Beatrijs Moerkerke

Identification of molecular markers linked with crown rust (Puccinia coronata f. sp. lolii) resistance in perennial ryegrass (Lolium perenne) using AFLP markers and a bulked segregant approach

Crown rust resistance is an important selection criterion in ryegrass breeding. The fungal disease caused by P. coronata causes yield loss and a reduced quality of the fodder crop. Molecular markers were used to unravel the genomic organization of crown rust resistance in a segregating L. perenne population. Two genomic regions involved in crown rust resistance were identified that together explained 35% of the phenotypic variance present. Bulked segregant analysis in combination with AFLP markers was a suitable method to identify DNA markers associated with genomic regions of major effect. One cluster of AFLP markers explained 6.1% of the variance and mapped to linkage group 2, a genomic region known to contain crown rust resistance genes. A second cluster of AFLP markers detected a novel genomic region of major effect that explained 27.7% of the phenotypic variance in crown rust resistance. This cluster was unlinked to the cluster on linkage group 2. Divergent selections performed within the segregating F1 population on the basis of genotype and phenotype revealed that the markers associated with crown rust resistance identified in this study have potential for marker assisted selection. Selection of plants on the basis of markers was more straightforward than the selection on the basis of phenotype.

A cautionary note on the power of the test for the indirect effect in mediation analysis

Recent simulation studies have pointed to the higher power of the test for the mediated effect vs. the test for the total effect, even in the presence of a direct effect. This has motivated applied researchers to investigate mediation in settings where there is no evidence of a total effect. In this paper we provide analytical insight into the circumstances under which higher power of the test for the mediated effect vs. the test for the total effect can be expected in the absence of a direct effect. We argue that the acclaimed power gain is somewhat deceptive and comes with a big price. On the basis of the results, we recommend that when the primary interest lies in mediation only, a significant test for the total effect should not be used as a prerequisite for the test for the indirect effect. However, because the test for the indirect effect is vulnerable to bias when common causes of mediator and outcome are not measured or not accounted for, it should be evaluated in a sensitivity analysis.

Genome-wide screening for cis-regulatory variation using a classical diallel crossing scheme

Large-scale screening studies carried out to date for genetic variants that affect gene regulation are generally limited to descriptions of differences in allele-specific expression (ASE) detected in vivo. Allele-specific differences in gene expression provide evidence for a model whereby cis-acting genetic variation results in differential expression between alleles. Such gene surveys for regulatory variation are a first step in identifying the specific nucleotide changes that govern gene expression differences, but they leave the underlying mechanisms unexplored. Here, we propose a quantitative genetics approach to perform a genome-wide analysis of ASE differences (GASED). The GASED approach is based on a diallel design that is often used in plant breeding programs to estimate general combining abilities (GCA) of specific inbred lines and to identify high-yielding hybrid combinations of parents based on their specific combining abilities (SCAs). In a context of gene expression, the values of GCA and SCA parameters allow cis- and trans-regulatory changes to be distinguished and imbalances in gene expression to be ascribed to cis-regulatory variation. With this approach, a total of 715 genes could be identified that are likely to carry allelic polymorphisms responsible for at least a 1.5-fold allelic expression difference in a total of 10 diploid Arabidopsis thaliana hybrids. The major strength of the GASED approach, compared to other ASE detection methods, is that it is not restricted to genes with allelic transcript variants. Although a false-positive rate of 9/41 was observed, the GASED approach is a valuable pre-screening method that can accelerate systematic surveys of naturally occurring cis-regulatory variation among inbred lines for laboratory species, such as Arabidopsis, mouse, rat and fruitfly, and economically important crop species, such as corn.

Mindful Attention and Awareness Mediate the Association Between Age and Negative Affect

OBJECTIVES Later life is often accompanied by experiences of loss and bereavement in several life domains. In spite of this, older adults experience less negative affect than their younger counterparts. Several explanations for this paradoxical finding have been put forward, but the mechanisms underlying the association between age and negative affect remain largely unclear. In the present study, we propose that mindfulness may be an important mediator of this association. METHOD A cross-sectional sample of 507 participants (age range 18-85 years) was used to investigate this question. Participants completed a range of self-report questionnaires on demographic variables, mindfulness, affect, quality of life (QoL), and personality. In our mediation analysis, we used an advanced statistical technique called G-estimation to control for the impact of confounding variables such as personality dimensions and QoL. RESULTS Our findings indicate that the age-related decrease in negative affect is mediated by mindfulness. The results remain significant when we control for QoL and personality. DISCUSSION These findings imply that mindfulness skills may be an important link between age and negative affect. Implications of these findings for the understanding of the well-being paradox are discussed.

Selecting "significant" differentially expressed genes from the combined perspective of the null and the alternative.

In the search for genes associated with disease, statistical analysis yields a key towards reproducible results. To avoid a plethora of type I errors, classical gene selection procedures strike a balance between magnitude and precision of observed effects in terms of p-values. Protecting false discovery rates recovers some power but still ranks genes according to classical p-values. In contrast, we propose a selection procedure driven by the concern to detect well-specified important alternatives. By summarizing evidence from the perspective of both the null and such an alternative hypothesis, genes line up in a substantially different order with different genes yielding powerful signals. A cutoff point for a measure of relative evidence which balances the standard p-value, p0, with its counterpart, p1, derived from the perspective of the target alternative, determines our gene selection. We find the cutoff point that maximizes an expected specific gain. This yields an optimal decision which exploits gene-specific variances and thus involves different type I and type II errors across genes. We show the dramatic impact of this alternative perspective on the detection of differentially expressed genes in hereditary breast cancer. Our analysis does not rely on parametric assumptions on the data.

A doubly robust test for gene-environment interaction in family-based studies of affected offspring

We develop a locally efficient test for (multiplicative) gene-environment interaction in family studies that collect genotypic information and environmental exposures for affected offspring along with genotypic information for their parents or relatives. The proposed test does not require modeling the effects of environmental exposures and is doubly robust in the sense of being valid if either a model for the main genetic effect holds or a model for the expected environmental exposure (given the offspring affection status and parental mating types) but not necessarily both. It extends the FBAT-I to allow for missing parental mating types and families of arbitrary size. Simulation studies and the analysis of an Alzheimers disease study confirm the adequate performance of the proposed test.

Power and sample size calculations for fMRI studies based on the prevalence of active peaks.

Highlights The manuscript presents a method to calculate sample sizes for fMRI experiments The power analysis is based on the estimation of the mixture distribution of null and active peaks The methodology is validated with simulated and real data. 1 Abstract Mounting evidence over the last few years suggest that published neuroscience research suffer from low power, and especially for published fMRI experiments. Not only does low power decrease the chance of detecting a true effect, it also reduces the chance that a statistically significant result indicates a true effect (Ioannidis, 2005). Put another way, findings with the least power will be the least reproducible, and thus a (prospective) power analysis is a critical component of any paper. In this work we present a simple way to characterize the spatial signal in a fMRI study with just two parameters, and a direct way to estimate these two parameters based on an existing study. Specifically, using just (1) the proportion of the brain activated and (2) the average effect size in activated brain regions, we can produce closed form power calculations for given sample size, brain volume and smoothness. This procedure allows one to minimize the cost of an fMRI experiment, while preserving a predefined statistical power. The method is evaluated and illustrated using simulations and real neuroimaging data from the Human Connectome Project. The procedures presented in this paper are made publicly available in an online web-based toolbox available at www.neuropowertools.org.

Structural equation modeling versus marginal structural modeling for assessing mediation in the presence of posttreatment confounding.

Inverse probability weighting for marginal structural models has been suggested as a strategy to estimate the direct effect of a treatment or exposure on an outcome in studies where the effect of mediator on outcome is subject to posttreatment confounding. This type of confounding, whereby confounders of the effect of mediator on outcome are themselves affected by the exposure, complicates mediation analyses and necessitates apt analysis strategies. In this article, we contrast the inverse probability weighting approach with the traditional path analysis approach to mediation analysis. We show that in a particular class of linear models, adjustment for posttreatment confounding can be realized via a fairly standard modification of the traditional path analysis approach. The resulting approach is simpler; by avoiding inverse probability weighting, it moreover results in direct effect estimators with smaller finite sample bias and greater precision. We further show that a particular variant of the G-estimation approach from the causal inference literature is equivalent with the path analysis approach in simple linear settings but is more generally applicable in settings with interactions and/or noncontinuous mediators and confounders. We conclude that the use of inverse probability weighting for marginal structural models to adjust for posttreatment confounding in mediation analysis is primarily indicated in nonlinear models for the outcome.

Flexible Mediation Analysis With Multiple Mediators

The advent of counterfactual-based mediation analysis has triggered enormous progress on how, and under what assumptions, one may disentangle path-specific effects upon combining arbitrary (possibly nonlinear) models for mediator and outcome. However, current developments have largely focused on single mediators because required identification assumptions prohibit simple extensions to settings with multiple mediators that may depend on one another. In this article, we propose a procedure for obtaining fine-grained decompositions that may still be recovered from observed data in such complex settings. We first show that existing analytical approaches target specific instances of a more general set of decompositions and may therefore fail to provide a comprehensive assessment of the processes that underpin cause-effect relationships between exposure and outcome. We then outline conditions for obtaining the remaining set of decompositions. Because the number of targeted decompositions increases rapidly with the number of mediators, we introduce natural effects models along with estimation methods that allow for flexible and parsimonious modeling. Our procedure can easily be implemented using off-the-shelf software and is illustrated using a reanalysis of the World Health Organizations Large Analysis and Review of European Housing and Health Status (WHO-LARES) study on the effect of mold exposure on mental health (2002-2003).

Bootstrapping fMRI Data: Dealing with Misspecification

The validity of inference based on the General Linear Model (GLM) for the analysis of functional magnetic resonance imaging (fMRI) time series has recently been questioned. Bootstrap procedures that partially avoid modeling assumptions may offer a welcome solution. We empirically compare two voxelwise GLM-based bootstrap approaches: a semi-parametric approach, relying solely on a model for the expected signal; and a fully parametric bootstrap approach, requiring an additional parameterization of the temporal structure. While the fully parametric approach assumes independent whitened residuals, the semi-parametric approach relies on independent blocks of residuals. The evaluation is based on inferential properties and the potential to reproduce important data characteristics. Different noise structures and data-generating mechanisms for the signal are simulated. When the model for the noise and expected signal is correct, we find that the fully parametric approach works well, with respect to both inference and reproduction of data characteristics. However, in the presence of misspecification, the fully parametric approach can be improved with additional blocking. The semi-parametric approach performs worse than the (fully) parametric approach with respect to inference but achieves comparable results as the parametric approach with additional blocking with respect to image reproducibility. We demonstrate that when the expected signal is incorrect GLM-based bootstrapping can overcome the poor performance of classical (non-bootstrap) parametric inference. We illustrate both approaches on a study exploring the neural representation of object representation in the visual pathway.