Does psi exist? In a recent article, Dr. Bem conducted nine studies with over a thousand participants in an attempt to demonstrate that future events retroactively affect people’s responses. Here we discuss several limitations of Bem’s experiments on psi; in particular, we show that the data analysis was partly exploratory, and that one-sided p-values may overstate the statistical evidence against the null hypothesis. We reanalyze Bem’s data using a default Bayesian t-test and show that the evidence for psi is weak to nonexistent. We argue that in order to convince a skeptical audience of a controversial claim, one needs to conduct strictly confirmatory studies and analyze the results with statistical tests that are conservative rather than liberal. We conclude that Bem’s p-values do not indicate evidence in favor of precognition; instead, they indicate that experimental psychologists need to change the way they conduct their experiments and analyze their data.

Abstract not found

Understanding the genotype-phenotype relationship is at the core of the life sciences. For the latter half of the twentieth century, the reductionist approaches of genetics, biochemistry and molecular biology focused on the elucidation of biological components that underlie this fundamental relationship. These approaches have provided detailed understanding of individual components, but they do not address the systemic inter actions of biological and environmental components that underlie phenotypes. Technological advances have now enabled high-throughput methods to comprehensively characterize biological components simultaneously. The cost of such data generation has decreased exponentially and the amount of data generated has become more abundant, which enables biologists to view and study cells as systems of interacting components. To cope with the rapidly growing number of highdimensional data sets, sophisticated data analysis methods are needed. Diverse approaches that range from stochastic kinetic models to statistical Bayesian networks have been applied, and each of these approaches has differing rationales and advantages . This matrix is the central component of a constraint-based model (CBM), which can be queried by an ever-growing set of modelling methods 3 (BOX 2). CBMs have been primarily built for metabolic networks, including multicellular metabolic interactions Foundational developments Constraint-based analysis has been applied to biochemical reaction networks for more than 25 years. To put these developments into context, we exhaustively searched the literature using Web of Knowledge to collect research articles that use CBMs for interpreting Constraint-based models predict metabolic and associated cellular functions Aarash Bordbar, Jonathan M. Monk, Zachary A. King and Bernhard O. Palsson Abstract | The prediction of cellular function from a genotype is a fundamental goal in biology. For metabolism, constraint-based modelling methods systematize biochemical, genetic and genomic knowledge into a mathematical framework that enables a mechanistic description of metabolic physiology. The use of constraint-based approaches has evolved over ~30 years, and an increasing number of studies have recently combined models with high-throughput data sets for prospective experimentation. These studies have led to validation of increasingly important and relevant biological predictions. As reviewed here, these recent successes have tangible implications in the fields of microbial evolution, interaction networks, genetic engineering and drug discovery.

Technological advances in genotyping have given rise to hypothesisbased association studies of increasing scope. As a result, the scientific hypotheses addressed by these studies have become more complex and more difficult to address using existing analytic methodologies. Obstacles to analysis include inference in the face of multiple comparisons, complications arising from correlations among the SNPs (single nucleotide polymorphisms), choice of their genetic parametrization and missing data. In this paper we present an efficient Bayesian model search strategy that searches over the space of genetic markers and their genetic parametrization. The resulting method for Multilevel Inference of SNP Associations, MISA, allows computation of multilevel posterior probabilities and Bayes factors at the global, gene and SNP level, with the prior distribution on SNP inclusion in the model providing an intrinsic multiplicity correction. We use simulated data sets to characterize MISA’s statistical power, and show that MISA has higher power to detect association than standard procedures. Using data from the North Carolina Ovarian Cancer Study (NCOCS), MISA identifies variants that were not identified by standard methods and have been externally “validated ” in independent studies. We examine sensitivity of the NCOCS results to prior choice and method for imputing missing data. MISA is available in an R package on CRAN. 1. Introduction. Recent

We consider the problem of statistical inference on unknown quantities structured as a multiway table. We show that such multiway tables are naturally formed by arranging regression coefficients in complex systems of linear models for association analysis. In genetics and genomics, the resulting two-way and three-way tables cover many important applications. Within the Bayesian hierarchical model framework, we define the structure of a multiway table through prior specification. Focusing on model comparison and selection, we derive analytic expressions of Bayes factors and their approximations and discuss their theoretical and computational properties. Finally, we demonstrate the strength of our approach using a genomic application of mapping tissue-specific eQTLs (expression quantitative loci). 1

We explore the use of generalized t priors on regression coefficients to help understand the nature of association signal within “hit regions ” of genome-wide association studies. The particular generalized t distribution we adopt is a Student distribution on the absolute value of its argument. For low degrees of freedom we show that the generalized t exhibits ‘sparsity-prior ’ properties with some attractive features over other common forms of sparse priors and includes the well known double-exponential distribution as the degrees of freedom tends to ∞. We pay particular attention to graphical representations of posterior statistics obtained from sparsity-path-analysis (SPA) where we sweep over the setting of the scale (shrinkage / precision) parameter in the prior to explore the space of posterior models obtained over a range of complexities, from very sparse models with all coefficient distributions heavily concentrated around zero, to models with diffuse priors and coefficients distributed around their maximum likelihood estimates. The SPA plots are akin to LASSO plots of maximum a posteriori (MAP) estimates but they characterise the complete marginal posterior distri-butions of the coefficients plotted as a function of the precision of the prior. Generating posterior distributions over a range of prior precisions is computationally challenging but naturally amenable to sequential Monte Carlo (SMC) algorithms indexed on the scale pa-rameter. We show how SMC simulation on graphic-processing-units (GPUs) provides very efficient inference for SPA. We also present a scale-mixture representation of the general-ized t prior that leads to an EM algorithm to obtain MAP estimates should only these be required. 1

Does psi exist? D. J. Bem (2011) conducted 9 studies with over 1,000 participants in an attempt to demonstrate that future events retroactively affect people’s responses. Here we discuss several limitations of Bem’s experiments on psi; in particular, we show that the data analysis was partly exploratory and that one-sided p values may overstate the statistical evidence against the null hypothesis. We reanalyze Bem’s data with a default Bayesian t test and show that the evidence for psi is weak to nonexistent. We argue that in order to convince a skeptical audience of a controversial claim, one needs to conduct strictly confirmatory studies and analyze the results with statistical tests that are conservative rather than liberal. We conclude that Bem’s p values do not indicate evidence in favor of precognition; instead, they indicate that experimental psychologists need to change the way they conduct their experiments and analyze their data.

Abstract not found

Abstract Despite their theoretical appeal, Bayesian methods for the assessment of poor effort and malingering are still rarely used in neuropsychological research and clinical diagnosis. In this article, we outline a novel and easy-to-use Bayesian latent group analysis of malingering whose goal is to identify participants displaying poor effort when tested. Our Bayesian approach also quantifies the confidence with which each participant is classified and estimates the base rates of malingering from the observed data. We implement our Bayesian approach and compare its utility in effort assessment to that of the classic below-chance criterion of symptom validity testing (SVT). In two experiments, we evaluate the accuracy of both a Bayesian latent group analysis and the below-chance criterion of SVT in recovering the membership of participants assigned to the malingering group. Experiment 1 uses a simulation research design, whereas Experiment 2 involves the differentiation of patients with a history of stroke from coached malingerers. In both experiments, sensitivity levels are high for the Bayesian method, but low for the below-chance criterion of SVT. Additionally, the Bayesian approach proves to be resistant to possible effects of coaching. We conclude that Bayesian latent group methods complement existing methods in making more informed choices about malingering.

Genetic mapping of complex diseases to date depends on variations inside or close to the genes that perturb their activities. A strong body of evidence suggests that changes in gene expression play a key role in complex diseases and that numerous loci perturb gene expression in trans. The information in trans variants, however, has largely been ignored in the current analysis paradigm. Here we present a statistical framework for genetic mapping by utilizing collective information in both cis and trans variants. We reason that for a disease-associated gene, any genetic variation that perturbs its expression is also likely to influence the disease risk. Thus, the expression quantitative trait loci (eQTL) of the gene, which constitute a unique ‘‘genetic signature,’ ’ should overlap significantly with the set of loci associated with the disease. We translate this idea into a computational algorithm (named Sherlock) to search for gene-disease associations from GWASs, taking advantage of independent eQTL data. Application of this strategy to Crohn disease and type 2 diabetes predicts a number of genes with possible disease roles, including several predictions supported by solid experimental evidence. Importantly, predicted genes are often implicated by multiple trans eQTL with moderate associations. These genes are far from any GWAS association signals and thus cannot be identified from the GWAS alone. Our approach allows analysis of association data from a new perspective and is applicable to any complex phenotype. It is readily generalizable to molecular traits other than gene expression, such as metabolites, noncoding RNAs, and epigenetic modifications.