SPARSE INFERENCE IN COVARIATE ADJUSTED CENSORED GAUSSIAN GRAPHICAL MODELS

The covariate adjusted glasso is one of the most used estimators for inferring genetic networks. Despite its diffusion, there are several fields in applied research where the limits of detection of modern measurement technologies make the use of this estimator theoretically unfounded, even when the assumption of a multivariate Gaussian distribution is satisfied. In this paper we propose an extension to censored data

Isolated Subtle Neurological Abnormalities in Mild Cognitive Impairment Types

Background: Isolated, subtle neurological abnormalities (ISNA) are commonly seen in aging and have been related to cerebral small vessel disease (SVD) and subcortical atrophy in neurologically and cognitively healthy aging subjects. Objective: To investigate the frequency of ISNA in different mild cognitive impairment (MCI) types and to evaluate for each MCI type, the crosssectional relation between ISNA and white matter hyperintensities (WMH), lacunes, caudate atrophy, and ventricular enlargement. Methods: One thousand two hundred fifty subjects with different MCI types were included in the analysis and underwent brain magnetic resonance imaging. WMHs were assessed through two visual rating scales. Lacunes were also rated. Atrophy of the caudate nuclei and ventricular enlargement were assessed through the bicaudate ratio (BCr) and the lateral ventricles to brain ratio (LVBr), respectively. Apolipoprotein E (APOE) genotypes were also assessed. The routine neurological examination was used to evaluate ISNAs that were clustered as central-based signs, cerebellar-based signs, and primitive reflexes. The items of Part-III of the Unified Parkinson’s Disease Rating Scale were used to evaluate ISNAs that were clustered as mild parkinsonian signs. Associations of ISNAs with imaging findings were determined through logistic regression analysis. Results: The ISNAs increase with the age and are present in all MCI types, particularly in those multiple domains, and carrying the APOE ϵ4 allele, and are associated with WMH, lacunes, BCr, and LVBr. Conclusion: This study demonstrates that cortical and subcortical vascular and atrophic processes contribute to ISNAs. Long prospective population-based studies are needed to disentangle the role of ISNAs in the conversion from MCI to dementia

cglasso: An R Package for Conditional Graphical Lasso Inference with Censored and Missing Values

Sparse graphical models have revolutionized multivariate inference. With the advent of high-dimensional multivariate data in many applied fields, these methods are able to detect a much lower-dimensional structure, often represented via a sparse conditional independence graph. There have been numerous extensions of such methods in the past decade. Many practical applications have additional covariates or suffer from missing or censored data. Despite the development of these extensions of sparse inference methods for graphical models, there have been so far no implementations for, e.g., conditional graphical models. Here we present the general-purpose package cglasso for estimating sparse conditional Gaussian graphical models with potentially missing or censored data. The method employs an efficient expectation-maximization estimation of an ℓ1 -penalized likelihood via a block-coordinate descent algorithm. The package has a user-friendly data manipulation interface. It estimates a solution path and includes various automatic selection algorithms for the two ℓ1 tuning parameters, associated with the sparse precision matrix and sparse regression coefficients, respectively. The package pays particular attention to the visualization of the results, both by means of marginal tables and figures, and of the inferred conditional independence graphs. This package provides a unique and computational efficient implementation of a conditional Gaussian graphical model that is able to deal with the additional complications of missing and censored data. As such it constitutes an important contribution for empirical scientists wishing to detect sparse structures in high-dimensional data

Genome-wide association study between CNVs and milk production traits in Valle del Belice sheep

Copy number variation (CNV) is a major source of genomic structural variation. The aim of this study was to detect genomic CNV regions (CNVR) in Valle del Belice dairy sheep population and to identify those affecting milk production traits. The GO analysis identified possible candidate genes and pathways related to the selected traits. We identified CNVs in 416 individuals genotyped using the Illumina OvineSNP50 BeadChip array. The CNV association using a correlation-trend test model was examined with the Golden Helix SVS 8.7.0 tool. Significant CNVs were detected when their adjusted p-value was <0.01 after false discovery rate (FDR) correction. We identified 7,208 CNVs, which gave 365 CNVRs after aggregating overlapping CNVs. Thirty-one CNVRs were significantly associated with one or more traits included in the analysis. All CNVRs, except those on OAR19, overlapped with quantitative trait loci (QTL), even if they were not directly related to the traits of interest. A total of 222 genes were annotated within the significantly associated CNVRs, most of which played important roles in biological processes related to milk production and health-related traits. Identification of the genes in the CNVRs associated with the studied traits will provide the basis for further investigation of their role in the metabolic pathways related to milk production and health traits

Iron and Ferritin Modulate MHC Class I Expression and NK Cell Recognition

The ability of pathogens to sequester iron from their host cells and proteins affects their virulence. Moreover, iron is required for various innate host defense mechanisms as well as for acquired immune responses. Therefore, intracellular iron concentration may influence the interplay between pathogens and immune system. Here, we investigated whether changes in iron concentrations and intracellular ferritin heavy chain (FTH) abundance may modulate the expression of Major Histocompatibility Complex molecules (MHC), and susceptibility to Natural Killer (NK) cell cytotoxicity. FTH downregulation, either by shRNA transfection or iron chelation, led to MHC surface reduction in primary cancer cells and macrophages. On the contrary, mouse embryonic fibroblasts (MEFs) from NCOA4 null mice accumulated FTH for ferritinophagy impairment and displayed MHC class I cell surface overexpression. Low iron concentration, but not FTH, interfered with IFN-γ receptor signaling, preventing the increase of MHC-class I molecules on the membrane by obstructing STAT1 phosphorylation and nuclear translocation. Finally, iron depletion and FTH downregulation increased the target susceptibility of both primary cancer cells and macrophages to NK cell recognition. In conclusion, the reduction of iron and FTH may influence the expression of MHC class I molecules leading to NK cells activation

Defining Kawasaki disease and pediatric inflammatory multisystem syndrome-temporally associated to SARS-CoV-2 infection during SARS-CoV-2 epidemic in Italy: results from a national, multicenter survey

Background: There is mounting evidence on the existence of a Pediatric Inflammatory Multisystem Syndrome-temporally associated to SARS-CoV-2 infection (PIMS-TS), sharing similarities with Kawasaki Disease (KD). The main outcome of the study were to better characterize the clinical features and the treatment response of PIMS-TS and to explore its relationship with KD determining whether KD and PIMS are two distinct entities. Methods: The Rheumatology Study Group of the Italian Pediatric Society launched a survey to enroll patients diagnosed with KD (Kawasaki Disease Group - KDG) or KD-like (Kawacovid Group - KCG) disease between February 1st 2020, and May 31st 2020. Demographic, clinical, laboratory data, treatment information, and patients' outcome were collected in an online anonymized database (RedCAP®). Relationship between clinical presentation and SARS-CoV-2 infection was also taken into account. Moreover, clinical characteristics of KDG during SARS-CoV-2 epidemic (KDG-CoV2) were compared to Kawasaki Disease patients (KDG-Historical) seen in three different Italian tertiary pediatric hospitals (Institute for Maternal and Child Health, IRCCS "Burlo Garofolo", Trieste; AOU Meyer, Florence; IRCCS Istituto Giannina Gaslini, Genoa) from January 1st 2000 to December 31st 2019. Chi square test or exact Fisher test and non-parametric Wilcoxon Mann-Whitney test were used to study differences between two groups. Results: One-hundred-forty-nine cases were enrolled, (96 KDG and 53 KCG). KCG children were significantly older and presented more frequently from gastrointestinal and respiratory involvement. Cardiac involvement was more common in KCG, with 60,4% of patients with myocarditis. 37,8% of patients among KCG presented hypotension/non-cardiogenic shock. Coronary artery abnormalities (CAA) were more common in the KDG. The risk of ICU admission were higher in KCG. Lymphopenia, higher CRP levels, elevated ferritin and troponin-T characterized KCG. KDG received more frequently immunoglobulins (IVIG) and acetylsalicylic acid (ASA) (81,3% vs 66%; p = 0.04 and 71,9% vs 43,4%; p = 0.001 respectively) as KCG more often received glucocorticoids (56,6% vs 14,6%; p < 0.0001). SARS-CoV-2 assay more often resulted positive in KCG than in KDG (75,5% vs 20%; p < 0.0001). Short-term follow data showed minor complications. Comparing KDG with a KD-Historical Italian cohort (598 patients), no statistical difference was found in terms of clinical manifestations and laboratory data. Conclusion: Our study suggests that SARS-CoV-2 infection might determine two distinct inflammatory diseases in children: KD and PIMS-TS. Older age at onset and clinical peculiarities like the occurrence of myocarditis characterize this multi-inflammatory syndrome. Our patients had an optimal response to treatments and a good outcome, with few complications and no deaths

Penalized regression and clustering in high-dimensional data

The main goal of this Thesis is to describe numerous statistical techniques that deal with high-dimensional genomic data. The Thesis begins with a review of the literature on penalized regression models, with particular attention to least absolute shrinkage and selection operator (LASSO) or L1-penalty methods. L1 logistic/multinomial regression models are used for variable selection and discriminant analysis with a binary/categorical response variable. The Thesis discusses and compares several methods that are commonly utilized in genetics, and introduces new strategies to select markers according to their informative content and to discriminate clusters by offering reduced panels for population genetic analysis. After having accomplished its main objective, the thesis addresses the issue of tuning parameter selection in LASSO models, studying consistency with high-dimensional data. The tuning parameter balances the trade-off between model fit and variance reduction in sparse models and its value is crucial in all the lasso-type regression. Finally, this Thesis introduces a LASSO method that can be applied to quantile regression coefficients modeling (QRCM), an approach that permits describing the coefficients of a quantile regression model as parametric functions of the order of the quantile. Compared with standard quantile regression, QRCM facilitates estimation, inference, and interpretation of the results, and generally yields a gain in efficiency. However, since each predictor has multiple associated coefficients, the total number of parameters escalates quickly with the size of the model matrix, causing numerical problems and large standard errors. Using the L1-penalty in this framework permits keeping a parsimonious set of parameters and performing variable selection in an efficient way

Clusters of effects in quantile regression models

In this paper we propose a new method for nding similarity of effects in a multivariate regression context. Using quantile regression, the effect of each covariate on a response variable is represented as a function of percentiles. Col- lecting all these curves, describing the effects of each covariate on the response, we could assess if there are covariates with similar effects. Moreover, we provide a exible algorithm which could be used not only for clustering the coefcient effects of a quantile regression framework, but also for nding clusters of generic curves. We provide also some simulated results and applications on real data, highlighting the exibility of the proposed approach in several research elds.Published551–569Coruna, SPAIN3T. Sorgente sismicaJCR Journa

Non-crossing parametric quantile functions: an application to extreme temperatures

Quantile regression can be used to obtain a non-parametric estimate of a conditional quantile function. The presence of quantile crossing, however, leads to an invalid distribution of the response and makes it difficult to use the fitted model for prediction. In this work, we show that crossing can be alleviated by modelling the quantile function parametrically. We then describe an algorithm for constrained optimisation that can be used to estimate parametric quantile functions with the noncrossing property. We investigate climate change by modelling the long-term trends of extreme temperatures in the Arctic Circle

The Joint Censored Gaussian Graphical Lasso Model

The Gaussian graphical model is one of the most used tools for inferring genetic networks. Nowadays, the data are often collected from different sources or under different biological conditions, resulting in heterogeneous datasets that exhibit a dependency structure that varies across groups. The complex structure of these data is typically recovered using regularized inferential procedures that use two penalties, one that encourages sparsity within each graph and the other that encourages common structures among the different groups. To this date, these approaches have not been developed for handling the case of censored data. However, these data are often generated by gene expression technologies such as RT-qPCR experiments. In this paper, we fill this gap and propose an extension of joint Gaussian graphical modelling to account for censored, or more generally missing, data