The fear of being laughed at among psychiatric patients

The fear of being laughed at brings to the fore the problematic side of an otherwise very positive aspect of human experience. In the streamline of investigations analyzing the presence and characteristics of gelotophobia, a study focusing on psychiatric patients was carried out. The diagnoses were established according to the criteria of the DSM IV TR (American Psychiatric Association, Diagnostic and Statistical Manual of Mental Disorders (DSM), APA, 2000). Based both on clinical and empirical observations, the main hypothesis advanced was that using the Geloph15 scale, Ss with a psychiatric diagnosis would have higher mean scores than Normal Controls. An additional hypothesis was that intragroup differences were also expected among the various diagnostic categories. The main hypothesis was amply supported, and explanatory suggestions of the finding were proposed. Intragroup differences proved also to be significant. Patients with personality disorders and patients with schizophrenic disorders scored higher than Normal Controls and the other diagnostic groups. And also the number of years spent in psychiatric care resulted significantly associated with higher gelotophobia mean scores. From the present study, a circular, interactive relationship was confirmed between laughter and mental health, which can alternatively be highly positive or deeply negativ

Meta-Analysis of Genome-Wide Linkage Studies in Celiac Disease.

OBJECTIVE: A meta-analysis of genome-wide linkage studies allows us to summarize the extensive information available from family-based studies, as the field moves into genome-wide association studies. METHODS: Here we apply the genome scan meta-analysis (GSMA) method, a rank-based, model-free approach, to combine results across eight independent genome-wide linkages performed on celiac disease (CD), including 554 families with over 1,500 affected individuals. We also investigate the agreement between signals we identified from this meta-analysis of linkage studies and those identified from genome-wide association analysis using a hypergeometric distribution. RESULTS: Not surprisingly, the most significant result was obtained in the HLA region. Outside the HLA region, suggestive evidence for linkage was obtained at the telomeric region of chromosome 10 (10q26.12-qter; p = 0.00366), and on chromosome 8 (8q22.2-q24.21; p = 0.00491). Testing signals of association and linkage within bins showed no significant evidence for co-localization of results. CONCLUSION: This meta-analysis allowed us to pool the results from available genome-wide linkage studies and to identify novel regions potentially harboring predisposing genetic variation contributing to CD. This study also shows that linkage and association studies may identify different types of disease-predisposing variants

Association of the IL-10 gene family locus on chromosome 1 with juvenile idiopathic arthritis (JIA)

The cytokine IL-10 and its family members have been implicated in autoimmune diseases and we have previously reported that genetic variants in IL-10 were associated with a rare group of diseases called juvenile idiopathic arthritis (JIA). The aim of this study was to fine map genetic variants within the IL-10 cytokine family cluster on chromosome 1 using linkage disequilibrium (LD)-tagging single nucleotide polymorphisms (tSNPs) approach with imputation and conditional analysis to test for disease associations

Frontotemporal dementia: insights into the biological underpinnings of disease through gene co-expression network analysis

BACKGROUND: In frontotemporal dementia (FTD) there is a critical lack in the understanding of biological and molecular mechanisms involved in disease pathogenesis. The heterogeneous genetic features associated with FTD suggest that multiple disease-mechanisms are likely to contribute to the development of this neurodegenerative condition. We here present a systems biology approach with the scope of i) shedding light on the biological processes potentially implicated in the pathogenesis of FTD and ii) identifying novel potential risk factors for FTD. We performed a gene co-expression network analysis of microarray expression data from 101 individuals without neurodegenerative diseases to explore regional-specific co-expression patterns in the frontal and temporal cortices for 12 genes (MAPT, GRN, CHMP2B, CTSC, HLA-DRA, TMEM106B, C9orf72, VCP, UBQLN2, OPTN, TARDBP and FUS) associated with FTD and we then carried out gene set enrichment and pathway analyses, and investigated known protein-protein interactors (PPIs) of FTD-genes products. RESULTS: Gene co-expression networks revealed that several FTD-genes (such as MAPT and GRN, CTSC and HLA-DRA, TMEM106B, and C9orf72, VCP, UBQLN2 and OPTN) were clustering in modules of relevance in the frontal and temporal cortices. Functional annotation and pathway analyses of such modules indicated enrichment for: i) DNA metabolism, i.e. transcription regulation, DNA protection and chromatin remodelling (MAPT and GRN modules); ii) immune and lysosomal processes (CTSC and HLA-DRA modules), and; iii) protein meta/catabolism (C9orf72, VCP, UBQLN2 and OPTN, and TMEM106B modules). PPI analysis supported the results of the functional annotation and pathway analyses. CONCLUSIONS: This work further characterizes known FTD-genes and elaborates on their biological relevance to disease: not only do we indicate likely impacted regional-specific biological processes driven by FTD-genes containing modules, but also do we suggest novel potential risk factors among the FTD-genes interactors as targets for further mechanistic characterization in hypothesis driven cell biology work

An additional k-means clustering step improves the biological features of WGCNA gene co-expression networks

Background: Weighted Gene Co-expression Network Analysis (WGCNA) is a widely used R software package for the generation of gene co-expression networks (GCN). WGCNA generates both a GCN and a derived partitioning of clusters of genes (modules). We propose k-means clustering as an additional processing step to conventional WGCNA, which we have implemented in the R package km2gcn (k-means to gene co-expression network, https://github.com/juanbot/km2gcn). Results: We assessed our method on networks created from UKBEC data (10 different human brain tissues), on networks created from GTEx data (42 human tissues, including 13 brain tissues), and on simulated networks derived from GTEx data. We observed substantially improved module properties, including: (1) few or zero misplaced genes; (2) increased counts of replicable clusters in alternate tissues (x3.1 on average); (3) improved enrichment of Gene Ontology terms (seen in 48/52 GCNs) (4) improved cell type enrichment signals (seen in 21/23 brain GCNs); and (5) more accurate partitions in simulated data according to a range of similarity indices. Conclusions: The results obtained from our investigations indicate that our k-means method, applied as an adjunct to standard WGCNA, results in better network partitions. These improved partitions enable more fruitful downstream analyses, as gene modules are more biologically meaningful

Gene co-expression networks shed light into diseases of brain iron accumulation

Aberrant brain iron deposition is observed in both common and rare neurodegenerative disorders, including those categorized as Neurodegeneration with Brain Iron Accumulation (NBIA), which are characterized by focal iron accumulation in the basal ganglia. Two NBIA genes are directly involved in iron metabolism, but whether other NBIA-related genes also regulate iron homeostasis in the human brain, and whether aberrant iron deposition contributes to neurodegenerative processes remains largely unknown. This study aims to expand our understanding of these iron overload diseases and identify relationships between known NBIA genes and their main interacting partners by using a systems biology approach. We used whole-transcriptome gene expression data from human brain samples originating from 101 neuropathologically normal individuals (10 brain regions) to generate weighted gene co-expression networks and cluster the 10 known NBIA genes in an unsupervised manner. We investigated NBIA-enriched networks for relevant cell types and pathways, and whether they are disrupted by iron loading in NBIA diseased tissue and in an in vivo mouse model. We identified two basal ganglia gene co-expression modules significantly enriched for NBIA genes, which resemble neuronal and oligodendrocytic signatures. These NBIA gene networks are enriched for iron-related genes, and implicate synapse and lipid metabolism related pathways. Our data also indicates that these networks are disrupted by excessive brain iron loading. We identified multiple cell types in the origin of NBIA disorders. We also found unforeseen links between NBIA networks and iron-related processes, and demonstrate convergent pathways connecting NBIAs and phenotypically overlapping diseases. Our results are of further relevance for these diseases by providing candidates for new causative genes and possible points for therapeutic intervention

Application of a New Method for GWAS in a Related Case/Control Sample with Known Pedigree Structure: Identification of New Loci for Nephrolithiasis

In contrast to large GWA studies based on thousands of individuals and large meta-analyses combining GWAS results, we analyzed a small case/control sample for uric acid nephrolithiasis. Our cohort of closely related individuals is derived from a small, genetically isolated village in Sardinia, with well-characterized genealogical data linking the extant population up to the 16th century. It is expected that the number of risk alleles involved in complex disorders is smaller in isolated founder populations than in more diverse populations, and the power to detect association with complex traits may be increased when related, homogeneous affected individuals are selected, as they are more likely to be enriched with and share specific risk variants than are unrelated, affected individuals from the general population. When related individuals are included in an association study, correlations among relatives must be accurately taken into account to ensure validity of the results. A recently proposed association method uses an empirical genotypic covariance matrix estimated from genome-screen data to allow for additional population structure and cryptic relatedness that may not be captured by the genealogical data. We apply the method to our data, and we also investigate the properties of the method, as well as other association methods, in our highly inbred population, as previous applications were to outbred samples. The more promising regions identified in our initial study in the genetic isolate were then further investigated in an independent sample collected from the Italian population. Among the loci that showed association in this study, we observed evidence of a possible involvement of the region encompassing the gene LRRC16A, already associated to serum uric acid levels in a large meta-analysis of 14 GWAS, suggesting that this locus might lead a pathway for uric acid metabolism that may be involved in gout as well as in nephrolithiasis

Genetic heterogeneity in Italian families with IgA nephropathy: suggestive linkage for two novel IgA nephropathy loci.

IgA nephropathy (IgAN) is the most common glomerulonephritis worldwide, but its etiologic mechanisms are still poorly understood. Different prevalences among ethnic groups and familial aggregation, together with an increased familial risk, suggest important genetic influences on its pathogenesis. A locus for familial IgAN, called "IGAN1," on chromosome 6q22-23 has been described, without the identification of any responsible gene. The partners of the European IgAN Consortium organized a second genomewide scan in 22 new informative Italian multiplex families. A total of 186 subjects (59 affected and 127 unaffected) were genotyped and were included in a two-stage genomewide linkage analysis. The regions 4q26-31 and 17q12-22 exhibited the strongest evidence of linkage by nonparametric analysis (best P=.0025 and .0045, respectively). These localizations were also supported by multipoint parametric analysis, in which peak LOD scores of 1.83 ( alpha =0.50) and 2.56 ( alpha =0.65) were obtained using the affected-only dominant model, and by allowance for the presence of genetic heterogeneity. Our results provide further evidence for genetic heterogeneity among families with IgAN. Evidence of linkage to multiple chromosomal regions is consistent with both an oligo/polygenic and a multiple-susceptibility-gene model for familial IgAN, with small or moderate effects in determining the pathological phenotype. Although we identified new candidate regions, replication studies are required to confirm the genetic contribution to familial IgA

Teaching molecular genetics: chapter 4—positional cloning of genetic disorders

Positional cloning is the approach of choice for the identification of genetic mutations underlying the pathological development of diseases with simple Mendelian inheritance. It consists of different consecutive steps, starting with recruitment of patients and DNA collection, that are critical to the overall process. A genetic analysis of the enrolled patients and their families is performed, based on genetic recombination frequencies generated by meiotic cross-overs and on genome-wide molecular studies, to define a critical DNA region of interest. This analysis culminates in a statistical estimate of the probability that disease features may segregate in the families independently or in association with specific molecular markers located in known regions. In this latter case, a marker can be defined as being linked to the disease manifestations. The genetic markers define an interval that is a function of their recombination frequencies with the disease, in which the disease gene is localised. The identification and characterisation of chromosome abnormalities as translocations, deletions and duplications by classical cytogenetic methods or by the newly developed microarray-based comparative genomic hybridisation (array CGH) technique may define extensions and borders of the genomic regions involved. The step following the definition of a critical genomic region is the identification of candidate genes that is based on the analysis of available databases from genome browsers. Positional cloning culminates in the identification of the causative gene mutation, and the definition of its functional role in the pathogenesis of the disorder, by the use of cell-based or animal-based experiments. More often, positional cloning ends with the generation of mice with homologous mutations reproducing the human clinical phenotype. Altogether, positional cloning has represented a fundamental step in the research on genetic renal disorders, leading to the definition of several disease mechanisms and allowing a proper diagnostic approach to many conditions

Predicting the Risk of Rheumatoid Arthritis and Its Age of Onset through Modelling Genetic Risk Variants with Smoking

The improved characterisation of risk factors for rheumatoid arthritis (RA) suggests they could be combined to identify individuals at increased disease risks in whom preventive strategies may be evaluated. We aimed to develop an RA prediction model capable of generating clinically relevant predictive data and to determine if it better predicted younger onset RA (YORA). Our novel modelling approach combined odds ratios for 15 four-digit/10 two-digit HLA-DRB1 alleles, 31 single nucleotide polymorphisms (SNPs) and ever-smoking status in males to determine risk using computer simulation and confidence interval based risk categorisation. Only males were evaluated in our models incorporating smoking as ever-smoking is a significant risk factor for RA in men but not women. We developed multiple models to evaluate each risk factor's impact on prediction. Each model's ability to discriminate anti-citrullinated protein antibody (ACPA)-positive RA from controls was evaluated in two cohorts: Wellcome Trust Case Control Consortium (WTCCC: 1,516 cases; 1,647 controls); UK RA Genetics Group Consortium (UKRAGG: 2,623 cases; 1,500 controls). HLA and smoking provided strongest prediction with good discrimination evidenced by an HLA-smoking model area under the curve (AUC) value of 0.813 in both WTCCC and UKRAGG. SNPs provided minimal prediction (AUC 0.660 WTCCC/0.617 UKRAGG). Whilst high individual risks were identified, with some cases having estimated lifetime risks of 86%, only a minority overall had substantially increased odds for RA. High risks from the HLA model were associated with YORA (P<0.0001); ever-smoking associated with older onset disease. This latter finding suggests smoking's impact on RA risk manifests later in life. Our modelling demonstrates that combining risk factors provides clinically informative RA prediction; additionally HLA and smoking status can be used to predict the risk of younger and older onset RA, respectively