Clustering by phenotype and genome-wide association study in autism

Seeing beyond words: Visualizing autism spectrum disorder biomarker insights

Objective

This study employs bibliometric and visual analysis to elucidate global research trends in Autism Spectrum Disorder (ASD) biomarkers, identify critical research focal points, and discuss the potential integration of diverse biomarker modalities for precise ASD assessment.

Methods

A comprehensive bibliometric analysis was conducted using data from the Web of Science Core Collection database until December 31, 2022. Visualization tools, including R, VOSviewer, CiteSpace, and gCLUTO, were utilized to examine collaborative networks, co-citation patterns, and keyword associations among countries, institutions, authors, journals, documents, and keywords.

Results

ASD biomarker research emerged in 2004, accumulating a corpus of 4348 documents by December 31, 2022. The United States, with 1574 publications and an H-index of 213, emerged as the most prolific and influential country. The University of California, Davis, contributed significantly with 346 publications and an H-index of 69, making it the leading institution. Concerning journals, the Journal of Autism and Developmental Disorders, Autism Research, and PLOS ONE were the top three publishers of ASD biomarker-related articles among a total of 1140 academic journals. Co-citation and keyword analyses revealed research hotspots in genetics, imaging, oxidative stress, neuroinflammation, gut microbiota, and eye tracking. Emerging topics included "DNA methylation," "eye tracking," "metabolomics," and "resting-state fMRI."

Conclusion

The field of ASD biomarker research is dynamically evolving. Future endeavors should prioritize individual stratification, methodological standardization, the harmonious integration of biomarker modalities, and longitudinal studies to advance the precision of ASD diagnosis and treatment.

Gene Discovery and Biological Insights into Anxiety Disorders from a Multi-Ancestry Genome-wide Association Study of >1.2 Million Participants

We leveraged information from more than 1.2 million participants to investigate the genetics of anxiety disorders across five continental ancestral groups. Ancestry-specific and cross-ancestry genome-wide association studies identified 51 anxiety-associated loci, 39 of which are novel. Additionally, polygenic risk scores derived from individuals of European descent were associated with anxiety in African, Admixed-American, and East Asian groups. The heritability of anxiety was enriched for genes expressed in the limbic system, the cerebral cortex, the cerebellum, the metencephalon, the entorhinal cortex, and the brain stem. Transcriptome- and proteome-wide analyses highlighted 115 genes associated with anxiety through brain-specific and cross-tissue regulation. We also observed global and local genetic correlations with depression, schizophrenia, and bipolar disorder and putative causal relationships with several physical health conditions. Overall, this study expands the knowledge regarding the genetic risk and pathogenesis of anxiety disorders, highlighting the importance of investigating diverse populations and integrating multi-omics information.

Investigating genotype-to-phenotype correlation in CHARGE syndrome by deep phenotyping and multiparametric clustering

CHARGE syndrome, due to CHD7 pathogenic variations, is an autosomal dominant disorder characterized by a large spectrum of severity. Despite the great number of variations reported, no clear genotype-to-phenotype correlation has been reported. Unsupervised machine learning and clustering was undertaken using a retrospective cohort of 42 patients, after deep radiologic and clinical phenotyping, to establish genotype-phenotype correlation for CHD7-related CHARGE syndrome. It resulted in three clusters showing phenotypes of different severities. While no clear genotype-phenotype correlation appeared within the first two clusters, a single patient was outlying the cohort data (cluster 3) with the most atypical phenotype and the most distal frameshift variant in the gene. We added two other patients with similar distal pathogenic variants and observed a tendency toward mild and/or atypical phenotypes. We hypothesized that this finding could potentially be related to escaping nonsense mediated RNA decay, but found no evidence of such decay in vivo for any of the CHD7 pathogenic variation tested. This indicates that this milder phenotype may rather result from the production of a protein retaining all functional domains.

Developing a cluster-based approach for deciphering complexity in individuals with neurodevelopmental differences

Objective

Individuals with neurodevelopmental disorders such as global developmental delay (GDD) present both genotypic and phenotypic heterogeneity. This diversity has hampered developing of targeted interventions given the relative rarity of each individual genetic etiology. Novel approaches to clinical trials where distinct, but related diseases can be treated by a common drug, known as basket trials, which have shown benefits in oncology but have yet to be used in GDD. Nonetheless, it remains unclear how individuals with GDD could be clustered. Here, we assess two different approaches: agglomerative and divisive clustering.

Methods

Using the largest cohort of individuals with GDD, which is the Deciphering Developmental Disorders (DDD), characterized using a systematic approach, we extracted genotypic and phenotypic information from 6,588 individuals with GDD. We then used a k-means clustering (divisive) and hierarchical agglomerative clustering (HAC) to identify subgroups of individuals. Next, we extracted gene network and molecular function information with regard to the clusters identified by each approach.

Results

HAC based on phenotypes identified in individuals with GDD revealed 16 clusters, each presenting with one dominant phenotype displayed by most individuals in the cluster, along with other minor phenotypes. Among the most common phenotypes reported were delayed speech, absent speech, and seizure. Interestingly, each phenotypic cluster molecularly included several (3-12) gene sub-networks of more closely related genes with diverse molecular function. k-means clustering also segregated individuals harboring those phenotypes, but the genetic pathways identified were different from the ones identified from HAC.

Conclusion

Our study illustrates how divisive (k-means) and agglomerative clustering can be used in order to group individuals with GDD for future basket trials. Moreover, the result of our analysis suggests that phenotypic clusters should be subdivided into molecular sub-networks for an increased likelihood of successful treatment. Finally, a combination of both agglomerative and divisive clustering may be required for developing of a comprehensive treatment.

Phenotypically driven subgroups of ASD display distinct metabolomic profiles

Autism Spectrum Disorder (ASD) is a heterogeneous condition that includes a broad range of characteristics and associated comorbidities; however, the biology underlying the variability in phenotypes is not well understood. As ASD impacts approximately 1 in 100 children globally, there is an urgent need to better understand the biological mechanisms that contribute to features of ASD. In this study, we leveraged rich phenotypic and diagnostic information related to ASD in 2001 individuals aged 4 to 17 years from the Simons Simplex Collection to derive phenotypically driven subgroups and investigate their respective metabolomes. We performed hierarchical clustering on 40 phenotypes spanning four ASD clinical domains, resulting in three subgroups with distinct phenotype patterns. Using global plasma metabolomic profiling generated by ultrahigh-performance liquid chromatography mass spectrometry, we characterized the metabolome of individuals in each subgroup to interrogate underlying biology related to the subgroups. Subgroup 1 included children with the least maladaptive behavioral traits (N = 862); global decreases in lipid metabolites and concomitant increases in amino acid and nucleotide pathways were observed for children in this subgroup. Subgroup 2 included children with the highest degree of challenges across all phenotype domains (N = 631), and their metabolome profiles demonstrated aberrant metabolism of membrane lipids and increases in lipid oxidation products. Subgroup 3 included children with maladaptive behaviors and co-occurring conditions that showed the highest IQ scores (N = 508); these individuals had increases in sphingolipid metabolites and fatty acid byproducts. Overall, these findings indicated distinct metabolic patterns within ASD subgroups, which may reflect the biological mechanisms giving rise to specific patterns of ASD characteristics. Our results may have important clinical applications relevant to personalized medicine approaches towards managing ASD symptoms.

Analysis and comparisons of gene expression changes in patient- derived neurons from ROHHAD, CCHS, and PWS

Background

Rapid-onset obesity with hypothalamic dysfunction, hypoventilation, and autonomic dysregulation (ROHHAD) syndrome is an ultra-rare neurocristopathy with no known genetic or environmental etiology. Rapid-onset obesity over a 3-12 month period with onset between ages 1.5-7 years of age is followed by an unfolding constellation of symptoms including severe hypoventilation that can lead to cardiorespiratory arrest in previously healthy children if not identified early and intervention provided. Congenital Central Hypoventilation syndrome (CCHS) and Prader-Willi syndrome (PWS) have overlapping clinical features with ROHHAD and known genetic etiologies. Here we compare patient neurons from three pediatric syndromes (ROHHAD, CCHS, and PWS) and neurotypical control subjects to identify molecular overlap that may explain the clinical similarities.

Methods

Dental pulp stem cells (DPSC) from neurotypical control, ROHHAD, and CCHS subjects were differentiated into neuronal cultures for RNA sequencing (RNAseq). Differential expression analysis identified transcripts variably regulated in ROHHAD and CCHS vs. neurotypical control neurons. In addition, we used previously published PWS transcript data to compare both groups to PWS patient-derived DPSC neurons. Enrichment analysis was performed on RNAseq data and downstream protein expression analysis was performed using immunoblotting.

Results

We identified three transcripts differentially regulated in all three syndromes vs. neurotypical control subjects. Gene ontology analysis on the ROHHAD dataset revealed enrichments in several molecular pathways that may contribute to disease pathology. Importantly, we found 58 transcripts differentially expressed in both ROHHAD and CCHS patient neurons vs. control neurons. Finally, we validated transcript level changes in expression of ADORA2A, a gene encoding for an adenosine receptor, at the protein level in CCHS neurons and found variable, although significant, changes in ROHHAD neurons.

Conclusions

The molecular overlap between CCHS and ROHHAD neurons suggests that the clinical phenotypes in these syndromes likely arise from or affect similar transcriptional pathways. Further, gene ontology analysis identified enrichments in ATPase transmembrane transporters, acetylglucosaminyltransferases, and phagocytic vesicle membrane proteins that may contribute to the ROHHAD phenotype. Finally, our data imply that the rapid-onset obesity seen in both ROHHAD and PWS likely arise from different molecular mechanisms. The data presented here describes important preliminary findings that warrant further validation.

Chromosomal Microarray in Patients with Non-Syndromic Autism Spectrum Disorders in the Clinical Routine of a Tertiary Hospital

Autism spectrum disorders (ASD) comprise a group of neurodevelopmental disorders (NDD) characterized by deficits in communication and social interaction, as well as repetitive and restrictive behaviors, etc. The genetic implications of ASD have been widely documented, and numerous genes have been associated with it. The use of chromosomal microarray analysis (CMA) has proven to be a rapid and effective method for detecting both small and large deletions and duplications associated with ASD. In this article, we present the implementation of CMA as a first-tier test in our clinical laboratory for patients with primary ASD over a prospective period of four years. The cohort was composed of 212 individuals over 3 years of age, who met DSM-5 diagnostic criteria for ASD. The use of a customized array-CGH (comparative genomic hybridization) design (KaryoArray®) found 99 individuals (45.20%) with copy number variants (CNVs); 34 of them carried deletions (34.34%) and 65 duplications (65.65%). A total of 28 of 212 patients had pathogenic or likely pathogenic CNVs, representing approximately 13% of the cohort. In turn, 28 out of 212 (approximately 12%) had variants of uncertain clinical significance (VUS). Our findings involve clinically significant CNVs, known to cause ASD (syndromic and non-syndromic), and other CNVs previously related to other comorbidities such as epilepsy or intellectual disability (ID). Lastly, we observed new rearrangements that will enhance the information available and the collection of genes associated with this disorder. Our data also highlight that CMA could be very useful in diagnosing patients with essential/primary autism, and demonstrate the existence of substantial genetic and clinical heterogeneity in non-syndromic ASD individuals, underscoring the continued challenge for genetic laboratories in terms of its molecular diagnosis.

A machine learning approach on whole blood immunomarkers to identify an inflammation-associated psychosis onset subgroup

Psychosis onset is a transdiagnostic event that leads to a range of psychiatric disorders, which are currently diagnosed through clinical observation. The integration of multimodal biological data could reveal different subtypes of psychosis onset to target for the personalization of care. In this study, we tested the existence of subgroups of patients affected by first-episode psychosis (FEP) with a possible immunopathogenic basis. To do this, we designed a data-driven unsupervised machine learning model to cluster a sample of 127 FEP patients and 117 healthy controls (HC), based on the peripheral blood expression levels of 12 psychosis-related immune gene transcripts. To validate the model, we applied a resampling strategy based on the half-splitting of the total sample with random allocation of the cases. Further, we performed a post-hoc univariate analysis to verify the clinical, cognitive, and structural brain correlates of the subgroups identified. The model identified and validated two distinct clusters: 1) a FEP cluster characterized by the high expression of inflammatory and immune-activating genes (IL1B, CCR7, IL12A and CXCR3); 2) a cluster consisting of an equal number of FEP and HC subjects, which did not show a relative over or under expression of any immune marker (balanced subgroup). None of the subgroups was related to specific symptoms dimensions or longitudinal diagnosis of affective vs non-affective psychosis. FEP patients included in the balanced immune subgroup showed a thinning of the left supramarginal and superiorfrontal cortex (FDR-adjusted p-values < 0.05). Our results demonstrated the existence of a FEP patients' subgroup identified by a multivariate pattern of immunomarkers involved in inflammatory activation. This evidence may pave the way to sample stratification in clinical studies aiming to develop diagnostic tools and therapies targeting specific immunopathogenic pathways of psychosis.

Translating precision medicine for autism spectrum disorder: A pressing need

Autism spectrum disorder (ASD) is a heterogenous group of neurodevelopmental disorders (NDDs) with a high unmet medical need. Currently, ASD is diagnosed according to behavior-based criteria that overlook clinical and genomic heterogeneity, thus repeatedly resulting in failed clinical trials. Here, we summarize the scientific evidence pointing to the pressing need to create a precision medicine framework for ASD and other NDDs. We discuss the role of omics and systems biology to characterize more homogeneous disease subtypes with different underlying pathophysiological mechanisms and to determine corresponding tailored treatments. Finally, we provide recent initiatives towards tackling the complexity in NDDs for precision medicine and cost-effective drug discovery.

Connecting phenotype to genotype: PheWAS-inspired analysis of autism spectrum disorder

Autism Spectrum Disorder (ASD) is extremely heterogeneous clinically and genetically. There is a pressing need for a better understanding of the heterogeneity of ASD based on scientifically rigorous approaches centered on systematic evaluation of the clinical and research utility of both phenotype and genotype markers. This paper presents a holistic PheWAS-inspired method to identify meaningful associations between ASD phenotypes and genotypes. We generate two types of phenotype-phenotype (p-p) graphs: a direct graph that utilizes only phenotype data, and an indirect graph that incorporates genotype as well as phenotype data. We introduce a novel methodology for fusing the direct and indirect p-p networks in which the genotype data is incorporated into the phenotype data in varying degrees. The hypothesis is that the heterogeneity of ASD can be distinguished by clustering the p-p graph. The obtained graphs are clustered using network-oriented clustering techniques, and results are evaluated. The most promising clusterings are subsequently analyzed for biological and domain-based relevance. Clusters obtained delineated different aspects of ASD, including differentiating ASD-specific symptoms, cognitive, adaptive, language and communication functions, and behavioral problems. Some of the important genes associated with the clusters have previous known associations to ASD. We found that clusters based on integrated genetic and phenotype data were more effective at identifying relevant genes than clusters constructed from phenotype information alone. These genes included five with suggestive evidence of ASD association and one known to be a strong candidate.

Extensive genome introgression between domestic ferret and European polecat during population recovery in Great Britain

The European polecat (Mustela putorius) is a mammalian predator which occurs across much of Europe east to the Ural Mountains. In Great Britain, following years of persecution the range of the European polecat contracted and by the early 1900s was restricted to unmanaged forests of central Wales. The European polecat has recently undergone a population increase due to legal protection and its range now overlaps that of feral domestic ferrets (Mustela putorius furo). During this range expansion, European polecats hybridized with feral domestic ferrets producing viable offspring. Here, we carry out population-level whole-genome sequencing on 8 domestic ferrets, 19 British European polecats, and 15 European polecats from the European mainland. We used a range of population genomics methods to examine the data, including phylogenetics, phylogenetic graphs, model-based clustering, phylogenetic invariants, ABBA-BABA tests, topology weighting, and Fst. We found high degrees of genome introgression in British polecats outside their previous stronghold, even in those individuals phenotyped as “pure” polecats. These polecats ranged from presumed F1 hybrids (gamma = 0.53) to individuals that were much less introgressed (gamma = 0.2). We quantify this introgression and find introgressed genes containing Fst outliers associated with cognitive function and sight.

Migrating Pyramidal Neurons Require DSCAM to Bypass the Border of the Developing Cortical Plate

During mammalian neocortex development, nascent pyramidal neurons migrate along radial glial cells and overtake earlier-born neurons to terminate at the front of the developing cortical plate (CP), leading to the outward expansion of the CP border. While much has been learned about the cellular and molecular mechanisms that underlie the migration of pyramidal neurons, how migrating neurons bypass the preceding neurons at the end of migration to reach their final positions remains poorly understood. Here, we report that Down syndrome cell adhesion molecule (DSCAM) is required for migrating neurons to bypass their postmigratory predecessors during the expansion of the upper cortical layers. DSCAM is a type I transmembrane cell adhesion molecule. It has been linked to Down syndrome through its location on Chromosome 21 trisomy and to autism spectrum disorders through loss-of-function mutations. Ex vivo time-lapse imaging demonstrates that DSCAM is required for migrating neurons to bypass their postmigratory predecessors, crossing the CP border to expand the upper cortical layers. In DSCAM-deficient cortices, migrating neurons stop prematurely under the CP border, leading to thinner upper cortical layers with higher neuronal density. We further show that DSCAM weakens cell adhesion mediated by N-cadherin in the upper cortical plate, allowing migrating neurons to traverse the CP border and expand the CP. These findings suggest that DSCAM is required for proper migratory termination and final positioning of nascent pyramidal neurons, which may provide insight into brain disorders that exhibit thinner upper layers of the cerebral cortex without neuronal loss.SIGNIFICANCE STATEMENT Newly born neurons in the developing mammalian neocortex migrate outward toward the cortical surface, bypassing earlier born neurons to expand the developing cortex. How migrating neurons bypass the preceding neurons and terminate at the front of the expanding cortex remains poorly understood. We demonstrate that Down syndrome cell adhesion molecule (DSCAM), linked to Down syndrome and autism spectrum disorder, is required by migrating neurons to bypass their postmigratory predecessors and terminate migration in the outwardly expanding cortical layer. Migrating neurons deficient in DSCAM stop prematurely, failing to expand the cortex. We further show that DSCAM likely mediates migratory termination by weakening cell adhesion mediated by N-cadherin.

The rs7404339 AA Genotype in CDH5 Contributes to Increased Risks of Kawasaki Disease and Coronary Artery Lesions in a Southern Chinese Child Population

Background

Kawasaki disease (KD) is an acute, self-limited febrile illness of unknown cause. And it predominantly affects children <5 years and the main complication is coronary artery lesion (CAL). Studies demonstrated that vascular endothelial cells (VECs) played a very important role in the CAL of KD. VE-cad encoded by CDH5 may exert a relevant role in endothelial cell biology through controlling the cohesion of the intercellular junctions. The pathogenesis of KD remains unclear and genetic factors may increase susceptibility of KD. However, the relationship between CDH5 polymorphisms and KD susceptibility has not been reported before. The present study is aimed at investigating whether the rs7404339 polymorphism in CDH5 is associated with KD susceptibility and CAL in a southern Chinese child population.

Methods and Results

We recruited 1,335 patients with KD and 1,669 healthy children. Each participant had supplied 2 mL of fresh blood in the clinical biologic bank at our hospital for other studies. Multiplex PCR is used to assess the genotypes of rs7404339 polymorphism in CDH5. According to the results, we found significant correlated relationship between rs7404339 polymorphism in CDH5 and KD susceptibility [AA vs. GG: adjusted odds ratio (OR) = 1.43, 95% confidence interval (CI) = 1.00-2.05; p = 0.0493; recessive model: adjusted OR = 1.44, 95% CI = 1.01-2.06, P = 0.0431]. In further stratified analysis, we found that children younger than 60 months (adjusted OR = 1.46, 95% CI = 1.01-2.10; p = 0.0424) and male (adjusted OR = 1.70, 95% CI = 1.09-2.65; p = 0.0203) with the rs7404339 AA genotype in CDH5 had a higher risk of KD than carriers of the GA/GG genotype. Furthermore, stratification analysis revealed that patients with the rs7404339 AA genotype exhibited the significantly higher onset risk for CAL than carriers of the GA/GG genotype (adjusted age and gender odds ratio = 1.56, 95% CI = 1.01-2.41; P = 0.0433).

Conclusion

Our results showed that rs7404339 AA genotype in CDH5 is significant associated with KD susceptibility. And children younger than 60 months and male with the rs7404339 AA genotype had a higher risk of KD than carriers with the GA/GG genotype. Furthermore, patients with the rs7404339 AA genotype exhibited a significantly higher risk of CAL complication than carriers of the GA/GG genotype.

Analysis of recent shared ancestry in a familial cohort identifies coding and noncoding autism spectrum disorder variants

Autism spectrum disorder (ASD) is a collection of neurodevelopmental disorders characterized by deficits in social communication and restricted, repetitive patterns of behavior or interests. ASD is highly heritable, but genetically and phenotypically heterogeneous, reducing the power to identify causative genes. We performed whole genome sequencing (WGS) in an ASD cohort of 68 individuals from 22 families enriched for recent shared ancestry. We identified an average of 3.07 million variants per genome, of which an average of 112,512 were rare. We mapped runs of homozygosity (ROHs) in affected individuals and found an average genomic homozygosity of 9.65%, consistent with expectations for multiple generations of consanguineous unions. We identified potentially pathogenic rare exonic or splice site variants in 12 known (including KMT2C, SCN1A, SPTBN1, SYNE1, ZNF292) and 12 candidate (including CHD5, GRB10, PPP1R13B) ASD genes. Furthermore, we annotated noncoding variants in ROHs with brain-specific regulatory elements and identified putative disease-causing variants within brain-specific promoters and enhancers for 5 known ASD and neurodevelopmental disease genes (ACTG1, AUTS2, CTNND2, CNTNAP4, SPTBN4). We also identified copy number variants in two known ASD and neurodevelopmental disease loci in two affected individuals. In total we identified potentially etiological variants in known ASD or neurodevelopmental disease genes for ~61% (14/23) of affected individuals. We combined WGS with homozygosity mapping and regulatory element annotations to identify candidate ASD variants. Our analyses add to the growing number of ASD genes and variants and emphasize the importance of leveraging recent shared ancestry to map disease variants in complex neurodevelopmental disorders.

Computational Genomics in the Era of Precision Medicine: Applications to Variant Analysis and Gene Therapy

Rapid methodological advances in statistical and computational genomics have enabled researchers to better identify and interpret both rare and common variants responsible for complex human diseases. As we continue to see an expansion of these advances in the field, it is now imperative for researchers to understand the resources and methodologies available for various data types and study designs. In this review, we provide an overview of recent methods for identifying rare and common variants and understanding their roles in disease etiology. Additionally, we discuss the strategy, challenge, and promise of gene therapy. As computational and statistical approaches continue to improve, we will have an opportunity to translate human genetic findings into personalized health care.

Multi-faceted semantic clustering with text-derived phenotypes

Identification of ontology concepts in clinical narrative text enables the creation of phenotype profiles that can be associated with clinical entities, such as patients or drugs. Constructing patient phenotype profiles using formal ontologies enables their analysis via semantic similarity, in turn enabling the use of background knowledge in clustering or classification analyses. However, traditional semantic similarity approaches collapse complex relationships between patient phenotypes into a unitary similarity scores for each pair of patients. Moreover, single scores may be based only on matching terms with the greatest information content (IC), ignoring other dimensions of patient similarity. This process necessarily leads to a loss of information in the resulting representation of patient similarity, and is especially apparent when using very large text-derived and highly multi-morbid phenotype profiles. Moreover, it renders finding a biological explanation for similarity very difficult; the black box problem. In this article, we explore the generation of multiple semantic similarity scores for patients based on different facets of their phenotypic manifestation, which we define through different sub-graphs in the Human Phenotype Ontology. We further present a new methodology for deriving sets of qualitative class descriptions for groups of entities described by ontology terms. Leveraging this strategy to obtain meaningful explanations for our semantic clusters alongside other evaluation techniques, we show that semantic clustering with ontology-derived facets enables the representation, and thus identification of, clinically relevant phenotype relationships not easily recoverable using overall clustering alone. In this way, we demonstrate the potential of faceted semantic clustering for gaining a deeper and more nuanced understanding of text-derived patient phenotypes.

EWAS of Monozygotic Twins Implicate a Role of mTOR Pathway in Pathogenesis of Tic Spectrum Disorder

Tic spectrum disorder (TSD) is an umbrella term which includes Gilles de la Tourette syndrome (GTS) and chronic tic disorder (CTD). They are considered highly heritable, yet the genetic components remain largely unknown. In this study we aimed to investigate disease-associated DNA methylation differences to identify genes and pathways which may be implicated in TSD aetiology. For this purpose, we performed an exploratory analysis of the genome-wide DNA methylation patterns in whole blood samples of 16 monozygotic twin pairs, of which eight were discordant and six concordant for TSD, while two pairs were asymptomatic. Although no sites reached genome-wide significance, we identified several sites and regions with a suggestive significance, which were located within or in the vicinity of genes with biological functions associated with neuropsychiatric disorders. The two top genes identified (TSC1 and CRYZ/TYW3) and the enriched pathways and components (phosphoinosides and PTEN pathways, and insulin receptor substrate binding) are related to, or have been associated with, the PI3K/AKT/mTOR pathway. Genes in this pathway have previously been associated with GTS, and mTOR signalling has been implicated in a range of neuropsychiatric disorders. It is thus possible that altered mTOR signalling plays a role in the complex pathogenesis of TSD.

Text mining of gene-phenotype associations reveals new phenotypic profiles of autism-associated genes

Autism is a spectrum disorder with wide variation in type and severity of symptoms. Understanding gene-phenotype associations is vital to unravel the disease mechanisms and advance its diagnosis and treatment. To date, several databases have stored a large portion of gene-phenotype associations which are mainly obtained from genetic experiments. However, a large proportion of gene-phenotype associations are still buried in the autism-related literature and there are limited resources to investigate autism-associated gene-phenotype associations. Given the abundance of the autism-related literature, we were thus motivated to develop Autism_genepheno, a text mining pipeline to identify sentence-level mentions of autism-associated genes and phenotypes in literature through natural language processing methods. We have generated a comprehensive database of gene-phenotype associations in the last five years' autism-related literature that can be easily updated as new literature becomes available. We have evaluated our pipeline through several different approaches, and we are able to rank and select top autism-associated genes through their unique and wide spectrum of phenotypic profiles, which could provide a unique resource for the diagnosis and treatment of autism. The data resources and the Autism_genpheno pipeline are available at: https://github.com/maiziezhoulab/Autism_genepheno .

Alternative Promoter Use Governs the Expression of IgLON Cell Adhesion Molecules in Histogenetic Fields of the Embryonic Mouse Brain

The members of the IgLON superfamily of cell adhesion molecules facilitate fundamental cellular communication during brain development, maintain functional brain circuitry, and are associated with several neuropsychiatric disorders such as depression, autism, schizophrenia, and intellectual disabilities. Usage of alternative promoter-specific 1a and 1b mRNA isoforms in Lsamp, Opcml, Ntm, and the single promoter of Negr1 in the mouse and human brain has been previously described. To determine the precise spatiotemporal expression dynamics of Lsamp, Opcml, Ntm isoforms, and Negr1, in the developing brain, we generated isoform-specific RNA probes and carried out in situ hybridization in the developing (embryonic, E10.5, E11.5, 13.5, 17; postnatal, P0) and adult mouse brains. We show that promoter-specific expression of IgLONs is established early during pallial development (at E10.5), where it remains throughout its differentiation through adulthood. In the diencephalon, midbrain, and hindbrain, strong expression patterns are initiated a few days later and begin fading after birth, being only faintly expressed during adulthood. Thus, the expression of specific IgLONs in the developing brain may provide the means for regionally specific functionality as well as for specific regional vulnerabilities. The current study will therefore improve the understanding of how IgLON genes are implicated in the development of neuropsychiatric disorders.