A statistical test for detecting parent-of-origin effects when parental information is missing.

Genomic imprinting is an epigenetic mechanism that leads to differential contributions of maternal and paternal alleles to offspring gene expression in a parent-of-origin manner. We propose a novel test for detecting the parent-of-origin effects (POEs) in genome wide genotype data from related individuals (twins) when the parental origin cannot be inferred. The proposed method exploits a finite mixture of linear mixed models: the key idea is that in the case of POEs the population can be clustered in two different groups in which the reference allele is inherited by a different parent. A further advantage of this approach is the possibility to obtain an estimation of parental effect when the parental information is missing. We will also show that the approach is flexible enough to be applicable to the general scenario of independent data. The performance of the proposed test is evaluated through a wide simulation study. The method is finally applied to known imprinted genes of the MuTHER twin study data.

Mixture model with multiple allocations for clustering spatially correlated observations in the analysis of ChIP-Seq data.

Model-based clustering is a technique widely used to group a collection of units into mutually exclusive groups. There are, however, situations in which an observation could in principle belong to more than one cluster. In the context of next-generation sequencing (NGS) experiments, for example, the signal observed in the data might be produced by two (or more) different biological processes operating together and a gene could participate in both (or all) of them. We propose a novel approach to cluster NGS discrete data, coming from a ChIP-Seq experiment, with a mixture model, allowing each unit to belong potentially to more than one group: these multiple allocation clusters can be flexibly defined via a function combining the features of the original groups without introducing new parameters. The formulation naturally gives rise to a 'zero-inflation group' in which values close to zero can be allocated, acting as a correction for the abundance of zeros that manifest in this type of data. We take into account the spatial dependency between observations, which is described through a latent conditional autoregressive process that can reflect different dependency patterns. We assess the performance of our model within a simulation environment and then we apply it to ChIP-seq real data.

Modeling overdispersion heterogeneity in differential expression analysis using mixtures.

Next-generation sequencing technologies now constitute a method of choice to measure gene expression. Data to analyze are read counts, commonly modeled using negative binomial distributions. A relevant issue associated with this probabilistic framework is the reliable estimation of the overdispersion parameter, reinforced by the limited number of replicates generally observable for each gene. Many strategies have been proposed to estimate this parameter, but when differential analysis is the purpose, they often result in procedures based on plug-in estimates, and we show here that this discrepancy between the estimation framework and the testing framework can lead to uncontrolled type-I errors. Instead, we propose a mixture model that allows each gene to share information with other genes that exhibit similar variability. Three consistent statistical tests are developed for differential expression analysis. We show through a wide simulation study that the proposed method improves the sensitivity of detecting differentially expressed genes with respect to the common procedures, since it reaches the nominal value for the type-I error, while keeping elevate discriminative power between differentially and not differentially expressed genes. The method is finally illustrated on prostate cancer RNA-Seq data.

Spatio-temporal model for multiple ChIP-seq experiments.

The increasing availability of ChIP-seq data demands for advanced statistical tools to analyze the results of such experiments. The inherent features of high-throughput sequencing output call for a modelling framework that can account for the spatial dependency between neighboring regions of the genome and the temporal dimension that arises from observing the protein binding process at progressing time points; also, multiple biological/technical replicates of the experiment are usually produced and methods to jointly account for them are needed. Furthermore, the antibodies used in the experiment lead to potentially different immunoprecipitation efficiencies, which can affect the capability of distinguishing between the true signal in the data and the background noise. The statistical procedure proposed consist of a discrete mixture model with an underlying latent Markov random field: the novelty of the model is to allow both spatial and temporal dependency to play a role in determining the latent state of genomic regions involved in the protein binding process, while combining all the information of the replicates available instead of treating them separately. It is also possible to take into account the different antibodies used, in order to obtain better insights of the process and exploit all the biological information available.

The relationship between mind wandering and reading comprehension: A meta-analysis.

Mind wandering (MW), a shift of attention away from external tasks toward internally generated thoughts, has been frequently associated with costs in reading comprehension (RC), although with some contrasting results and many reported potential intervening factors. The aim of the meta-analysis was to evaluate the relationship between MW and RC, considering the role of participants' and text's characteristics, as well as methodological issues in the measurement of the two constructs. From a set of 25 selected full texts (73 correlation coefficients), pooled correlation (r = -0.21) revealed a negative significant relationship. Using trait-based questionnaires to assess MW compared with online probes resulted in an average significant change of 0.30 in the correlation between MW and RC, leading to a null correlation. A significant effect of age was also found, with more negative correlations with increasing age. None of the other moderating variables considered (i.e., language, text type, text length, RC assessment, text difficulty, text interest, and working memory) resulted in a significant effect. From the present meta-analysis, we might suggest that MW and RC are partially overlapping and vary, within a swing effect, in relation to a set of shared factors, such as working memory, interest, and text length. There might also be side-specific factors that drive the movement of primarily one side of the swing, and future research should further consider the role of individual differences in RC. Implications for research and educational settings are discussed.

Estimating Y-Str Mutation Rates and Tmrca Through Deep-Rooting Italian Pedigrees.

In the population genomics era, the study of Y-chromosome variability is still of the greatest interest for several fields ranging from molecular anthropology to forensics and genetic genealogy. In particular, mutation rates of Y-chromosomal Short Tandem Repeats markers (Y-STRs) are key parameters for different interdisciplinary applications. Among them, testing the patrilineal relatedness between individuals and calculating their Time of Most Recent Common Ancestors (TMRCAs) are of the utmost importance. To provide new valuable estimates and to address these issues, we typed 47 Y-STRs (comprising Yfiler, PowerPlex23 and YfilerPlus loci, the recently defined Rapidly Mutating [RM] panel and 11 additional markers often used in genetic genealogical applications) in 135 individuals belonging to 66 deep-rooting paternal genealogies from Northern Italy. Our results confirmed that the genealogy approach is an effective way to obtain reliable Y-STR mutation rate estimates even with a limited number of samples. Moreover, they showed that the impact of multi-step mutations and backmutations is negligible within the temporal scale usually adopted by forensic and genetic genealogy analyses. We then detected a significant association between the number of mutations within genealogies and observed TMRCAs. Therefore, we compared observed and expected TMRCAs by implementing a Bayesian procedure originally designed by Walsh (2001) and showed that the method yields a good performance (up to 96.72%), especially when using the Infinite Alleles Model (IAM).

Development and First Validation of a Disease Activity Score for Gout.

OBJECTIVE: To develop a new composite disease activity score for gout and provide its first validation. METHODS: Disease activity has been defined as the ongoing presence of urate deposits that lead to acute arthritis and joint damage. Every measure for each Outcome Measures in Rheumatology core domain was considered. A 3-step approach (factor analysis, linear discriminant analysis, and linear regression) was applied to derive the Gout Activity Score (GAS). Decision to change treatment or 6-month flare count were used as the surrogate criteria of high disease activity. Baseline and 12-month followup data of 446 patients included in the Kick-Off of the Italian Network for Gout cohort were used. Construct- and criterion-related validity were tested. External validation on an independent sample is reported. RESULTS: Factor analysis identified 5 factors: patient-reported outcomes, joint examination, flares, tophi, and serum uric acid (sUA). Discriminant function analysis resulted in a correct classification of 79%. Linear regression analysis identified a first candidate GAS including 12-month flare count, sUA, visual analog scale (VAS) of pain, VAS global activity assessment, swollen and tender joint counts, and a cumulative measure of tophi. Alternative scores were also developed. The developed GAS demonstrated a good correlation with functional disability (criterion validity) and discrimination between patient- and physician-reported measures of active disease (construct validity). The results were reproduced in the external sample. CONCLUSION: This study developed and validated a composite measure of disease activity in gout. Further testing is required to confirm its generalizability, responsiveness, and usefulness in assisting with clinical decisions.

Heterogeneity and symptom structure of schizophrenia.

Previous studies failed to identify a consistent factor structure of the BPRS-24 in schizophrenia. Our aims were to examine the fit of all previously published factor models and then to explore unobserved population heterogeneity and identify salient latent classes. Two hundred thirty-nine patients with ICD-10 schizophrenia admitted to a random sample of all Italian public and private acute inpatient units during an index period were administered the BPRS-24. Confirmatory factor analysis (CFA) was used to test all factor models derived in previous studies. Then, factor mixture analysis (FMA) with heteroscedastic components was carried out to explore unobserved population heterogeneity. No previously reported factor solution showed adequate fit in CFA. FMA indicated the presence of three heterogeneous groups and yielded a 5-factor solution (Depression, Positive Symptoms, Disorganization, Negative Symptoms, Activation). Group 1 was characterized by higher Disorganization, lower Activation, lower psychosocial functioning, greater lifetime number of admissions, more frequent history of compulsory admission. Group 2 displayed lower Disorganization. Group 3 showed higher Activation and more frequent history of recent self-harming behavior. Our finding that a reliable factor structure for the BPRS-24 could be obtained only after assuming population heterogeneity suggests that the difficulty in identifying a consistent factor structure may be ascribed to the clinical heterogeneity of schizophrenia. As compared with clinical subtypes, the psychopathological dimensions displayed much greater discriminatory power between groups identified by FMA. Though preliminary, our findings corroborate that a dimensional approach to psychopathology can facilitate the assessment of the clinical heterogeneity of schizophrenia.