Sparse estimation in semiparametric finite mixture of varying coefficient regression models.

Finite mixture of regressions (FMR) are commonly used to model heterogeneous effects of covariates on a response variable in settings where there are unknown underlying subpopulations. FMRs, however, cannot accommodate situations where covariates' effects also vary according to an "index" variable-known as finite mixture of varying coefficient regression (FM-VCR). Although complex, this situation occurs in real data applications: the osteocalcin (OCN) data analyzed in this manuscript presents a heterogeneous relationship where the effect of a genetic variant on OCN in each hidden subpopulation varies over time. Oftentimes, the number of covariates with varying coefficients also presents a challenge: in the OCN study, genetic variants on the same chromosome are considered jointly. The relative proportions of hidden subpopulations may also change over time. Nevertheless, existing methods cannot provide suitable solutions for accommodating all these features in real data applications. To fill this gap, we develop statistical methodologies based on regularized local-kernel likelihood for simultaneous parameter estimation and variable selection in sparse FM-VCR models. We study large-sample properties of the proposed methods. We then carry out a simulation study to evaluate the performance of various penalties adopted for our regularized approach and ascertain the ability of a BIC-type criterion for estimating the number of subpopulations. Finally, we applied the FM-VCR model to analyze the OCN data and identified several covariates, including genetic variants, that have age-dependent effects on OCN.

Disseminated Intravascular Coagulation Associated with Large Deletion of Immunoglobulin Heavy Chain.

Although the majority of monogenic defects underlying primary immunodeficiency are microlesions, large lesions like large deletions are rare and constitute less than 10% of these patients. The immunoglobulin heavy chain (IGH) locus is one of the common regions for such genetic alterations. This study describes a rare case of autosomal recessive agammaglobulinemia with a homozygous large deletion in chromosome 14q32.33 (106067756-106237742) immunoglobulin heavy chain clusters with an unusual and severe skin infection and disseminated intravascular coagulopathy.

Autoimmune manifestations among 461 patients with monogenic inborn errors of immunity.

BACKGROUND: The inborn errors of immunity (IEIs) are a group of heterogeneous disorders mainly characterized by severe and recurrent infections besides other complications including autoimmune and inflammatory diseases. In this study, we aim to evaluate clinical, immunologic, and molecular data of monogenic IEI patients with and without autoimmune manifestations. METHODS: We have retrospectively screened cases of monogenic IEI in the Iranian PID registry for the occurrence of autoimmunity and immune dysregulation. A questionnaire was filled for all qualified patients with monogenic defects to evaluate demographic, laboratory, clinical, and molecular data. RESULTS: A total of 461 monogenic IEI patients (290 male and 171 female) with a median (IQR) age of 11.0 (6.0-20.0) years were enrolled in this study. Overall, 331 patients (72.1%) were born to consanguineous parents. At the time of the study, 330 individuals (75.7%) were alive and 106 (24.3%) were deceased. Autoimmunity was reported in 92 (20.0%) patients with a median (IQR) age at autoimmune diagnosis of 4.0 (2.0-7.0) years. Sixteen patients (3.5%) showed autoimmune complications (mostly autoimmune cytopenia) as the first presentation of the disease. Most of the patients with autoimmunity were diagnosed clinically with common variable immunodeficiency (42.4%). The frequency of sinusitis and splenomegaly was significantly higher in patients with autoimmunity than patients without autoimmunity. In patients with autoimmunity, the most common pathogenic variants were identified in LRBA (in 21 patients, 23.0%), ATM (in 13 patients, 14.0%), and BTK (in 9 patients, 10.0%) genes. In the evaluation of autoimmunity by different genes, 4 of 4 IL10RB (100%), 3 of 3 AIRE (100%), and 21 of 30 LRBA (70.0%) mutated genes had the highest prevalence of autoimmunity. CONCLUSIONS: Autoimmune phenomena are common features among patients with monogenic IEI and are associated with a more complicated course of the disease. Therefore, when encountering autoimmune disorders, especially in the setting of dysgammaglobulinemia, it would be appropriate to conduct next-generation sequencing to discover responsible genes for the immune dysregulation at an early stage of the disease.

Monogenic Primary Immunodeficiency Disorder Associated with Common Variable Immunodeficiency and Autoimmunity.

BACKGROUND: Common variable immunodeficiency (CVID) is the most frequent primary immunodeficiency disorder mainly characterized by recurrent bacterial infections besides other immunological defects including loss of or dysfunction of B cells and decreased immunoglobulin levels. In this study, our aim is to evaluate clinical, immunological, and molecular data of patients with a primary clinical diagnosis of CVID and autoimmune phenotype with a confirmed genetic diagnosis. METHODS: Among 297 patients with CVID, who were registered in the Iranian Primary Immunodeficiency Registry at Children's Medical Center Hospital in Iran, 83 patients have been genetically examined and 27 patients with autoimmunity and confirmed genetic mutations were selected for analysis. Whole-exome sequencing and confirmatory Sanger sequencing methods were used for the study population. A questionnaire was retrospectively filled for all patients to evaluate demographic, laboratory, clinical, and genetic data. RESULTS: In the 27 studied patients, 11 different genetic defects were identified, and the most common mutated gene was LRBA, reported in 17 (63.0%) patients. Two patients (7.7%) showed autoimmune complications as the first presentation of immunodeficiency. Eleven patients (40.7%) developed one type of autoimmunity, and 16 patients (59.3%) progressed to poly-autoimmunity. Most of the patients with mono-autoimmunity (n = 9, 90.0%) primarily developed infectious complications, while in patients with poly-autoimmunity, the most common first presentation was enteropathy (n = 6, 37.6%). In 13 patients (61.9%), the diagnosis of autoimmune disorders preceded the diagnosis of primary immunodeficiency. The most frequent autoimmune manifestations were hematologic (40.7%), gastrointestinal (48.1%), rheumatologic (25.9%), and dermatologic (22.2%) disorders. Patients with poly-autoimmunity had lower regulatory T cells than patients with mono-autoimmunity. CONCLUSION: In our cohort, the diagnosis of autoimmune disorders preceded the diagnosis of primary immunodeficiency in most patients. This association highlights the fact that patients referring with autoimmune manifestations should be evaluated for humoral immunity.

Comparison of Common Monogenic Defects in a Large Predominantly Antibody Deficiency Cohort.

BACKGROUND: Predominantly antibody deficiencies (PADs) are the most common primary immunodeficiencies, characterized by hypogammaglobulinemia and inability to generate effective antibody responses. OBJECTIVE: We intended to report most common monogenic PADs and to investigate how patients with PAD who were primarily diagnosed as suffering from agammaglobulinemia, hyper-IgM (HIgM) syndrome, and common variable immunodeficiency (CVID) have different clinical and immunological findings. METHODS: Stepwise next-generation sequencing and Sanger sequencing were performed for confirmation of the mutations in the patients clinically diagnosed as suffering from agammaglobulinemia, HIgM syndrome, and CVID. RESULTS: Among 550 registered patients, the predominant genetic defects associated with agammaglobulinemia (48 Bruton's tyrosine kinase [BTK] and 6 µ heavy chain deficiencies), HIgM syndrome (21 CD40 ligand and 7 activation-induced cytidine deaminase deficiencies), and CVID (17 lipopolysaccharides-responsive beige-like anchor deficiency and 12 atypical Immunodeficiency, Centromeric instability, and Facial dysmorphism syndromes) were identified. Clinical disease severity was significantly higher in patients with µ heavy chain and CD40 ligand mutations compared with patients with BTK (P = .003) and activation-induced cytidine deaminase (P = .009) mutations. Paralysis following live polio vaccination was considerably higher in patients with µ heavy chain deficiency compared with BTK deficiency (P < .001). We found a genotype-phenotype correlation among patients with BTK mutations regarding clinical manifestation of meningitis and chronic diarrhea. Surprisingly, we noticed that first presentations in most patients with Immunodeficiency, Centromeric instability, and Facial dysmorphism were respiratory complications (P = .008), whereas first presentations in patients with lipopolysaccharides-responsive beige-like anchor deficiency were nonrespiratory complications (P = .008). CONCLUSIONS: This study highlights similarities and differences in the clinical and genetic spectrum of the most common PAD-associated gene defects. This comprehensive comparison will facilitate clinical decision making, and improve prognosis and targeted treatment.

Fourth Update on the Iranian National Registry of Primary Immunodeficiencies: Integration of Molecular Diagnosis.

BACKGROUND: The number of inherited diseases and the spectrum of clinical manifestations of primary immunodeficiency disorders (PIDs) are ever-expanding. Molecular diagnosis using genomic approaches should be performed for all PID patients since it provides a resource to improve the management and to estimate the prognosis of patients with these rare immune disorders. METHOD: The current update of Iranian PID registry (IPIDR) contains the clinical phenotype of newly registered patients during last 5 years (2013-2018) and the result of molecular diagnosis in patients enrolled for targeted and next-generation sequencing. RESULTS: Considering the newly diagnosed patients (n = 1395), the total number of registered PID patients reached 3056 (1852 male and 1204 female) from 31 medical centers. The predominantly antibody deficiency was the most common subcategory of PID (29.5%). The putative causative genetic defect was identified in 1014 patients (33.1%) and an autosomal recessive pattern was found in 79.3% of these patients. Among the genetically different categories of PID patients, the diagnostic rate was highest in defects in immune dysregulation and lowest in predominantly antibody deficiencies and mutations in the MEFV gene were the most frequent genetic disorder in our cohort. CONCLUSIONS: During a 20-year registration of Iranian PID patients, significant changes have been observed by increasing the awareness of the medical community, national PID network establishment, improving therapeutic facilities, and recently by inclusion of the molecular diagnosis. The current collective study of PID phenotypes and genotypes provides a major source for ethnic surveillance, newborn screening, and genetic consultation for prenatal and preimplantation genetic diagnosis.

Clinical, immunologic, and genetic spectrum of 696 patients with combined immunodeficiency.

BACKGROUND: Combined immunodeficiencies (CIDs) are diseases of defective adaptive immunity with diverse clinical phenotypes. Although CIDs are more prevalent in the Middle East than Western countries, the resources for genetic diagnosis are limited. OBJECTIVES: This study aims to characterize the categories of patients with CIDs in Iran clinically and genetically. METHODS: Clinical and laboratory data were obtained from 696 patients with CIDs. Patients were subdivided into those with syndromic (344 patients) and nonsyndromic (352 patients) CIDs. Targeted DNA sequencing was performed on 243 (34.9%) patients. RESULTS: The overall diagnostic yield of the 243 sequenced patients was 77.8% (189 patients). The clinical diagnosis of hyper-IgE syndrome (P < .001), onset of disease at greater than 5 years (P = .02), and absence of multiple affected family members (P = .04) were significantly more frequent in the patients without a genetic diagnosis. An autosomal recessive disease was found in 62.9% of patients, reflecting the high rate of consanguinity in this cohort. Mutations impairing VDJ recombination and DNA repair were the most common underlying causes of CIDs. However, in patients with syndromic CIDs, autosomal recessive mutations in ataxia-telangiectasia mutated (ATM), autosomal dominant mutations in signal transducer and activator of transcription 3 (STAT3), and microdeletions in 22q11.21 were the most commonly affected genomic loci. Patients with syndromic CIDs had a significantly lower 5-year survival rate rather than those with nonsyndromic CIDs. CONCLUSIONS: This study provides proof of principle for the application of targeted next-generation sequencing panels in countries with limited diagnostic resources. The effect of genetic diagnosis on clinical care requires continued improvements in therapeutic resources for these patients.

Modeling, control, and simulation of grid connected intelligent hybrid battery/photovoltaic system using new hybrid fuzzy-neural method.

Nowadays, photovoltaic (PV) generation is growing increasingly fast as a renewable energy source. Nevertheless, the drawback of the PV system is its dependence on weather conditions. Therefore, battery energy storage (BES) can be considered to assist for a stable and reliable output from PV generation system for loads and improve the dynamic performance of the whole generation system in grid connected mode. In this paper, a novel topology of intelligent hybrid generation systems with PV and BES in a DC-coupled structure is presented. Each photovoltaic cell has a specific point named maximum power point on its operational curve (i.e. current-voltage or power-voltage curve) in which it can generate maximum power. Irradiance and temperature changes affect these operational curves. Therefore, the nonlinear characteristic of maximum power point to environment has caused to development of different maximum power point tracking techniques. In order to capture the maximum power point (MPP), a hybrid fuzzy-neural maximum power point tracking (MPPT) method is applied in the PV system. Obtained results represent the effectiveness and superiority of the proposed method, and the average tracking efficiency of the hybrid fuzzy-neural is incremented by approximately two percentage points in comparison to the conventional methods. It has the advantages of robustness, fast response and good performance. A detailed mathematical model and a control approach of a three-phase grid-connected intelligent hybrid system have been proposed using Matlab/Simulink.

Optimum study design for detecting imprinting and maternal effects based on partial likelihood.

Despite spectacular advances in molecular genomic technologies in the past two decades, resources available for genomic studies are still finite and limited, especially for family-based studies. Hence, it is important to consider an optimum study design to maximally utilize limited resources to increase statistical power in family-based studies. A particular question of interest is whether it is more profitable to genotype siblings of probands or to recruit more independent families. Numerous studies have attempted to address this study design issue for simultaneous detection of imprinting and maternal effects, two important epigenetic factors for studying complex diseases. The question is far from settled, however, mainly due to the fact that results and recommendations in the literature are based on anecdotal evidence from limited simulation studies rather than based on rigorous statistical analysis. In this article, we propose a systematic approach to study various designs based on a partial likelihood formulation. We derive the asymptotic properties and obtain formulas for computing the information contents of study designs being considered. Our results show that, for a common disease, recruiting additional siblings is beneficial because both affected and unaffected individuals will be included. However, if a disease is rare, then any additional siblings recruited are most likely to be unaffected, thus contributing little additional information; in such cases, additional families will be a better choice with a fixed amount of resources. Our work thus offers a practical strategy for investigators to select the optimum study design within a case-control family scheme before data collection.

Long-term evaluation of a historical cohort of Iranian common variable immunodeficiency patients.

OBJECTIVES: Common variable immune deficiency (CVID) is the most frequent form of symptomatic primary immunodeficiency disease, characterized by hypogammaglobulinemia, recurrent infections and increased predisposition to autoimmunity and malignancies. The aim of this study was to reconsider important points of previously performed studies on Iranian CVID patients diagnosed and followed from 1984 to 2013. METHODS: Diagnosis was made using approved criteria including reductions of serum levels of immunoglobulins and exclusion of well-known single gene defects in individuals with an age >4 years and evidence of specific antibody deficiency. RESULTS: Detailed information on demographic data, survival rates, clinical phenotypes, immunologic and genetic data and treatment of 173 patients are provided. The early onset presentation (74.5%) and rate of consanguineous marriage (61.2%) were considerably higher in our cohort. Our study revealed clinically related correlations regarding consanguinity, the population of naïve CD4(+) T cells and switched-memory B cells, cytokine levels and special genetic factors (including HLA and AID genes). CONCLUSION: Despite current efforts, more comprehensive studies are needed, especially for classification and investigation of the genetic background and prognostic factors for patients with CVID in order to better managment and followup of patinets.

Autosomal recessive agammaglobulinemia: a novel non-sense mutation in CD79a.

This study describes the fifth case worldwide of autosomal recessive agammaglobulinemia due to a novel non-sense mutation in CD79a gene with a severe unusual onset due to an invasive central nervous system infection.

Regularization in finite mixture of regression models with diverging number of parameters.

Feature (variable) selection has become a fundamentally important problem in recent statistical literature. Sometimes, in applications, many variables are introduced to reduce possible modeling biases, but the number of variables a model can accommodate is often limited by the amount of data available. In other words, the number of variables considered depends on the sample size, which reflects the estimability of the parametric model. In this article, we consider the problem of feature selection in finite mixture of regression models when the number of parameters in the model can increase with the sample size. We propose a penalized likelihood approach for feature selection in these models. Under certain regularity conditions, our approach leads to consistent variable selection. We carry out extensive simulation studies to evaluate the performance of the proposed approach under controlled settings. We also applied the proposed method to two real data. The first is on telemonitoring of Parkinson's disease (PD), where the problem concerns whether dysphonic features extracted from the patients' speech signals recorded at home can be used as surrogates to study PD severity and progression. The second is on breast cancer prognosis, in which one is interested in assessing whether cell nuclear features may offer prognostic values on long-term survival of breast cancer patients. Our analysis in each of the application revealed a mixture structure in the study population and uncovered a unique relationship between the features and the response variable in each of the mixture component.

Feature selection in finite mixture of sparse normal linear models in high-dimensional feature space.

Rapid advancement in modern technology has allowed scientists to collect data of unprecedented size and complexity. This is particularly the case in genomics applications. One type of statistical problem in such applications is concerned with modeling an output variable as a function of a small subset of a large number of features based on relatively small sample sizes, which may even be coming from multiple subpopulations. As such, selecting the correct predictive features (variables) for each subpopulation is the key. To address this issue, we consider the problem of feature selection in finite mixture of sparse normal linear (FMSL) models in large feature spaces. We propose a 2-stage procedure to overcome computational difficulties and large false discovery rates caused by the large model space. First, to deal with the curse of dimensionality, a likelihood-based boosting is designed to effectively reduce the number of candidate features. This is the key thrust of our new method. The greatly reduced set of features is then subjected to a sparsity inducing procedure via a penalized likelihood method. A novel scheme is also proposed for the difficult problem of finding good starting points for the expectation-maximization estimation of mixture parameters. We use an extended Bayesian information criterion to determine the final FMSL model. Simulation results indicate that the procedure is successful in selecting the significant features without including a large number of insignificant ones. A real data example on gene transcription regulation is also presented.

Pairwise network mechanisms in the host signaling response to coxsackievirus B3 infection.

Signal transduction networks can be perturbed biochemically, genetically, and pharmacologically to unravel their functions. But at the systems level, it is not clear how such perturbations are best implemented to extract molecular mechanisms that underlie network function. Here, we combined pairwise perturbations with multiparameter phosphorylation measurements to reveal causal mechanisms within the signaling network response of cardiomyocytes to coxsackievirus B3 (CVB3) infection. Using all possible pairs of six kinase inhibitors, we assembled a dynamic nine-protein phosphorylation signature of perturbed CVB3 infectivity. Cluster analysis of the resulting dataset showed repeatedly that paired inhibitor data were required for accurate data-driven predictions of kinase substrate links in the host network. With pairwise data, we also derived a high-confidence network based on partial correlations, which identified phospho-IκBα as a central "hub" in the measured phosphorylation signature. The reconstructed network helped to connect phospho-IκBα with an autocrine feedback circuit in host cells involving the proinflammatory cytokines, TNF and IL-1. Autocrine blockade substantially inhibited CVB3 progeny release and improved host cell viability, implicating TNF and IL-1 as cell autonomous components of CVB3-induced myocardial damage. We conclude that pairwise perturbations, when combined with network-level intracellular measurements, enrich for mechanisms that would be overlooked by single perturbants.

A Robust Unified Approach to Analyzing Methylation and Gene Expression Data.

Microarray technology has made it possible to investigate expression levels, and more recently methylation signatures, of thousands of genes simultaneously, in a biological sample. Since more and more data from different biological systems or technological platforms are being generated at an incredible rate, there is an increasing need to develop statistical methods that are applicable to multiple data types and platforms. Motivated by such a need, a flexible finite mixture model that is applicable to methylation, gene expression, and potentially data from other biological systems, is proposed. Two major thrusts of this approach are to allow for a variable number of components in the mixture to capture non-biological variation and small biases, and to use a robust procedure for parameter estimation and probe classification. The method was applied to the analysis of methylation signatures of three breast cancer cell lines. It was also tested on three sets of expression microarray data to study its power and type I error rates. Comparison with a number of existing methods in the literature yielded very encouraging results; lower type I error rates and comparable/better power were achieved based on the limited study. Furthermore, the method also leads to more biologically interpretable results for the three breast cancer cell lines.

Gamma-Normal-Gamma mixture model for detecting differentially methylated loci in three breast cancer cell lines.

With state-of-the-art microarray technologies now available for whole genome CpG island (CGI) methylation profiling, there is a need to develop statistical models that are specifically geared toward the analysis of such data. In this article, we propose a Gamma-Normal-Gamma (GNG) mixture model for describing three groups of CGI loci: hypomethylated, undifferentiated, and hypermethylated, from a single methylation microarray. This model was applied to study the methylation signatures of three breast cancer cell lines: MCF7, T47D, and MDAMB361. Biologically interesting and interpretable results are obtained, which highlights the heterogeneity nature of the three cell lines. This underlies the premise for the need of analyzing each of the microarray slides individually as opposed to pooling them together for a single analysis. Our comparisons with the fitted densities from the Normal-Uniform (NU) mixture model in the literature proposed for gene expression analysis show an improved goodness of fit of the GNG model over the NU model. Although the GNG model was proposed in the context of single-slide methylation analysis, it can be readily adapted to analyze multi-slide methylation data as well as other types of microarray data.