits and diseases and could be one of the main sources of the missing heritability. This paper provides an overview of the available statistical methods and related computer software for identifying genetic interactions in animal and plant experimental crosses and human genetic association studies. The main discussion falls under the three broad issues in statistical analysis of genetic interactions: the definition, detection and interpretation of genetic interactions. Recently developed methods based on modern techniques for high-dimensional data are reviewed, including penalized likelihood approaches and hierarchical models; the relationships between these methods are also discussed. I conclude this review by highlighting some areas of future research.

Response to antimalarial agents in cutaneous lupus erythematosus: a prospective analysis

OBJECTIVE

To demonstrate response to antimalarial agents in patients with cutaneous lupus erythematosus (CLE) using activity scores from the Cutaneous Lupus Erythematosus Disease Area and Severity Index, a validated outcome measure.

DESIGN

Prospective, longitudinal cohort study.

SETTING

University cutaneous autoimmune disease clinic.

PARTICIPANTS

A total of 128 patients with CLE who presented from January 2007 to July 2010 and had at least 2 visits with activity scores.

INTERVENTION

Administration of antimalarial agents.

MAIN OUTCOME MEASURES

Response was defined by a 4-point or 20% decrease in activity score. Response to initiation was determined by the difference between the scores before treatment and at the first visit at least 2 months after treatment. Response to continuation was determined by the difference between the scores at the first visit and the most recent visit while undergoing treatment.

RESULTS

Of 11 patients who initiated treatment with hydroxychloroquine, 55% were responders (n = 6), showing a decrease in median (interquartile range [IQR]) activity score from 8.0 (3.5-13.0) to 3.0 (1.8-7.3) (P = .03). Of 15 patients for whom hydroxychloroquine failed, 67% were responders to initiation of hydroxychloroquine-quinacrine therapy (n = 10), showing a decrease in median (IQR) activity score from 6.0 (4.8-8.3) to 3.0 (0.75-5.0) (P = .004). Nine of 21 patients who continued hydroxychloroquine treatment (43%), and 9 of 21 patients who continued hydroxychloroquine-quinacrine (43%) were responders, showing a decrease in median (IQR) activity score from 6.0 (1.5-9.5) to 1.0 (0.0-4.5) (P = .01) and 8.5 (4.25-17.5) to 5.0 (0.5-11.5) (P = .01), respectively.

CONCLUSIONS

The use of quinacrine with hydroxychloroquine is associated with response in patients for whom hydroxychloroquine monotherapy fails. Further reduction in disease activity can be associated with continuation of treatment with antimalarial agents.

A model selection method for nonlinear system identification based FMRI effective connectivity analysis

In this paper a model selection algorithm for a nonlinear system identification method is proposed to study functional magnetic resonance imaging (fMRI) effective connectivity. Unlike most other methods, this method does not need a pre-defined structure/model for effective connectivity analysis. Instead, it relies on selecting significant nonlinear or linear covariates for the differential equations to describe the mapping relationship between brain output (fMRI response) and input (experiment design). These covariates, as well as their coefficients, are estimated based on a least angle regression (LARS) method. In the implementation of the LARS method, Akaike's information criterion corrected (AICc) algorithm and the leave-one-out (LOO) cross-validation method were employed and compared for model selection. Simulation comparison between the dynamic causal model (DCM), nonlinear identification method, and model selection method for modelling the single-input-single-output (SISO) and multiple-input multiple-output (MIMO) systems were conducted. Results show that the LARS model selection method is faster than DCM and achieves a compact and economic nonlinear model simultaneously. To verify the efficacy of the proposed approach, an analysis of the dorsal and ventral visual pathway networks was carried out based on three real datasets. The results show that LARS can be used for model selection in an fMRI effective connectivity study with phase-encoded, standard block, and random block designs. It is also shown that the LOO cross-validation method for nonlinear model selection has less residual sum squares than the AICc algorithm for the study.

Nonparametric model validations for hidden Markov models with applications in financial econometrics

We address the nonparametric model validation problem for hidden Markov models with partially observable variables and hidden states. We achieve this goal by constructing a nonparametric simultaneous confidence envelope for transition density function of the observable variables and checking whether the parametric density estimate is contained within such an envelope. Our specification test procedure is motivated by a functional connection between the transition density of the observable variables and the Markov transition kernel of the hidden states. Our approach is applicable for continuous time diffusion models, stochastic volatility models, nonlinear time series models, and models with market microstructure noise.

Sequential Support Vector Regression with Embedded Entropy for SNP Selection and Disease Classification

Comprehensive evaluation of common genetic variations through association of SNP structure with common diseases on the genome-wide scale is currently a hot area in human genome research. For less costly and faster diagnostics, advanced computational approaches are needed to select the minimum SNPs with the highest prediction accuracy for common complex diseases. In this paper, we present a sequential support vector regression model with embedded entropy algorithm to deal with the redundancy for the selection of the SNPs that have best prediction performance of diseases. We implemented our proposed method for both SNP selection and disease classification, and applied it to simulation data sets and two real disease data sets. Results show that on the average, our proposed method outperforms the well known methods of Support Vector Machine Recursive Feature Elimination, logistic regression, CART, and logic regression based SNP selections for disease classification.

Introduction to the Special Issue on Propensity Score Methods in Behavioral Research

This issue includes six articles that present logic, methods, and models for causal analyses of observational data, in particular those based on propensity score (PS) methods. The articles include a general introduction to propensity score analysis (PSA), uses of PSA in mediation studies, issues involved in choosing covariates, challenges that often arise in PSA applications, hierarchical data issues and models, and an application in an educational testing context. In this editorial I briefly summarize each article and make a few recommendations that relate to future applications in this field: the first pertains to how propensity score (PS) work could profit by connecting it with stronger forms of randomized experiments, not just simple randomization; the second to how and why graphical methods could be used to greater advantage in PSA studies; then why it might be helpful to reconsider the meaning of the term "treatments" in observational studies and why conventional usage might be modified; and finally, to the distinction between retrospective and prospective approaches to observational study design, noting the advantages, when feasible, of the latter approach.

Homophily and Contagion Are Generically Confounded in Observational Social Network Studies

The authors consider processes on social networks that can potentially involve three factors: homophily, or the formation of social ties due to matching individual traits; social contagion, also known as social influence; and the causal effect of an individual's covariates on his or her behavior or other measurable responses. The authors show that generically, all of these are confounded with each other. Distinguishing them from one another requires strong assumptions on the parametrization of the social process or on the adequacy of the covariates used (or both). In particular the authors demonstrate, with simple examples, that asymmetries in regression coefficients cannot identify causal effects and that very simple models of imitation (a form of social contagion) can produce substantial correlations between an individual's enduring traits and his or her choices, even when there is no intrinsic affinity between them. The authors also suggest some possible constructive responses to these results.

Prediction of body mass index in mice using dense molecular markers and a regularized neural network

Bayesian regularization of artificial neural networks (BRANNs) were used to predict body mass index (BMI) in mice using single nucleotide polymorphism (SNP) markers. Data from 1896 animals with both phenotypic and genotypic (12 320 loci) information were used for the analysis. Missing genotypes were imputed based on estimated allelic frequencies, with no attempt to reconstruct haplotypes based on family information or linkage disequilibrium between markers. A feed-forward multilayer perceptron network consisting of a single output layer and one hidden layer was used. Training of the neural network was done using the Bayesian regularized backpropagation algorithm. When the number of neurons in the hidden layer was increased, the number of effective parameters, γ, increased up to a point and stabilized thereafter. A model with five neurons in the hidden layer produced a value of γ that saturated the data. In terms of predictive ability, a network with five neurons in the hidden layer attained the smallest error and highest correlation in the test data although differences among networks were negligible. Using inherent weight information of BRANN with different number of neurons in the hidden layer, it was observed that 17 SNPs had a larger impact on the network, indicating their possible relevance in prediction of BMI. It is concluded that BRANN may be at least as useful as other methods for high-dimensional genome-enabled prediction, with the advantage of its potential ability of capturing non-linear relationships, which may be useful in the study of quantitative traits under complex gene action.

The Longitudinal Interplay of Adolescents' Self-Esteem and Body Image: A Conditional Autoregressive Latent Trajectory Analysis

Self-esteem and body image are central to coping successfully with the developmental challenges of adolescence. However, the current knowledge surrounding self-esteem and body image is fraught with controversy. This study attempts to clarify some of them by addressing three questions: (1) Are the intraindividual developmental trajectories of self-esteem and body image stable across adolescence? (2) What is the direction of the relations between body image and self-esteem over time? (3) What is the role of gender, ethnicity, and pubertal development on those trajectories? This study relies on Autoregressive Latent Trajectory analyses based on data from a 4-year, 6-wave, prospective longitudinal study of 1,001 adolescents. Self-esteem and body image levels remained high and stable over time, although body image levels also tended to increase slightly. The results show that levels of self-esteem were positively influenced by levels of body image. However, these effects remained small and most of the observed associations were cross-sectional. Finally, the effects of pubertal development on body image and self-esteem levels were mostly limited to non-Caucasian females who appeared to benefit from more advanced pubertal development. Conversely, Caucasian females presented the lowest self-esteem and body image levels of all, although for them more advanced pubertal development levels were associated with a slight rise in body image over time.

Evaluating haplotype effects in case-control studies via penalized-likelihood approaches: prospective or retrospective analysis?

Penalized likelihood methods have become increasingly popular in recent years for evaluating haplotype-phenotype association in case-control studies. Although a retrospective likelihood is dictated by the sampling scheme, these penalized methods are typically built on prospective likelihoods due to their modeling simplicity and computational feasibility. It has been well documented that for unpenalized methods, prospective analyses of case-control data can be valid but less efficient than their retrospective counterparts when testing for association, and result in substantial bias when estimating the haplotype effects. For penalized methods, which combine effect estimation and testing in one step, the impact of using a prospective likelihood is not clear. In this work, we examine the consequences of ignoring the sampling scheme for haplotype-based penalized likelihood methods. Our results suggest that the impact of prospective analyses depends on (1) the underlying genetic mode and (2) the genetic model adopted in the analysis. When the correct genetic model is used, the difference between the two analyses is negligible for additive and slight for dominant haplotype effects. For recessive haplotype effects, the more appropriate retrospective likelihood clearly outperforms the prospective likelihood. If an additive model is incorrectly used, as the true underlying genetic mode is unknown a priori, both retrospective and prospective penalized methods suffer from a sizeable power loss and increase in bias. The impact of using the incorrect genetic model is much bigger on retrospective analyses than prospective analyses, and results in comparable performances for both methods. An application of these methods to the Genetic Analysis Workshop 15 rheumatoid arthritis data is provided.

An attempt to use ectoparasites as tags for habitat occupancy by small mammalian hosts in central Europe: effects of host gender, parasite taxon and season

OBJECTIVE

We used data on fleas and gamasid mites parasitic on 8 species of small mammals to test whether (a) species composition of ectoparasite infracommunities may be used to predict host habitat occupancy and (b) the accuracy of this prediction differs between ectoparasite taxa, host genders and seasons.

METHODS

We used a Random Forests algorithm that is based on the methodology of classification trees.

RESULTS

The accuracy of prediction of habitat occupancy was relatively low and varied substantially among host species. The combined rate of the correct prediction of host habitat occupancy from data on ectoparasites was significantly higher than 50%, albeit being relatively low. The accuracy of prediction (a) did not differ between male and female hosts when it was based on species composition of fleas in summer or of mites in summer and winter, (b) was significantly higher in male hosts than in female hosts when the winter data on fleas were used and (c) was significantly higher for flea than mite assemblages. The effect of season was found in mites but not in fleas with the accuracy of prediction being significantly higher in summer than in winter assemblages.

CONCLUSIONS

Ectoparasites appeared to be not especially useful as biological markers for distinguishing host populations in different habitats in temperate zones.

Blood profile of proteins and steroid hormones predicts weight change after weight loss with interactions of dietary protein level and glycemic index

Background

Weight regain after weight loss is common. In the Diogenes dietary intervention study, high protein and low glycemic index (GI) diet improved weight maintenance.

Objective

To identify blood predictors for weight change after weight loss following the dietary intervention within the Diogenes study.

Design

Blood samples were collected at baseline and after 8-week low caloric diet-induced weight loss from 48 women who continued to lose weight and 48 women who regained weight during subsequent 6-month dietary intervention period with 4 diets varying in protein and GI levels. Thirty-one proteins and 3 steroid hormones were measured.

Results

Angiotensin I converting enzyme (ACE) was the most important predictor. Its greater reduction during the 8-week weight loss was related to continued weight loss during the subsequent 6 months, identified by both Logistic Regression and Random Forests analyses. The prediction power of ACE was influenced by immunoproteins, particularly fibrinogen. Leptin, luteinizing hormone and some immunoproteins showed interactions with dietary protein level, while interleukin 8 showed interaction with GI level on the prediction of weight maintenance. A predictor panel of 15 variables enabled an optimal classification by Random Forests with an error rate of 24±1%. A logistic regression model with independent variables from 9 blood analytes had a prediction accuracy of 92%.

Conclusions

A selected panel of blood proteins/steroids can predict the weight change after weight loss. ACE may play an important role in weight maintenance. The interactions of blood factors with dietary components are important for personalized dietary advice after weight loss.

Registration

ClinicalTrials.gov NCT00390637

Application of machine learning methods to describe the effects of conjugated equine estrogens therapy on region-specific brain volumes

Use of conjugated equine estrogens (CEE) has been linked to smaller regional brain volumes in women aged ≥65 years; however, it is unknown whether this results in a broad-based characteristic pattern of effects. Structural magnetic resonance imaging was used to assess regional volumes of normal tissue and ischemic lesions among 513 women who had been enrolled in a randomized clinical trial of CEE therapy for an average of 6.6 years, beginning at ages 65-80 years. A multivariate pattern analysis, based on a machine learning technique that combined Random Forest and logistic regression with L(1) penalty, was applied to identify patterns among regional volumes associated with therapy and whether patterns discriminate between treatment groups. The multivariate pattern analysis detected smaller regional volumes of normal tissue within the limbic and temporal lobes among women that had been assigned to CEE therapy. Mean decrements ranged as high as 7% in the left entorhinal cortex and 5% in the left perirhinal cortex, which exceeded the effect sizes reported previously in frontal lobe and hippocampus. Overall accuracy of classification based on these patterns, however, was projected to be only 54.5%. Prescription of CEE therapy for an average of 6.6 years is associated with lower regional brain volumes, but it does not induce a characteristic spatial pattern of changes in brain volumes of sufficient magnitude to discriminate users and nonusers.

A hierarchical Bayesian network approach for linkage disequilibrium modeling and data-dimensionality reduction prior to genome-wide association studies

Background

Discovering the genetic basis of common genetic diseases in the human genome represents a public health issue. However, the dimensionality of the genetic data (up to 1 million genetic markers) and its complexity make the statistical analysis a challenging task.

Results

We present an accurate modeling of dependences between genetic markers, based on a forest of hierarchical latent class models which is a particular class of probabilistic graphical models. This model offers an adapted framework to deal with the fuzzy nature of linkage disequilibrium blocks. In addition, the data dimensionality can be reduced through the latent variables of the model which synthesize the information borne by genetic markers. In order to tackle the learning of both forest structure and probability distributions, a generic algorithm has been proposed. A first implementation of our algorithm has been shown to be tractable on benchmarks describing 10⁵ variables for 2000 individuals.

Conclusions

The forest of hierarchical latent class models offers several advantages for genome-wide association studies: accurate modeling of linkage disequilibrium, flexible data dimensionality reduction and biological meaning borne by latent variables.

The use of classification trees for bioinformatics

Classification trees are non-parametric statistical learning methods that incorporate feature selection and interactions, possess intuitive interpretability, are efficient, and have high prediction accuracy when used in ensembles. This paper provides a brief introduction to the classification tree-based methods, a review of the recent developments, and a survey of the applications in bioinformatics and statistical genetics.

Improving health care outcomes through personalized comparisons of treatment effectiveness based on electronic health records

Comparative effectiveness research (CER) is one of the Patient Protection and Affordable Care Act's significant initiatives that aims to improve treatment outcomes and lower health care costs. This article takes CER a step further and suggests a novel clinical application for it. The article proposes the development of a national framework to enable physicians to rapidly perform, through a computerized service, medically sound personalized comparisons of the effectiveness of possible treatments for patients' conditions. A treatment comparison for a given patient would be based on data from electronic health records of a cohort of clinically similar patients who received the treatments previously and whose outcomes were recorded. This framework has unique potential to simultaneously improve the quality of health care, reduce its cost, and alleviate public concerns about rationing and "one size fits all" medicine.

Racial disparities in infant mortality: what has birth weight got to do with it and how large is it?

BACKGROUND

It has been hypothesized that birth weight is not on the causal pathway to infant mortality, at least among "normal" births (i.e. those located in the central part of the birth weight distribution), and that US racial disparities (African American versus European American) may be underestimated. Here these hypotheses are tested by examining the role of birth weight on racial disparities in infant mortality.

METHODS

A two-component Covariate Density Defined mixture of logistic regressions model is used to decompose racial disparities, 1) into disparities due to "normal" versus "compromised" components of the birth cohort, and 2) further decompose these components into indirect effects, which are associated with birth weight, versus direct effects, which are independent of birth weight.

RESULTS

The results indicate that a direct effect is responsible for the racial disparity in mortality among "normal" births. No indirect effect of birth weight is observed despite significant disparities in birth weight. Among "compromised" births, an indirect effect is responsible for the disparity, which is consistent with disparities in birth weight. However, there is also a direct effect among "compromised" births that reduces the racial disparity in mortality. This direct effect is responsible for the "pediatric paradox" and maybe due to differential fetal loss. Model-based adjustment for this effect indicates that racial disparities corrected for fetal loss could be as high as 3 or 4 fold. This estimate is higher than the observed racial disparities in infant mortality (2.1 for both sexes).

CONCLUSIONS

The results support the hypothesis that birth weight is not on the causal pathway to infant mortality among "normal" births, although birth weight could play a role among "compromised" births. The overall size of the US racial disparities in infant mortality maybe considerably underestimated in the observed data possibly due to racial disparities in fetal loss.

SNP selection in genome-wide and candidate gene studies via penalized logistic regression

Penalized regression methods offer an attractive alternative to single marker testing in genetic association analysis. Penalized regression methods shrink down to zero the coefficient of markers that have little apparent effect on the trait of interest, resulting in a parsimonious subset of what we hope are true pertinent predictors. Here we explore the performance of penalization in selecting SNPs as predictors in genetic association studies. The strength of the penalty can be chosen either to select a good predictive model (via methods such as computationally expensive cross validation), through maximum likelihood-based model selection criterion (such as the BIC), or to select a model that controls for type I error, as done here. We have investigated the performance of several penalized logistic regression approaches, simulating data under a variety of disease locus effect size and linkage disequilibrium patterns. We compared several penalties, including the elastic net, ridge, Lasso, MCP and the normal-exponential-γ shrinkage prior implemented in the hyperlasso software, to standard single locus analysis and simple forward stepwise regression. We examined how markers enter the model as penalties and P-value thresholds are varied, and report the sensitivity and specificity of each of the methods. Results show that penalized methods outperform single marker analysis, with the main difference being that penalized methods allow the simultaneous inclusion of a number of markers, and generally do not allow correlated variables to enter the model, producing a sparse model in which most of the identified explanatory markers are accounted for.

Mistreating Psychology in the Decades of the Brain

We systematically mistreat psychological phenomena, both logically and clinically. This article explores three contentions: that the dominant discourse in modern cognitive, affective, and clinical neuroscience assumes that we know how psychology/biology causation works when we do not; that there are serious intellectual, clinical, and policy costs to pretending we do know; and that crucial scientific and clinical progress will be stymied as long as we frame psychology, biology, and their relationship in currently dominant ways. The arguments are developed with emphasis on misguided attempts to localize psychological function via neuroimaging, misunderstandings about the role of genetics in psychopathology, and untoward constraints on health-care policy and clinical service delivery. A particular challenge, articulated but not resolved in this article, is determining what constitutes adequate explanation in the relationship between psychology and biology.

Multivariate Hawkes process models of the occurrence of regulatory elements

BACKGROUND

A central question in molecular biology is how transcriptional regulatory elements (TREs) act in combination. Recent high-throughput data provide us with the location of multiple regulatory regions for multiple regulators, and thus with the possibility of analyzing the multivariate distribution of the occurrences of these TREs along the genome.

RESULTS

We present a model of TRE occurrences known as the Hawkes process. We illustrate the use of this model by analyzing two different publically available data sets. We are able to model, in detail, how the occurrence of one TRE is affected by the occurrences of others, and we can test a range of natural hypotheses about the dependencies among the TRE occurrences. In contrast to earlier efforts, pre-processing steps such as clustering or binning are not needed, and we thus retain information about the dependencies among the TREs that is otherwise lost. For each of the two data sets we provide two results: first, a qualitative description of the dependencies among the occurrences of the TREs, and second, quantitative results on the favored or avoided distances between the different TREs.

CONCLUSIONS

The Hawkes process is a novel way of modeling the joint occurrences of multiple TREs along the genome that is capable of providing new insights into dependencies among elements involved in transcriptional regulation. The method is available as an R package from http://www.math.ku.dk/~richard/ppstat/.

Model-based multifactor dimensionality reduction for detecting epistasis in case-control data in the presence of noise

Analyzing the combined effects of genes and/or environmental factors on the development of complex diseases is a great challenge from both the statistical and computational perspective, even using a relatively small number of genetic and nongenetic exposures. Several data-mining methods have been proposed for interaction analysis, among them, the Multifactor Dimensionality Reduction Method (MDR) has proven its utility in a variety of theoretical and practical settings. Model-Based Multifactor Dimensionality Reduction (MB-MDR), a relatively new MDR-based technique that is able to unify the best of both nonparametric and parametric worlds, was developed to address some of the remaining concerns that go along with an MDR analysis. These include the restriction to univariate, dichotomous traits, the absence of flexible ways to adjust for lower order effects and important confounders, and the difficulty in highlighting epistatic effects when too many multilocus genotype cells are pooled into two new genotype groups. We investigate the empirical power of MB-MDR to detect gene-gene interactions in the absence of any noise and in the presence of genotyping error, missing data, phenocopy, and genetic heterogeneity. Power is generally higher for MB-MDR than for MDR, in particular in the presence of genetic heterogeneity, phenocopy, or low minor allele frequencies.

When t-tests or Wilcoxon-Mann-Whitney tests won't do

t-Tests are widely used by researchers to compare the average values of a numeric outcome between two groups. If there are doubts about the suitability of the data for the requirements of a t-test, most notably the distribution being non-normal, the Wilcoxon-Mann-Whitney test may be used instead. However, although often applied, both tests may be invalid when discrete and/or extremely skew data are analyzed. In medicine, extremely skewed data having an excess of zeroes are often observed, representing a numeric outcome that does not occur for a large percentage of cases (so is often zero) but which also sometimes takes relatively large values. For data such as this, application of the t-test or Wilcoxon-Mann-Whitney test could lead researchers to draw incorrect conclusions. A valid alternative is regression modeling to quantify the characteristics of the data. The increased availability of software has simplified the application of these more complex statistical analyses and hence facilitates researchers to use them. In this article, we illustrate the methodology applied to a comparison of cyst counts taken from control and steroid-treated fetal mouse kidneys.

A general model for multilocus epistatic interactions in case-control studies

Background

Epistasis, i.e., the interaction of alleles at different loci, is thought to play a central role in the formation and progression of complex diseases. The complexity of disease expression should arise from a complex network of epistatic interactions involving multiple genes.

Methodology

We develop a general model for testing high-order epistatic interactions for a complex disease in a case-control study. We incorporate the quantitative genetic theory of high-order epistasis into the setting of cases and controls sampled from a natural population. The new model allows the identification and testing of epistasis and its various genetic components.

Conclusions

Simulation studies were used to examine the power and false positive rates of the model under different sampling strategies. The model was used to detect epistasis in a case-control study of inflammatory bowel disease, in which five SNPs at a candidate gene were typed, leading to the identification of a significant three-locus epistasis.

Causal inference in perception

Until recently, the question of how the brain performs causal inference has been studied primarily in the context of cognitive reasoning. However, this problem is at least equally crucial in perceptual processing. At any given moment, the perceptual system receives multiple sensory signals within and across modalities and, for example, has to determine the source of each of these signals. Recently, a growing number of studies from various fields of cognitive science have started to address this question and have converged to very similar computational models. Therefore, it seems that a common computational strategy, which is highly consistent with a normative model of causal inference, is exploited by the perceptual system in a variety of domains.