A comparative study of the genetic components of three subcategories of autism spectrum disorder

A novel framework for functional annotation of variants of uncertain significance in ID/ASD risk gene CC2D1A

Intellectual disability (ID) and autism spectrum disorder (ASD) are genetically heterogeneous with hundreds of identified risk genes, most affecting only a few patients. Novel missense variants in these genes are being discovered as clinical exome sequencing is now routinely integrated into diagnosis, yet most of them are annotated as variants of uncertain significance (VUS). VUSs are a major roadblock in using patient genetics to inform clinical action. We developed a framework to characterize VUSs in Coiled-coil and C2 domain containing 1A (CC2D1A), a gene causing autosomal recessive ID with comorbid ASD in 40% of cases. We analyzed seven VUSs (p.Pro319Leu, p.Ser327Leu, p.Gly441Val, p.Val449Met, p.Thr580Ile, p.Arg886His and p.Glu910Lys) from four cases of individuals with ID and ASD. Variants were cloned and overexpressed in HEK293 individually and in their respective heterozygous combination. CC2D1A is a signaling scaffold that positively regulates PKA-CREB signaling by repressing phosphodiesterase 4D (PDE4D) to prevent cAMP degradation. After testing multiple parameters including direct interaction between PDE4D and CC2D1A, cAMP levels and CREB activation, we found that the most sensitive readout was CREB transcriptional activity using a luciferase assay. Compared to WT CC2D1A, five VUSs (p.Pro319Leu, p.Gly441Val, p.Val449Met, p.Thr580Ile, and p.Arg886His) led to significantly blunted response to forskolin induced CREB activation. This luciferase assay approach can be scaled up to annotate ~150 CC2D1A VUSs that are currently listed in ClinVar. Since CREB activation is a common denominator for multiple ASD/ID genes, our paradigm can also be adapted for their VUSs.

Shared and divergent contribution of vitamin A and oxytocin to the aetiology of autism spectrum disorder

Rare genetic variations contribute to the heterogeneity of autism spectrum disorder (ASD) and the responses to various interventions for ASD probands. However, the associated molecular underpinnings remain unclear. Herein, we estimated the association between rare genetic variations in 410 vitamin A (VA)-related genes (VARGs) and ASD aetiology using publicly available de novo mutations (DNMs), rare inherited variants, and copy number variations (CNVs) from about 50,000 ASD probands and 20,000 normal controls (discovery and validation cohorts). Additionally, given the functional relevance of VA and oxytocin, we systematically compared the similarities and differences between VA and oxytocin with respect to ASD aetiology and evaluated their potential for clinical applications. Functional DNMs and pathogenic CNVs in VARGs contributed to ASD pathogenesis in the discovery and validation cohorts. Additionally, 324 potential VA-related biomarkers were identified, 243 of which were shared with previously identified oxytocin-related biomarkers, while 81 were unique VA biomarkers. Moreover, multivariable logistic regression analysis revealed that both VA- and oxytocin-related biomarkers were able to predict ASD aetiology for individuals carrying functional DNM in corresponding biomarkers with an average precision of 0.94. As well as, convergent and divergent functions were also identified between VA- and oxytocin-related biomarkers. The findings of this study provide a basis for future studies aimed at understanding the pathophysiological mechanisms underlying ASD while also defining a set of potential molecular biomarkers for adjuvant diagnosis and intervention in ASD.

Analysis of common genetic variation across targets of microRNAs dysregulated both in ASD and epilepsy reveals negative correlation

Genetic overlap involving rare disrupting mutations may contribute to high comorbidity rates between autism spectrum disorders and epilepsy. Despite their polygenic nature, genome-wide association studies have not reported a significant contribution of common genetic variation to comorbidity between both conditions. Analysis of common genetic variation affecting specific shared pathways such as miRNA dysregulation could help to elucidate the polygenic mechanisms underlying comorbidity between autism spectrum disorders and epilepsy. We evaluated here the role of common predisposing variation to autism spectrum disorders and epilepsy across target genes of 14 miRNAs selected through bibliographic research as being dysregulated in both disorders. We considered 4,581 target genes from various in silico sources. We described negative genetic correlation between autism spectrum disorders and epilepsy across variants located within target genes of the 14 miRNAs selected (p = 0.0228). Moreover, polygenic transmission disequilibrium test on an independent cohort of autism spectrum disorders trios (N = 233) revealed an under-transmission of autism spectrum disorders predisposing alleles within miRNAs’ target genes across autism spectrum disorders trios without comorbid epilepsy, thus reinforcing the negative relationship at the common genetic variation between both traits. Our study provides evidence of a negative relationship between autism spectrum disorders and epilepsy at the common genetic variation level that becomes more evident when focusing on the miRNA regulatory networks, which contrasts with observed clinical comorbidity and results from rare variation studies. Our findings may help to conceptualize the genetic heterogeneity and the comorbidity with epilepsy in autism spectrum disorders.

Integrative analysis prioritised oxytocin-related biomarkers associated with the aetiology of autism spectrum disorder

Background

Autism spectrum disorder (ASD) is a neurodevelopmental disorder with high phenotypic and genetic heterogeneity. The common variants of specific oxytocin-related genes (OTRGs), particularly OXTR, are associated with the aetiology of ASD. The contribution of rare genetic variations in OTRGs to ASD aetiology remains unclear.

Methods

We catalogued publicly available de novo mutations (DNMs) [from 6,511 patients with ASD and 3,391 controls], rare inherited variants (RIVs) [from 1,786 patients with ASD and 1,786 controls], and both de novo copy number variations (dnCNVs) and inherited CNVs (ihCNVs) [from 15,581 patients with ASD and 6,017 controls] in 963 curated OTRGs to explore their contribution to ASD pathology, respectively. Finally, a combined model was designed to prioritise the contribution of each gene to ASD aetiology by integrating DNMs and CNVs.

Findings

The rare genetic variations of OTRGs were significantly associated with ASD aetiology, in the order of dnCNVs > ihCNVs > DNMs. Furthermore, 172 OTRGs and their connected 286 ASD core genes were prioritised to positively contribute to ASD aetiology, including top-ranked MAPK3. Probands carrying rare disruptive variations in these genes were estimated to account for 10∼11% of all ASD probands.

Interpretation

Our findings suggest that rare disruptive variations in 172 OTRGs and their connected 286 ASD core genes are associated with ASD aetiology and may be potential biomarkers predicting the effects of oxytocin treatment.

Funding

Guangdong Key Project, National Natural Science Foundation of China, Key Laboratory of Clinical Laboratory Diagnosis and Translational Research of Zhejiang Province.

Cross-Disorder Analysis of De Novo Mutations in Neuropsychiatric Disorders

The clinical similarity among different neuropsychiatric disorders (NPDs) suggested a shared genetic basis. We catalogued 23,109 coding de novo mutations (DNMs) from 6511 patients with autism spectrum disorder (ASD), 4,293 undiagnosed developmental disorder (UDD), 933 epileptic encephalopathy (EE), 1022 intellectual disability (ID), 1094 schizophrenia (SCZ), and 3391 controls. We evaluated that putative functional DNMs contribute to 38.11%, 34.40%, 33.31%, 10.98% and 6.91% of patients with ID, EE, UDD, ASD and SCZ, respectively. Consistent with phenotype similarity and heterogeneity in different NPDs, they show different degree of genetic association. Cross-disorder analysis of DNMs prioritized 321 candidate genes (FDR < 0.05) and showed that genes shared in more disorders were more likely to exhibited specific expression pattern, functional pathway, genetic convergence, and genetic intolerance.

Distinct effect of prenatal and postnatal brain expression across 20 brain disorders and anthropometric social traits: a systematic study of spatiotemporal modularity

Different spatiotemporal abnormalities have been implicated in different neuropsychiatric disorders and anthropometric social traits, yet an investigation in the temporal network modularity with brain tissue transcriptomics has been lacking. We developed a supervised network approach to investigate the genome-wide association study (GWAS) results in the spatial and temporal contexts and demonstrated it in 20 brain disorders and anthropometric social traits. BrainSpan transcriptome profiles were used to discover significant modules enriched with trait susceptibility genes in a developmental stage-stratified manner. We investigated whether, and in which developmental stages, GWAS-implicated genes are coordinately expressed in brain transcriptome. We identified significant network modules for each disorder and trait at different developmental stages, providing a systematic view of network modularity at specific developmental stages for a myriad of brain disorders and traits. Specifically, we observed a strong pattern of the fetal origin for most psychiatric disorders and traits [such as schizophrenia (SCZ), bipolar disorder, obsessive-compulsive disorder and neuroticism], whereas increased co-expression activities of genes were more strongly associated with neurological diseases [such as Alzheimer's disease (AD) and amyotrophic lateral sclerosis] and anthropometric traits (such as college completion, education and subjective well-being) in postnatal brains. Further analyses revealed enriched cell types and functional features that were supported and corroborated prior knowledge in specific brain disorders, such as clathrin-mediated endocytosis in AD, myelin sheath in multiple sclerosis and regulation of synaptic plasticity in both college completion and education. Our study provides a landscape view of the spatiotemporal features in a myriad of brain-related disorders and traits.

Targeted sequencing and integrative analysis to prioritize candidate genes in neurodevelopmental disorders

Neurodevelopmental disorders (NDDs) are a group of diseases characterized by high heterogeneity and frequently co-occurring symptoms. The mutational spectrum in patients with NDDs is largely incomplete. Here, we sequenced 547 genes from 1102 patients with NDDs and validated 1271 potential functional variants, including 108 de novo variants (DNVs) in 78 autosomal genes and seven inherited hemizygous variants in six X chromosomal genes. Notably, 36 of these 78 genes are the first to be reported in Chinese patients with NDDs. By integrating our genetic data with public data, we prioritized 212 NDD candidate genes with FDR < 0.1, including 17 novel genes. The novel candidate genes interacted or were co-expressed with known candidate genes, forming a functional network involved in known pathways. We highlighted MSL2, which carried two de novo protein-truncating variants (p.L192Vfs*3 and p.S486Ifs*11) and was frequently connected with known candidate genes. This study provides the mutational spectrum of NDDs in China and prioritizes 212 NDD candidate genes for further functional validation and genetic counseling.

Targeted sequencing and integrative analysis of 3,195 Chinese patients with neurodevelopmental disorders prioritized 26 novel candidate genes

Neurodevelopmental disorders (NDDs) are a set of complex disorders characterized by diverse and co-occurring clinical symptoms. The genetic contribution in patients with NDDs remains largely unknown. Here, we sequence 519 NDD-related genes in 3,195 Chinese probands with neurodevelopmental phenotypes and identify 2,522 putative functional mutations consisting of 137 de novo mutations (DNMs) in 86 genes and 2,385 rare inherited mutations (RIMs) with 22 X-linked hemizygotes in 13 genes, 2 homozygous mutations in 2 genes and 23 compound heterozygous mutations in 10 genes. Furthermore, the DNMs of 16,807 probands with NDDs are retrieved from public datasets and combine in an integrated analysis with the mutation data of our Chinese NDD probands by taking 3,582 in-house controls of Chinese origin as background. We prioritize 26 novel candidate genes. Notably, six of these genes - ITSN1, UBR3, CADM1, RYR3, FLNA, and PLXNA3 - preferably contribute to autism spectrum disorders (ASDs), as demonstrated by high co-expression and/or interaction with ASD genes confirmed via rescue experiments in a mouse model. Importantly, these genes are differentially expressed in the ASD cortex in a significant manner and involved in ASD-associated networks. Together, our study expands the genetic spectrum of Chinese NDDs, further facilitating both basic and translational research.

Interpretable Machine Learning Reveals Dissimilarities Between Subtypes of Autism Spectrum Disorder

Autism spectrum disorder (ASD) is a heterogeneous neuropsychiatric disorder with a complex genetic background. Analysis of altered molecular processes in ASD patients requires linear and nonlinear methods that provide interpretable solutions. Interpretable machine learning provides legible models that allow explaining biological mechanisms and support analysis of clinical subgroups. In this work, we investigated several case-control studies of gene expression measurements of ASD individuals. We constructed a rule-based learning model from three independent datasets that we further visualized as a nonlinear gene-gene co-predictive network. To find dissimilarities between ASD subtypes, we scrutinized a topological structure of the network and estimated a centrality distance. Our analysis revealed that autism is the most severe subtype of ASD, while pervasive developmental disorder-not otherwise specified and Asperger syndrome are closely related and milder ASD subtypes. Furthermore, we analyzed the most important ASD-related features that were described in terms of gene co-predictors. Among others, we found a strong co-predictive mechanism between EMC4 and TMEM30A, which may suggest a co-regulation between these genes. The present study demonstrates the potential of applying interpretable machine learning in bioinformatics analyses. Although the proposed methodology was designed for transcriptomics data, it can be applied to other omics disciplines.

Identification of a β-Arrestin 2 Mutation Related to Autism by Whole-Exome Sequencing

Autism spectrum disorder (ASD) is a complex neurological disease characterized by impaired social communication and interaction skills, rigid behavior, decreased interest, and repetitive activities. The disease has a high degree of genetic heterogeneity, and the genetic cause of ASD in many autistic individuals is currently unclear. In this study, we report a patient with ASD whose clinical features included social interaction disorder, communication disorder, and repetitive behavior. We examined the patient's genetic variation using whole-exome sequencing technology and found new de novo mutations. After analysis and evaluation, ARRB2 was identified as a candidate gene. To study the potential contribution of the ARRB2 gene to the human brain development and function, we first evaluated the expression profile of this gene in different brain regions and developmental stages. Then, we used weighted gene coexpression network analysis to analyze the associations between ARRB2 and ASD risk genes. Additionally, the spatial conformation and stability of the ARRB2 wild type and mutant proteins were examined by simulations. Then, we further established a mouse model of ASD. The results showed abnormal ARRB2 expression in the mouse ASD model. Our study showed that ARRB2 may be a risk gene for ASD, but the contribution of de novo ARRB2 mutations to ASD is unclear. This information will provide references for the etiology of ASD and aid in the mechanism-based drug development and treatment.

Functional relationships between recessive inherited genes and genes with de novo variants in autism spectrum disorder

Background

Both de novo variants and recessive inherited variants were associated with autism spectrum disorder (ASD). This study aimed to use exome data to prioritize recessive inherited genes (RIGs) with biallelically inherited variants in autosomes or X-linked inherited variants in males and investigate the functional relationships between RIGs and genes with de novo variants (DNGs).

Methods

We used a bioinformatics pipeline to analyze whole-exome sequencing data from 1799 ASD quads (containing one proband, one unaffected sibling, and their parents) from the Simons Simplex Collection and prioritize candidate RIGs with rare biallelically inherited variants in autosomes or X-linked inherited variants in males. The relationships between RIGs and DNGs were characterized based on different genetic perspectives, including genetic variants, functional networks, and brain expression patterns.

Results

Among the biallelically or hemizygous constrained genes that were expressed in the brain, ASD probands carried significantly more biallelically inherited protein-truncating variants (PTVs) in autosomes (p = 0.038) and X-linked inherited PTVs in males (p = 0.026) than those in unaffected siblings. We prioritized eight autosomal, and 13 X-linked candidate RIGs, including 11 genes already associated with neurodevelopmental disorders. In total, we detected biallelically inherited variants or X-linked inherited variants of these 21 candidate RIGs in 26 (1.4%) of 1799 probands. We then integrated previously reported known or candidate genes in ASD, ultimately obtaining 70 RIGs and 87 DNGs for analysis. We found that RIGs were less likely to carry multiple recessive inherited variants than DNGs were to carry multiple de novo variants. Additionally, RIGs and DNGs were significantly co-expressed and interacted with each other, forming a network enriched in known functional ASD clusters, although RIGs were less likely to be enriched in these functional clusters compared with DNGs. Furthermore, although RIGs and DNGs presented comparable expression patterns in the human brain, RIGs were less likely to be associated with prenatal brain regions, the middle cortical layers, and excitatory neurons than DNGs.

Limitations

The RIGs analyzed in this study require functional validation, and the results should be replicated in more patients with ASD.

Conclusions

ASD RIGs were functionally associated with DNGs; however, they exhibited higher heterogeneity than DNGs.

STRAP as a New Therapeutic Target for Poor Prognosis of Pancreatic Ductal Adenocarcinoma Patients Mainly Caused by TP53 Mutation

Pancreatic ductal adenocarcinoma (PDAC) has a high mortality rate and poor prognosis. KRAS, TP53, CDKN2A, and SMAD4 are driver genes of PDAC and 30-75% patients have mutations in at least two of these four genes. Herein, we analyzed the relationship between these genes and prognosis of 762 patients in the absence of coexisting mutations, using data from three independent public datasets. Interestingly, we found that compared with mutations in other driver genes, TP53 mutation plays a significant role in leading to poor prognosis of PDAC. Additionally, we found that snoRNA-mediated rRNA maturation was responsible for the progression of cancer in PDAC patients with TP53 mutations. Inhibition of STRAP, which regulates the localization of SMN complexes and further affects the assembly of snoRNP, can effectively reduce maturation of rRNA and significantly suppress progression of TP53-mutant or low p53 expression pancreatic cancer cells in vitro and in vivo. Our study highlighted the actual contribution rate of driver genes to patient prognosis, enriching traditional understanding of the relationship between these genes and PDAC. We also provided a possible mechanism and a new target to combat progression of TP53-mutant PDAC patients.

Psychiatric comorbidities in Asperger syndrome are related with polygenic overlap and differ from other Autism subtypes

There is great phenotypic heterogeneity within autism spectrum disorders (ASD), which has led to question their classification into a single diagnostic category. The study of the common genetic variation in ASD has suggested a greater contribution of other psychiatric conditions in Asperger syndrome (AS) than in the rest of the DSM-IV ASD subtypes (Non_AS). Here, using available genetic data from previously performed genome-wide association studies (GWAS), we aimed to study the genetic overlap between five of the most related disorders (schizophrenia (SCZ), major depression disorder (MDD), attention deficit hyperactivity disorder (ADHD), obsessive-compulsive disorders (OCD) and anxiety (ANX)), and AS, comparing it with the overlap in Non_AS subtypes. A Spanish cohort of autism trios (N = 371) was exome sequenced as part of the Autism Sequencing Consortium (ASC) and 241 trios were extensively characterized to be diagnosed with AS following DSM-IV and Gillberg's criteria (N = 39) or not (N = 202). Following exome imputation, polygenic risk scores (PRS) were calculated for ASD, SCZ, ADHD, MDD, ANX, and OCD (from available summary data from Psychiatric Genomic Consortium (PGC) repository) in the Spanish trios' cohort. By using polygenic transmission disequilibrium test (pTDT), we reported that risk for SCZ (P_scz= 0.008, corrected-P_SCZ = 0.0409), ADHD (P_ADHD = 0.021, corrected-P_ADHD = 0.0301), and MDD (P_MDD = 0.039, corrected-P_MDD = 0.0501) is over-transmitted to children with AS but not to Non_AS. Indeed, agnostic clustering procedure with deviation values from pTDT tests suggested two differentiated clusters of subjects, one of which is significantly enriched in AS (P = 0.025). Subsequent analysis with S-Predixcan, a recently developed software to predict gene expression from genotype data, revealed a clear pattern of correlation between cortical gene expression in ADHD and AS (P < 0.001) and a similar strong correlation pattern between MDD and AS, but also extendable to another non-brain tissue such as lung (P < 0.001). Altogether, these results support the idea of AS being qualitatively distinct from Non_AS autism and consistently evidence the genetic overlap between AS and ADHD, MDD, or SCZ.

Relationship between Vitamin Deficiencies and Co-Occurring Symptoms in Autism Spectrum Disorder

Recently, connections have been made between feeding and eating problems and autism spectrum disorder (ASD) and between autism pathophysiology and diet issues. These could explain some of the mechanisms which have not yet been discovered or are not sufficiently characterized. Moreover, there is an increased awareness for micronutrients in ASD due to the presence of gastrointestinal (GI) problems that can be related to feeding issues. For example, levels of vitamins B1, B6, B12, A and D are often reported to be low in ASD children. Thus, in the present mini review we focused on describing the impact of some vitamins deficiencies and their relevance in ASD patients.

Pathogenic POGZ mutation causes impaired cortical development and reversible autism-like phenotypes

Pogo transposable element derived with ZNF domain (POGZ) has been identified as one of the most recurrently de novo mutated genes in patients with neurodevelopmental disorders (NDDs), including autism spectrum disorder (ASD), intellectual disability and White-Sutton syndrome; however, the neurobiological basis behind these disorders remains unknown. Here, we show that POGZ regulates neuronal development and that ASD-related de novo mutations impair neuronal development in the developing mouse brain and induced pluripotent cell lines from an ASD patient. We also develop the first mouse model heterozygous for a de novo POGZ mutation identified in a patient with ASD, and we identify ASD-like abnormalities in the mice. Importantly, social deficits can be treated by compensatory inhibition of elevated cell excitability in the mice. Our results provide insight into how de novo mutations on high-confidence ASD genes lead to impaired mature cortical network function, which underlies the cellular pathogenesis of NDDs, including ASD.

De novo mutations significantly contribute to autism spectrum disorders (ASD). Here, the authors demonstrate that ASD-associated de novo mutations in the POGZ gene, one of a high-confidence ASD gene, lead to ASD-related impaired neuronal development and disrupted mature cortical network function.

Genetic evidence of gender difference in autism spectrum disorder supports the female-protective effect

Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder with a male-to-female prevalence of 4:1. However, the genetic mechanisms underlying this gender difference remain unclear. Mutation burden analysis, a TADA model, and co-expression and functional network analyses were performed on de novo mutations (DNMs) and corresponding candidate genes. We found that the prevalence of putative functional DNMs (loss-of-function and predicted deleterious missense mutations) in females was significantly higher than that in males, suggesting that a higher genetic load was required in females to reach the threshold for a diagnosis. We then prioritized 174 candidate genes, including 60 shared genes, 91 male-specific genes, and 23 female-specific genes. All of the three subclasses of candidate genes were significantly more frequently co-expressed in female brains than male brains, suggesting that compensation effects of the deficiency of ASD candidate genes may be more likely in females. Nevertheless, the three subclasses of candidate genes were co-expressed with each other, suggesting a convergent functional network of male and female-specific genes. Our analysis of different aspects of genetic components provides suggestive evidence supporting the female-protective effect in ASD. Moreover, further study is needed to integrate neuronal and hormonal data to elucidate the underlying gender difference in ASD.

Gene4Denovo: an integrated database and analytic platform for de novo mutations in humans

De novo mutations (DNMs) significantly contribute to sporadic diseases, particularly in neuropsychiatric disorders. Whole-exome sequencing (WES) and whole-genome sequencing (WGS) provide effective methods for detecting DNMs and prioritizing candidate genes. However, it remains a challenge for scientists, clinicians, and biologists to conveniently access and analyse data regarding DNMs and candidate genes from scattered publications. To fill the unmet need, we integrated 580 799 DNMs, including 30 060 coding DNMs detected by WES/WGS from 23 951 individuals across 24 phenotypes and prioritized a list of candidate genes with different degrees of statistical evidence, including 346 genes with false discovery rates <0.05. We then developed a database called Gene4Denovo (http://www.genemed.tech/gene4denovo/), which allowed these genetic data to be conveniently catalogued, searched, browsed, and analysed. In addition, Gene4Denovo integrated data from >60 genomic sources to provide comprehensive variant-level and gene-level annotation and information regarding the DNMs and candidate genes. Furthermore, Gene4Denovo provides end-users with limited bioinformatics skills to analyse their own genetic data, perform comprehensive annotation, and prioritize candidate genes using custom parameters. In conclusion, Gene4Denovo conveniently allows for the accelerated interpretation of DNM pathogenicity and the clinical implication of DNMs in humans.

Expansion of GGC repeat in the human-specific NOTCH2NLC gene is associated with essential tremor

Essential tremor is one of the most common movement disorders. Despite its high prevalence and heritability, the genetic aetiology of essential tremor remains elusive. Up to now, only a few genes/loci have been identified, but these genes have not been replicated in other essential tremor families or cohorts. Here we report a genetic study in a cohort of 197 Chinese pedigrees clinically diagnosed with essential tremor. Using a comprehensive strategy combining linkage analysis, whole-exome sequencing, long-read whole-genome sequencing, repeat-primed polymerase chain reaction and GC-rich polymerase chain reaction, we identified an abnormal GGC repeat expansion in the 5′ region of the NOTCH2NLC gene that co-segregated with disease in 11 essential tremor families (5.58%) from our cohort. Clinically, probands that had an abnormal GGC repeat expansion were found to have more severe tremor phenotypes, lower activities of daily living ability. Obvious genetic anticipation was also detected in these 11 essential tremor-positive families. These results indicate that abnormal GGC repeat expansion in the 5′ region of NOTCH2NLC gene is associated with essential tremor, and provide strong evidence that essential tremor is a family of diseases with high clinical and genetic heterogeneities.

The Relevance of Variants With Unknown Significance for Autism Spectrum Disorder Considering the Genotype-Phenotype Interrelationship

Several efforts in basic and clinical research have been contributing to unveiling the genetics behind autism spectrum disorders (ASD). However, despite these advancements, many individuals diagnosed with ASD and related neuropsychiatric conditions have been genetically investigated without elucidative results. The enormous genetic complexity of ASD-related conditions makes it a significant challenge to achieve, with a growing number of genes (close to a thousand) involved, belonging to different molecular pathways and presenting distinct genetic variations. Next-generation sequencing (NGS) is the approach most used in genetic research related to ASD, identifying de novo mutation, which is closely related to more severe clinical phenotypes, especially when they affect constrained and loss-of-function intolerant genes. On the other hand, de novo mutation findings contribute to a small percentage of the ASD population, since most of the cases and genetic variants associated with neuropsychiatric conditions are inherited and phenotypes are results of additive polygenic models, which makes statistical efforts more difficult. As a result, NGS investigation can sound vainly or unsuccessful, and new mutations on genes already related with ASD are classified as variants of unknown significance (VUS), hampering their endorsement to a clinical phenotype. This review is focused on currently available strategies to clarify the impact of VUS and to describe the efforts to identify more pieces of evidence throughout clinical interpretation and genetic curation process.