Traits Contributing to the Autistic Spectrum

It is increasingly recognised that traits associated with autism reflect a spectrum with no clear boundary between typical and atypical behaviour. Dimensional traits are needed to investigate the broader autism phenotype.Ninety-three individual measures reflecting components of social, communication and repetitive behaviours characterising autistic spectrum disorder (ASD) were identified between the ages of 6 months and 9 years from the ALSPAC database. Using missing value imputation, data for 13,138 children were analysed. Factor analysis suggested the existence of 7 factors explaining 85% of the variance. The factors were labelled: verbal ability, language acquisition, social understanding, semantic-pragmatic skills, repetitive-stereotyped behaviour, articulation and social inhibition. Four factors (1, 3, 5 and 7) were specific to ASD being more strongly associated with this phenotype than other co-morbid conditions while other factors were more associated with learning difficulties and specific language impairment. Nevertheless, all 7 factors contributed independently to the explanation of ASD (p<0.001). Exploration of putative genetic causal factors such as variants in the CNTNAP2 gene showed a varying pattern of associations with these traits. An alternative predictive model of ASD was derived using four individual measures: the coherence subscale of the Children's Communication Checklist (9y), the Social and Communication Disorders Checklist (91 m), repetitive behaviour (69 m) and the sociability subscale of the Emotionality Activity and Sociability measure (38 m). Although univarably these traits performed better than some factors, their combined explanations of ASD were similar (R(2) =  0.48).These results support the fractional nature of ASD with different aetiological origins for these components despite pleiotropic genetic effects being observed. These traits are likely to be useful in the exploration of ASD

Dissection of genetic associations with language-related traits in population-based cohorts

Recent advances in the field of language-related disorders have led to the identification of candidate genes for specific language impairment (SLI) and dyslexia. Replication studies have been conducted in independent samples including population-based cohorts, which can be characterised for a large number of relevant cognitive measures. The availability of a wide range of phenotypes allows us to not only identify the most suitable traits for replication of genetic association but also to refine the associated cognitive trait. In addition, it is possible to test for pleiotropic effects across multiple phenotypes which could explain the extensive comorbidity observed across SLI, dyslexia and other neurodevelopmental disorders. The availability of genome-wide genotype data for such cohorts will facilitate this kind of analysis but important issues, such as multiple test corrections, have to be taken into account considering that small effect sizes are expected to underlie such associations

Investigation of Dyslexia and SLI Risk Variants in Reading- and Language-Impaired Subjects

Dyslexia (or reading disability) and specific language impairment (or SLI) are common childhood disorders that show considerable co-morbidity and diagnostic overlaps and have been suggested to share some genetic aetiology. Recently, genetic risk variants have been identified for SLI and dyslexia enabling the direct evaluation of possible shared genetic influences between these disorders. In this study we investigate the role of variants in these genes (namely MRPL19/C20RF3,ROBO1,DCDC2, KIAA0319, DYX1C1, CNTNAP2, ATP2C2 and CMIP) in the aetiology of SLI and dyslexia. We perform case–control and quantitative association analyses using measures of oral and written language skills in samples of SLI and dyslexic families and cases. We replicate association between KIAA0319 and DCDC2 and dyslexia and provide evidence to support a role for KIAA0319 in oral language ability. In addition, we find association between reading-related measures and variants in CNTNAP2 and CMIP in the SLI families

Pattern recognition receptors in immune disorders affecting the skin.

Contains fulltext : 109004.pdf (publisher's version ) (Open Access)Pattern recognition receptors (PRRs) evolved to protect organisms against pathogens, but excessive signaling can induce immune responses that are harmful to the host. Putative PRR dysfunction is associated with numerous immune disorders that affect the skin, such as systemic lupus erythematosus, cryopyrin-associated periodic syndrome, and primary inflammatory skin diseases including psoriasis and atopic dermatitis. As yet, the evidence is often confined to genetic association studies without additional proof of a causal relationship. However, insight into the role of PRRs in the pathophysiology of some disorders has already resulted in new therapeutic approaches based on immunomodulation of PRRs

A systematic review of the incidence of schizophrenia: the distribution of rates and the influence of sex, urbanicity, migrant status and methodology

BACKGROUND: Understanding variations in the incidence of schizophrenia is a crucial step in unravelling the aetiology of this group of disorders. The aims of this review are to systematically identify studies related to the incidence of schizophrenia, to describe the key features of these studies, and to explore the distribution of rates derived from these studies. METHODS: Studies with original data related to the incidence of schizophrenia (published 1965–2001) were identified via searching electronic databases, reviewing citations and writing to authors. These studies were divided into core studies, migrant studies, cohort studies and studies based on Other Special Groups. Between- and within-study filters were applied in order to identify discrete rates. Cumulative plots of these rates were made and these distributions were compared when the underlying rates were sorted according to sex, urbanicity, migrant status and various methodological features. RESULTS: We identified 100 core studies, 24 migrant studies, 23 cohort studies and 14 studies based on Other Special Groups. These studies, which were drawn from 33 countries, generated a total of 1,458 rates. Based on discrete core data for persons (55 studies and 170 rates), the distribution of rates was asymmetric and had a median value (10%–90% quantile) of 15.2 (7.7–43.0) per 100,000. The distribution of rates was significantly higher in males compared to females; the male/female rate ratio median (10%–90% quantile) was 1.40 (0.9–2.4). Those studies conducted in urban versus mixed urban-rural catchment areas generated significantly higher rate distributions. The distribution of rates in migrants was significantly higher compared to native-born; the migrant/native-born rate ratio median (10%–90% quantile) was 4.6 (1.0–12.8). Apart from the finding that older studies reported higher rates, other study features were not associated with significantly different rate distributions (e.g. overall quality, methods related to case finding, diagnostic confirmation and criteria, the use of age-standardization and age range). CONCLUSIONS: There is a wealth of data available on the incidence of schizophrenia. The width and skew of the rate distribution, and the significant impact of sex, urbanicity and migrant status on these distributions, indicate substantial variations in the incidence of schizophrenia

The mass and galaxy distribution around SZ-selected clusters

We present measurements of the radial profiles of the mass and galaxy number density around Sunyaev–Zel’dovich (SZ)-selected clusters using both weak lensing and galaxy counts. The clusters are selected from the Atacama Cosmology Telescope Data Release 5 and the galaxies from the Dark Energy Survey Year 3 data set. With signal-to-noise ratio of 62 (45) for galaxy (weak lensing) profiles over scales of about 0.2–20 h−1 Mpc, these are the highest precision measurements for SZ-selected clusters to date. Because SZ selection closely approximates mass selection, these measurements enable several tests of theoretical models of the mass and light distribution around clusters. Our main findings are: (1) The splashback feature is detected at a consistent location in both the mass and galaxy profiles and its location is consistent with predictions of cold dark matter N-body simulations. (2) The full mass profile is also consistent with the simulations. (3) The shapes of the galaxy and lensing profiles are remarkably similar for our sample over the entire range of scales, from well inside the cluster halo to the quasilinear regime. We measure the dependence of the profile shapes on the galaxy sample, redshift, and cluster mass. We extend the Diemer & Kravtsov model for the cluster profiles to the linear regime using perturbation theory and show that it provides a good match to the measured profiles. We also compare the measured profiles to predictions of the standard halo model and simulations that include hydrodynamics. Applications of these results to cluster mass estimation, cosmology, and astrophysics are discussed

A Deep Insight into the Sialotranscriptome of the Gulf Coast Tick, Amblyomma maculatum

Background: Saliva of blood sucking arthropods contains compounds that antagonize their hosts ’ hemostasis, which include platelet aggregation, vasoconstriction and blood clotting; saliva of these organisms also has anti-inflammatory and immunomodullatory properties. Perhaps because hosts mount an active immune response against these compounds, the diversity of these compounds is large even among related blood sucking species. Because of these properties, saliva helps blood feeding as well as help the establishment of pathogens that can be transmitted during blood feeding. Methodology/Principal Findings: We have obtained 1,626,969 reads by pyrosequencing a salivary gland cDNA library from adult females Amblyomma maculatum ticks at different times of feeding. Assembly of this data produced 72,441 sequences larger than 149 nucleotides from which 15,914 coding sequences were extracted. Of these, 5,353 had.75 % coverage to their best match in the non-redundant database from the National Center for Biotechnology information, allowing for the deposition of 4,850 sequences to GenBank. The annotated data sets are available as hyperlinked spreadsheets. Putative secreted proteins were classified in 133 families, most of which have no known function. Conclusions/Significance: This data set of proteins constitutes a mining platform for novel pharmacologically activ

The trans-ancestral genomic architecture of glycemic traits

Glycemic traits are used to diagnose and monitor type 2 diabetes and cardiometabolic health. To date, most genetic studies of glycemic traits have focused on individuals of European ancestry. Here we aggregated genome-wide association studies comprising up to 281,416 individuals without diabetes (30% non-European ancestry) for whom fasting glucose, 2-h glucose after an oral glucose challenge, glycated hemoglobin and fasting insulin data were available. Trans-ancestry and single-ancestry meta-analyses identified 242 loci (99 novel; P < 5 × 10−8), 80% of which had no significant evidence of between-ancestry heterogeneity. Analyses restricted to individuals of European ancestry with equivalent sample size would have led to 24 fewer new loci. Compared with single-ancestry analyses, equivalent-sized trans-ancestry fine-mapping reduced the number of estimated variants in 99% credible sets by a median of 37.5%. Genomic-feature, gene-expression and gene-set analyses revealed distinct biological signatures for each trait, highlighting different underlying biological pathways. Our results increase our understanding of diabetes pathophysiology by using trans-ancestry studies for improved power and resolution

Disentangling the heterogeneity of autism spectrum disorder through genetic findings

Autism spectrum disorder (ASD) represents a heterogeneous group of disorders, which presents a substantial challenge to diagnosis and treatment. Over the past decade, considerable progress has been made in the identification of genetic risk factors for ASD that define specific mechanisms and pathways underlying the associated behavioural deficits. In this Review, we discuss how some of the latest advances in the genetics of ASD have facilitated parsing of the phenotypic heterogeneity of this disorder. We argue that only through such advances will we begin to define endophenotypes that can benefit from targeted, hypothesis-driven treatments. We review the latest technologies used to identify and characterize the genetics underlying ASD and then consider three themes—single-gene disorders, the gender bias in ASD, and the genetics of neurological comorbidities—that highlight ways in which we can use genetics to define the many phenotypes within the autism spectrum. We also present current clinical guidelines for genetic testing in ASD and their implications for prognosis and treatment