A multiple comparison procedure for dose-finding trials with subpopulations

Identifying subgroups of patients with an enhanced response to a new treatment has become an area of increased interest in the last few years. When there is knowledge about possible subpopulations with an enhanced treatment effect before the start of a trial it might be beneficial to set up a testing strategy, which tests for a significant treatment effect not only in the full population, but also in these prespecified subpopulations. In this paper, we present a parametric multiple testing approach for tests in multiple populations for dose-finding trials. Our approach is based on the MCP-Mod methodology, which uses multiple comparison procedures (MCPs) to test for a dose–response signal, while considering multiple possible candidate dose–response shapes. Our proposed methods allow for heteroscedastic error variances between populations and control the family-wise error rate over tests in multiple populations and for multiple candidate models. We show in simulations that the proposed multipopulation testing approaches can increase the power to detect a significant dose–response signal over the standard single-population MCP-Mod, when the specified subpopulation has an enhanced treatment effect.     

A testing framework for identifying susceptibility genes in the presence of epistasis

An efficient testing strategy called the "focused interaction testing framework" (FITF) was developed to identify susceptibility genes involved in epistatic interactions for case-control studies of candidate genes. In the FITF approach, likelihood-ratio tests are performed in stages that increase in the order of interaction considered. Joint tests of main effects and interactions are performed conditional on significant lower-order effects. A reduction in the number of tests performed is achieved by prescreening gene combinations with a goodness-of-fit chi2 statistic that depends on association among candidate genes in the pooled case-control group. Multiple testing is accounted for by controlling false-discovery rates. Simulation analysis demonstrated that the FITF approach is more powerful than marginal tests of candidate genes. FITF also outperformed multifactor dimensionality reduction when interactions involved additive, dominant, or recessive genes. In an application to asthma case-control data from the Children's Health Study, FITF identified a significant multilocus effect between the nicotinamide adenine dinucleotide (phosphate) reduced:quinone oxidoreductase gene (NQO1), myeloperoxidase gene (MPO), and catalase gene (CAT) (unadjusted P = .00026), three genes that are involved in the oxidative stress pathway. In an independent data set consisting primarily of African American and Asian American children, these three genes also showed a significant association with asthma status (P = .0008).     

Understanding the genetic architecture of a quantitative trait in gymnosperms by genotyping haploid megagametophytes

The gymnosperms are a group of plants characterized by a haploid female gametophyte (megagametophyte). With the function of bearing the female gametes and nourishing the developing embryo, the megagametophyte has provided a simple way to understand the genetics of gymnosperm species using biochemical or genetic markers. In this paper, a quantitative genetic approach is proposed to study the genetic architecture of a quantitative trait in gymnosperms by taking advantage of the megagametophyte and the concept of average effect of a gene. Average effect describes the value associated with an allele carried by an individual and transmitted to its offspring. Through the genetic dissection of the average effect and genetic variance associated with a gamete carrying candidate genes, this approach can provide estimates of basic population genetic parameters, such as additive, dominant and epistatic effects, allelic frequencies and linkage disequilibrium. The candidate genes, known through their major mutant phenotype, have been reported in gymnosperms. An example for a candidate gene affecting lignin biosynthesis was applied to demonstrate the statistical procedures of the approach and its advantage. The conditions upon which the approach can be effectively used are discussed. Received: 15 January 1999 / Accepted: 12 March 1999     

Species delimitation and geography

Despite the importance of the geographical arrangement of populations for the inference of species boundaries, only a few approaches that integrate spatial information into species delimitation have thus far been developed. Persistent differentiation of sympatric groups of individuals is the best criterion for species status. Species delimitation becomes more prone to error if allopatric metapopulations are considered because it is often difficult to assess whether observed differences between allopatric metapopulations would be sufficient to prevent the fusion of these metapopulations upon contact. We propose a novel approach for testing the hypothesis that the multilocus genetic distances between individuals or populations belonging to two different candidate species are not larger than expected based on their geographical distances and the relationship of genetic and geographical distances within the candidate species. A rejection of this null hypothesis is an argument for classifying the two studied candidate species as distinct species. Case studies show that the proposed tests are suitable to distinguish between intra‐ and interspecific differentiation. The regression approach proposed here is more appropriate for testing species hypotheses with regard to isolation by distance than (partial) Mantel tests. Our tests assume a linear relationship between genetic and (transformed) geographical distances. This assumption can be compromised by a high genetic variability within populations as found in a case study with microsatellite markers.     

Prediction of Candidate Primary Immunodeficiency Disease Genes Using a Support Vector Machine Learning Approach

Screening and early identification of primary immunodeficiency disease (PID) genes is a major challenge for physicians. Many resources have catalogued molecular alterations in known PID genes along with their associated clinical and immunological phenotypes. However, these resources do not assist in identifying candidate PID genes. We have recently developed a platform designated Resource of Asian PDIs, which hosts information pertaining to molecular alterations, protein–protein interaction networks, mouse studies and microarray gene expression profiling of all known PID genes. Using this resource as a discovery tool, we describe the development of an algorithm for prediction of candidate PID genes. Using a support vector machine learning approach, we have predicted 1442 candidate PID genes using 69 binary features of 148 known PID genes and 3162 non-PID genes as a training data set. The power of this approach is illustrated by the fact that six of the predicted genes have recently been experimentally confirmed to be PID genes. The remaining genes in this predicted data set represent attractive candidates for testing in patients where the etiology cannot be ascribed to any of the known PID genes.     

Proteomic evidence of a paedomorphic evolutionary process within a marine snail species: a strategy for adapting to extreme ecological conditions?

The exposed and sheltered ecotypes of the marine snail Littorina saxatilis from European rocky shores are considered a key model system to study adaptation and ecological speciation. Previous studies showed that two ecotypes (RB and SU) of this species in NW Spain have adapted differently to different shore levels and microhabitats. In order to understand how this divergent adaptive process has been accomplished, we followed a quantitative proteomic approach to investigate the proteome variation in a number of different biological factors, that is, ecotype, ontogeny and their interactions. This approach allowed testing the hypothesis that one of the ecotypes has evolved by paedomorphosis, and also whether or not the molecular mechanisms related to ecotype differentiation are set up in early developmental stages. Additionally, the identification of some candidate proteins using mass spectrometry provides some functional insights into these evolutionary processes. Results from this study provided evidence of higher ontogenetic differentiation at proteome level in the RB (metamorphic) than in SU (paedomorphic) ecotype that point to the possibility of juvenile stage retention in this latter ecotype. The level of protein expression (proteome) differences between ecotypes maintained nearly constant from late embryonic stages to adulthood, although some proteins involved in these changes considerably differed in embryonic compared to other ontogenetic stages. Paedomorphosis may be the evolutionary response of the SU ecotype of solving the trade‐off during sexually immaturity that is caused by the evolution of small size arising from adaptation to the wave‐exposed habitat. Some potential candidate genes of adaptation related to energetic metabolism have been identified, providing a promising baseline for future functional analyses.     

Identification of <Emphasis Type="BoldItalic">b</Emphasis>-/<Emphasis Type="BoldItalic">y</Emphasis>-ions in MS/MS spectra using a two stage neural network

Independent of the approach used, the ability to correctly interpret tandem MS data depends on the quality of the original spectra. Even in the case of the highest quality spectra, the majority of spectral peaks can not be reliably interpreted. The accuracy of sequencing algorithms can be improved by filtering out such 'noise' peaks. Preprocessing MS/MS spectra to select informative ion peaks increases accuracy and reduces the processing time. Intuitively, the mix of informative versus non-informative peaks has a direct effect on the quality and size of the resulting candidate peptide search space. As the number of selected peaks increases, the corresponding search space increases exponentially. If we select too few peaks then the ion-ladder interpretation of the spectrum will contain gaps that can only be explained by permutations of combinations of amino acids. This will result in a larger candidate peptide search space and poorer quality candidates. The dependency that peptide sequencing accuracy has on an initial peak selection regime makes this preprocessing step a crucial facet of any approach, whether de novo or not, to MS/MS spectra interpretation.We have developed a novel approach to address this problem. Our approach uses a staged neural network to model ion fragmentation patterns and estimate the posterior probability of each ion type. Our method improves upon other preprocessing techniques and shows a significant reduction in the search space for candidate peptides without sacrificing candidate peptide quality.     

A bayesian approach to improved estimation of causal effect predictiveness for a principal surrogate endpoint

Summary The literature on potential outcomes has shown that traditional methods for characterizing surrogate endpoints in clinical trials based only on observed quantities can fail to capture causal relationships between treatments, surrogates, and outcomes. Building on the potential-outcomes formulation of a principal surrogate, we introduce a Bayesian method to estimate the causal effect predictiveness (CEP) surface and quantify a candidate surrogate's utility for reliably predicting clinical outcomes. In considering the full joint distribution of all potentially observable quantities, our Bayesian approach has the following features. First, our approach illuminates implicit assumptions embedded in previously-used estimation strategies that have been shown to result in poor performance. Second, our approach provides tools for making explicit and scientifically-interpretable assumptions regarding associations about which observed data are not informative. Through simulations based on an HIV vaccine trial, we found that the Bayesian approach can produce estimates of the CEP surface with improved performance compared to previous methods. Third, our approach can extend principal-surrogate estimation beyond the previously considered setting of a vaccine trial where the candidate surrogate is constant in one arm of the study. We illustrate this extension through an application to an AIDS therapy trial where the candidate surrogate varies in both treatment arms.     

Joint modeling of linkage and association: identifying SNPs responsible for a linkage signal

Once genetic linkage has been identified for a complex disease, the next step is often association analysis, in which single-nucleotide polymorphisms (SNPs) within the linkage region are genotyped and tested for association with the disease. If a SNP shows evidence of association, it is useful to know whether the linkage result can be explained, in part or in full, by the candidate SNP. We propose a novel approach that quantifies the degree of linkage disequilibrium (LD) between the candidate SNP and the putative disease locus through joint modeling of linkage and association. We describe a simple likelihood of the marker data conditional on the trait data for a sample of affected sib pairs, with disease penetrances and disease-SNP haplotype frequencies as parameters. We estimate model parameters by maximum likelihood and propose two likelihood-ratio tests to characterize the relationship of the candidate SNP and the disease locus. The first test assesses whether the candidate SNP and the disease locus are in linkage equilibrium so that the SNP plays no causal role in the linkage signal. The second test assesses whether the candidate SNP and the disease locus are in complete LD so that the SNP or a marker in complete LD with it may account fully for the linkage signal. Our method also yields a genetic model that includes parameter estimates for disease-SNP haplotype frequencies and the degree of disease-SNP LD. Our method provides a new tool for detecting linkage and association and can be extended to study designs that include unaffected family members.     

Coreferentiality: a new method for the hypothesis-based analysis of phenotypes characterized by multivariate data

Many multifactorial biologic effects, particularly in the context of complex human diseases, are still poorly understood. At the same time, the systematic acquisition of multivariate data has become increasingly easy. The use of such data to analyze and model complex phenotypes, however, remains a challenge. Here, a new analytic approach is described, termed coreferentiality, together with an appropriate statistical test. Coreferentiality is the indirect relation of two variables of functional interest in respect to whether they parallel each other in their respective relatedness to multivariate reference data, which can be informative for a complex effect or phenotype. It is shown that the power of coreferentiality testing is comparable to multiple regression analysis, sufficient even when reference data are informative only to a relatively small extent of 2.5%, and clearly exceeding the power of simple bivariate correlation testing. Thus, coreferentiality testing uses the increased power of multivariate analysis, however, in order to address a more straightforward interpretable bivariate relatedness. Systematic application of this approach could substantially improve the analysis and modeling of complex phenotypes, particularly in the context of human study where addressing functional hypotheses by direct experimentation is often difficult.     

Making host specificity testing more efficient: Exploring the use of abridged test plant lists

Testing the specificity of candidate agents is a key component of risk analysis in weed biological control. This step is often time-consuming due to the numerous plant species that need to be tested under quarantine conditions in the invaded country of the weed species. Here, we examined whether an abridged phylogenetically based test list could be used in the weed's native range to quickly screen the host specificity of candidate agents. Ten plant species were used to test the host specificity of a promising candidate for the biological control of Sonchus oleraceus in Australia, the gall midge, Cystiphora sonchi. No-choice and choice tests were carried out in the native Mediterranean range of the midge. The results showed the midge has potential to threaten native Australian species, as those species showed high infestation levels in no-choice tests and produced significantly higher numbers of galls in choice tests. As a result of this approach, C. sonchi was rapidly discarded from the list of agents to be imported into Australian quarantines for further tests. This study demonstrates that testing a few key phylogenetically related species in the native range may save cost and effort in a weed biological control programme.     

Modelling epistatic effects of embryo and endosperm QTL on seed quality traits

Coordinated expression of embryo and endosperm tissues is required for proper seed development. The coordination among these two tissues is controlled by the interaction between multiple genes expressed in the embryo and endosperm genomes. In this article, we present a statistical model for testing whether quantitative trait loci (QTL) active in different genomes, diploid embryo and triploid endosperm, epistatically affect a trait expressed on the endosperm tissue. The maximum likelihood approach, implemented with the EM algorithm, was derived to provide the maximum likelihood estimates of the locations of embryo- and endosperm-specific QTL and their main effects and epistatic effects. This model was used in a real example for rice in which two QTL, one from the embryo genome and the other from the endosperm genome, exert a significant interaction effect on gel consistency on the endosperm. Our model has successfully detected Waxy, a candidate gene in the embryo genome known to regulate one of the major steps of amylose biosynthesis in the endosperm. This model will have great implications for agricultural and evolutionary genetic research.     

Interactive effect of two candidate genes in a disease: extension of the marker-association-segregation chi(2) method.

For elucidating the genetic component of multifactorial diseases, it is important to investigate the effect of several factors and the possible interaction between them. In particular, for many diseases it is interesting to study the interactive effect of two genes. In this context, the marker-association-segregation chi 2 method (MASC), initially proposed to detect the involvement of a candidate gene in multifactorial diseases, is developed here to investigate the involvement of two candidate genes and to model the joint effect of these two genes. In particular, it is possible to precisely determine whether the joint effect of both genes is multiplicative. This extension simultaneously uses information on two markers, one for each candidate gene, at both the population and the familial segregation level. We show here that there can be an important gai of power to detect the effect of a second gene in a disease when information is used simultaneously on two markers instead of studying each marker separately. This extension of MASC is then applied on a sample of insulin-dependent diabetes (IDD) families typed for the markers of two candidate regions: HLA and that of the insulin gene (INS). This analysis allows us to confirm the involvement of INS in IDD, and the best-fitting model is a multiplicative (noninteractive) effect of HLA and INS, with a biallelic locus for INS and a complementation model for HLA.     

Comparison of statistics for candidate-gene association studies using cases and parents.

Studies of association between candidate genes and disease can be designed to use cases with disease, and in place of nonrelated controls, their parents. The advantage of this design is the elimination of spurious differences due to ethnic differences between cases and nonrelated controls. However, several statistical methods of analysis have been proposed in the literature, and the choice of analysis is not always clear. We review some of the statistical methods currently developed and present two new statistical methods aimed at specific genetic hypotheses of dominance and recessivity of the candidate gene. These new methods can be more powerful than other current methods, as demonstrated by simulations. The basis of these new statistical methods is a likelihood approach. The advantage of the likelihood framework is that regression models can be developed to assess genotype-environment interactions, as well as the relative contribution that alleles at the candidate-gene locus make to the relative risk (RR) of disease. This latter development allows testing of (1) whether interactions between alleles exist, on the scale of log RR, and (2) whether alleles originating from the mother or father of a case impart different risks, i.e., genomic imprinting.     

A combined targeted/phenotypic approach for the identification of new antiangiogenics agents active on a zebrafish model: From in silico screening to cyclodextrin formulation

A combined targeted/phenotypic approach for the rapid identification of novel antiangiogenics with in vivo efficacy is herein reported. Considering the important role played by the tyrosine kinase c-Src in the regulation of tumour angiogenesis, we submitted our in-house library of c-Src inhibitors to a sequential screening approach: in silico screening on VEGFR2, in vitro screening on HUVEC cells, ADME profiling, formulation and in vivo testing on a zebrafish model. A promising antiangiogenic candidate able to interfere with the vascular growth of a zebrafish model at low micromolar concentration was thus identified.     

Spending degrees of freedom in a poor economy: A case study of building a sightability model for moose in northeastern Minnesota

Sightability models are binary logistic-regression models used to estimate and adjust for visibility bias in wildlife-population surveys. Like many models in wildlife and ecology, sightability models are typically developed from small observational datasets with many candidate predictors. Aggressive model-selection methods are often employed to choose a best model for prediction and effect estimation, despite evidence that such methods can lead to overfitting (i.e., selected models may describe random error or noise rather than true predictor–response curves) and poor predictive ability. We used moose (Alces alces) sightability data from northeastern Minnesota (2005–2007) as a case study to illustrate an alternative approach, which we refer to as degrees-of-freedom (df) spending: sample-size guidelines are used to determine an acceptable level of model complexity and then a pre-specified model is fit to the data and used for inference. For comparison, we also constructed sightability models using Akaike's Information Criterion (AIC) step-down procedures and model averaging (based on a small set of models developed using df-spending guidelines). We used bootstrap procedures to mimic the process of model fitting and prediction, and to compute an index of overfitting, expected predictive accuracy, and model-selection uncertainty. The index of overfitting increased 13% when the number of candidate predictors was increased from three to eight and a best model was selected using step-down procedures. Likewise, model-selection uncertainty increased when the number of candidate predictors increased. Model averaging (based on R = 30 models with 1–3 predictors) effectively shrunk regression coefficients toward zero and produced similar estimates of precision to our 3-df pre-specified model. As such, model averaging may help to guard against overfitting when too many predictors are considered (relative to available sample size). The set of candidate models will influence the extent to which coefficients are shrunk toward zero, which has implications for how one might apply model averaging to problems traditionally approached using variable-selection methods. We often recommend the df-spending approach in our consulting work because it is easy to implement and it naturally forces investigators to think carefully about their models and predictors. Nonetheless, similar concepts should apply whether one is fitting 1 model or using multi-model inference. For example, model-building decisions should consider the effective sample size, and potential predictors should be screened (without looking at their relationship to the response) for missing data, narrow distributions, collinearity, potentially overly influential observations, and measurement errors (e.g., via logical error checks). © 2011 The Wildlife Society.     

Inferences about the between-study variance in meta-analysis with normally distributed outcomes

This paper presents a new approach for confidence interval estimation of the between-study variance in meta-analysis with normally distributed responses based on the concepts of generalized variables. Simulation study shows that the coverage probabilities of the proposed confidence intervals are generally satisfactory. Moreover, the proposed approach can easily provide P -values for hypothesis testing. For meta-analysis of controlled clinical trials or epidemiological studies, within which the responses are normally distributed, the proposed approach is an ideal candidate for making inference about the between-study variance.     

Comparison of different approaches for dose response analysis

Characterizing an appropriate dose‐response relationship and identifying the right dose in a clinical trial are two main goals of early drug‐development. MCP‐Mod is one of the pioneer approaches developed within the last 10 years that combines the modeling techniques with multiple comparison procedures to address the above goals in clinical drug development. The MCP‐Mod approach begins with a set of potential dose‐response models, tests for a significant dose‐response effect (proof of concept, PoC) using multiple linear contrasts tests and selects the “best” model among those with a significant contrast test. A disadvantage of the method is that the parameter values of the candidate models need to be fixed a priori for the contrasts tests. This may lead to a loss in power and unreliable model selection. For this reason, several variations of the MCP‐Mod approach and a hierarchical model selection approach have been suggested where the parameter values need not be fixed in the proof of concept testing step and can be estimated after the model selection step. This paper provides a numerical comparison of the different MCP‐Mod variants and the hierarchical model selection approach with regard to their ability of detecting the dose‐response trend, their potential to select the correct model and their accuracy in estimating the dose response shape and minimum effective dose. Additionally, as one of the approaches is based on two‐sided model comparisons only, we make it more consistent with the common goals of a PoC study, by extending it to one‐sided comparisons between the constant and alternative candidate models in the proof of concept step.     

Global risk transformative prioritization for prostate cancer candidate genes in molecular networks

Understanding the pathogenesis of complex diseases is aided by precise identification of the genes responsible. Many computational methods have been developed to prioritize candidate disease genes, but coverage of functional annotations may be a limiting factor for most of these methods. Here, we introduce a global candidate gene prioritization approach that considers information about network properties in the human protein interaction network and risk transformative contents from known disease genes. Global risk transformative scores were then used to prioritize candidate genes. This method was introduced to prioritize candidate genes for prostate cancer. The effectiveness of our global risk transformative algorithm for prioritizing candidate genes was evaluated according to validation studies. Compared with ToppGene and random walk-based methods, our method outperformed the two other candidate gene prioritization methods. The generality of our method was assessed by testing it on prostate cancer and other types of cancer. The performance was evaluated using standard leave-one-out cross-validation.     

Dose-response and thresholds in mutagenicity studies: a statistical testing approach

The analysis of dose-response relationships is an important objective in toxicology, and one in which both modelling and testing approaches are used. One particular question is whether a threshold exists at low doses. The concept of a pragmatic threshold is used, i.e. low doses with biologically unimportant effects are assumed to be threshold doses. "Biologically unimportant" means, in statistical terms, a lower effect than the effect of the negative control, or at least a just-tolerable margin delta higher than the effect of the negative control. Therefore, threshold doses can be tested in terms of a one-sided hypothesis of equivalence. A new approach is proposed, assuming, at the least, that the low dose is a threshold dose, and the highest dose is superior to the negative control. By analogy to the k-fold rule commonly used in mutagenicity studies, tests on ratio-to-control are used. The a priori definition of the threshold margin is inherently needed. A further approach proposes the analysis of dose-response relationships by means of order-restricted inference (the so-called trend test). A modification of a multiple-contrast test is used, in which only those contrasts are included that are sensitive for no effects at low doses. A further modification treats the complicated, but real, problem of simultaneous existence of a threshold, a monotonic increase, and a downturn effect at high dose(s). A parametric procedure is considered, together with an extension for proportions. The important problem of a priori sample size definition is discussed. The approaches are demonstrated by means of examples based on real data.