The MVGC multivariate Granger causality toolbox: a new approach to Granger-causal inference

Background: Wiener-Granger causality (“G-causality”) is a statistical notion of causality applicable to time series data, whereby cause precedes, and helps predict, effect. It is defined in both time and frequency domains, and allows for the conditioning out of common causal influences. Originally developed in the context of econometric theory, it has since achieved broad application in the neurosciences and beyond. Prediction in the G-causality formalism is based on VAR (Vector AutoRegressive) modelling. New Method: The MVGC Matlab c Toolbox approach to G-causal inference is based on multiple equivalent representations of a VAR model by (i) regression parameters, (ii) the autocovariance sequence and (iii) the cross-power spectral density of the underlying process. It features a variety of algorithms for moving between these representations, enabling selection of the most suitable algorithms with regard to computational efficiency and numerical accuracy. Results: In this paper we explain the theoretical basis, computational strategy and application to empirical G-causal inference of the MVGC Toolbox. We also show via numerical simulations the advantages of our Toolbox over previous methods in terms of computational accuracy and statistical inference. Comparison with Existing Method(s): The standard method of computing G-causality involves estimation of parameters for both a full and a nested (reduced) VAR model. The MVGC approach, by contrast, avoids explicit estimation of the reduced model, thus eliminating a source of estimation error and improving statistical power, and in addition facilitates fast and accurate estimation of the computationally awkward case of conditional G-causality in the frequency domain. Conclusions: The MVGC Toolbox implements a flexible, powerful and efficient approach to G-causal inference. Keywords: Granger causality, vector autoregressive modelling, time series analysi

EXPLAINING INTERNATIONAL DIFFERENCES IN GENETICALLY MODIFIED FOOD LABELING REGULATIONS

Replaced with revised version of paper 07/13/04.Food Consumption/Nutrition/Food Safety,

Staphylococcal enterotoxin sensitization in a community-based population : a potential role in adult-onset asthma

Background: Recent studies suggest that Staphylococcus aureus enterotoxin sensitization is a risk factor for asthma. However, there is a paucity of epidemiologic evidence on adult-onset asthma in community-based populations. Objective: We sought to evaluate the epidemiology and the clinical significance of staphylococcal enterotoxin sensitization in community-based adult populations. Methods: The present analyses were performed using the baseline data set of Korean adult population surveys, consisting of 1080 adults (mean age=60.2years) recruited from an urban and a rural community. Questionnaires, methacholine challenge tests, and allergen skin tests were performed for defining clinical phenotypes. Sera were analysed for total IgE and enterotoxin-specific IgE using ImmunoCAP. Results: Staphylococcal enterotoxin sensitization (0.35kU/L) had a prevalence of 27.0%. Risk factors were identified as male sex, current smoking, advanced age (61years), and inhalant allergen sensitization. Current asthma was mostly adult onset (18years old) and showed independent associations with high enterotoxin-specific IgE levels in multivariate logistic regression tests. In multivariate linear regressions, staphylococcal enterotoxin-specific IgE level was identified as the major determinant factor for total IgE level. Conclusions and Clinical Relevance: Staphylococcal enterotoxin sensitization was independently associated with adult-onset asthma in adult community populations. Strong correlations between the enterotoxin-specific IgE and total IgE levels support the clinical significance. The present findings warrant further studies for the precise roles of staphylococcal enterotoxin sensitization in the asthma pathogenesis

Adoption of Best Management Practices to Control Weed Resistance By Cotton, Corn, and Soybean Growers

This study examined adoption of ten best management practices (BMPs) to control weed resistance to herbicides. Using data from a survey of 1,205 U.S. cotton, corn, and soybean growers, count data models were estimated to explain the total number of practices frequently adopted. Ordered probit regressions were used to explain the frequency of individual BMP adoption. Growers practicing a greater number of BMPs frequently (a) had more education, but less farming experience; (b) grew cotton, (c) expected higher yields relative to the county average; and (d) farmed in counties with a lower coefficient of variation (CV) for yield of their primary crop. Yield expectations and variability were significant predictors of the frequency of adoption of individual BMPs. Most growers frequently adopted the same seven BMPs. Extension efforts may be more effective if they target a minority of growers and the three practices with low adoption rates. Counties with a high yield CV would be areas to look for low BMP adoption.weeds, herbicide, resistance management, corn, cotton, soybeans, adoption, Crop Production/Industries, Farm Management, Production Economics, Research and Development/Tech Change/Emerging Technologies, Q12, Q16,

Efficient inference for genetic association studies with multiple outcomes

Combined inference for heterogeneous high-dimensional data is critical in modern biology, where clinical and various kinds of molecular data may be available from a single study. Classical genetic association studies regress a single clinical outcome on many genetic variants one by one, but there is an increasing demand for joint analysis of many molecular outcomes and genetic variants in order to unravel functional interactions. Unfortunately, most existing approaches to joint modelling are either too simplistic to be powerful or are impracticable for computational reasons. Inspired by Richardson et al. (2010, Bayesian Statistics 9), we consider a sparse multivariate regression model that allows simultaneous selection of predictors and associated responses. As Markov chain Monte Carlo (MCMC) inference on such models can be prohibitively slow when the number of genetic variants exceeds a few thousand, we propose a variational inference approach which produces posterior information very close to that of MCMC inference, at a much reduced computational cost. Extensive numerical experiments show that our approach outperforms popular variable selection methods and tailored Bayesian procedures, dealing within hours with problems involving hundreds of thousands of genetic variants and tens to hundreds of clinical or molecular outcomes

Individual differences and health in chronic pain: are sex-differences relevant?

Background: Because psychological variables are known to intercorrelate, the goal of this investigation was to compare the unique association between several well-established psychological constructs in pain research and pain-related outcomes. Sex differences are considered because pain is experienced differently across sex groups. Methods: Participants were 456 consecutive chronic pain patients attending a tertiary pain clinic (mean age = 58.4 years, SD = 14.8, 63.6% women). The study design was cross-sectional. Psychological constructs included personality (NEO-Five Factor Inventory), irrational thinking (General Attitudes and Beliefs Scale), and coping (Social Problem Solving Inventory). Outcomes were pain severity and interference (Brief Pain Inventory) and physical, general, and mental health status (Short Form-36). To decide whether the bivariate analyses and the two-block, multivariate linear regressions for each study outcome (block 1 = age, sex, and pain severity; block 2 = psychological variables) should be conducted with the whole sample or split by sex, we first explored whether sex moderated the relationship between psychological variables and outcomes. An alpha level of 0.001 was set to reduce the risk of type I errors due to multiple comparisons. Results: The moderation analyses indicated no sex differences in the association between psychological variables and study outcomes (all interaction terms p > .05). Thus, further analyses were calculated with the whole sample. Specifically, the bivariate analyses revealed that psychological constructs were intercorrelated in the expected direction and mostly correlated with mental health and overall perceived health status. In the regressions, when controlling for age, sex, and pain severity, psychological factors as a block significantly increased the explained variance of physical functioning (ΔR2 = .037, p < .001), general health (ΔR2 = .138, p < .001), and mental health (ΔR2 = .362, p < .001). However, unique associations were only obtained for mental health and neuroticism (β = − 0.30, p < .001) and a negative problem orientation (β = − 0.26, p < .001). Conclusions: There is redundancy in the relationship between psychological variables and pain-related outcomes and the strength of this association is highest for mental health status. The association between psychological characteristics and health outcomes was comparable for men and women, which suggests that the same therapeutic targets could be selected in psychological interventions of pain patients irrespective of sex

A Latent Variable Approach to Multivariate Quantitative Trait Loci

A novel approach based on latent variable modelling is presented for the analysis of multivariate quantitative and qualitative trait loci. The approach is general in the sense that it enables the joint analysis of many kinds of quantitative and qualitative traits (including count data and censored traits) in a single modelling framework. In the framework, the observations are modelled as functions of latent variables, which are then affected by quantitative trait loci. Separating the analysis in this way means that measurement errors in the phenotypic observations can be included easily in the model, providing robust inferences. The performance of the method is illustrated using two real multivariate datasets, from barley and Scots pine

Robust Sparse Canonical Correlation Analysis

Canonical correlation analysis (CCA) is a multivariate statistical method which describes the associations between two sets of variables. The objective is to find linear combinations of the variables in each data set having maximal correlation. This paper discusses a method for Robust Sparse CCA. Sparse estimation produces canonical vectors with some of their elements estimated as exactly zero. As such, their interpretability is improved. We also robustify the method such that it can cope with outliers in the data. To estimate the canonical vectors, we convert the CCA problem into an alternating regression framework, and use the sparse Least Trimmed Squares estimator. We illustrate the good performance of the Robust Sparse CCA method in several simulation studies and two real data examples

Mediation Analysis Supports a Causal Relationship between Maternal Hyperglycemia and Placental DNA Methylation Variations at the Leptin Gene Locus and Cord Blood Leptin Levels.

Changes in fetal DNA methylation (DNAm) of the leptin (LEP) gene have been associated with exposure to maternal hyperglycemia, but their links with childhood obesity risk are still unclear. We investigated the association between maternal hyperglycemia, placental LEP DNAm (25 5-C-phosphate-G-3 (CpG) sites), neonatal leptinemia, and adiposity (i.e., BMI and skinfold thickness (ST) (subscapular (SS) + triceps (TR) skinfold measures, and the ratio of SS:TR) at 3-years-old, in 259 mother-child dyads, from Gen3G birth cohort. We conducted multivariate linear analyses adjusted for gestational age at birth, sex of the child, age at follow-up, and cellular heterogeneity. We assessed the causal role of DNAm in the association between maternal glycemia and childhood outcomes, using mediation analysis. We found three CpGs associated with neonatal leptinemia (p ≤ 0.002). Of these, cg05136031 and cg15758240 were also associated with BMI (β = -2.69, p = 0.05) and fat distribution (β = -0.581, p = 0.05) at 3-years-old, respectively. Maternal glycemia was associated with DNAm at cg15758240 (β = -0.01, p = 0.04) and neonatal leptinemia (β = 0.19, p = 0.004). DNAm levels at cg15758240 mediates 0.8% of the association between maternal glycemia and neonatal leptinemia (p &lt; 0.001). Our results support that DNAm regulation of the leptin pathway in response to maternal glycemia might be involved in programming adiposity in childhood