Rare Recurrent Variants in Noncoding Regions Impact Attention-Deficit Hyperactivity Disorder (ADHD) Gene Networks in Children of both African American and European American Ancestry

Association of ABCA13 Gene Variants with Autism Spectrum Disorder and Other Neuropsychiatric Disorders

Introduction

Autism spectrum disorder (ASD) is a neuropsychiatric disorder characterized by impaired social skills and limited or repetitive behaviors. In this study, we investigated the role of the ABCA13 gene in the etiopathogenesis of ASD.

Methods

Single-nucleotide variants were evaluated in 79 ASD patients (59 males +20 females) with no established genetic etiology associated with ASD using whole-exome sequencing/clinical exome sequencing method. Family segregation analysis was performed using Sanger sequencing. We presented the clinical and genetic findings of these cases and their parents in detail.

Results

We presented 10 different ABCA13 gene variants in cases with ASD and 10 parents carrying the same ABCA13 gene variant. There of these variants were likely pathogenic and seven variants were classified as variant of uncertain significance. Our cases had a comorbidity rate for attention deficit hyperactivity disorder (ADHD) as 70%. Various types of neuropsychiatric symptoms and diagnoses were detected including ADHD, anxiety disorder, intellectual disability, delay in speech, and febrile convulsion among the parents.

Conclusion

To date, very few variants have been reported in the ABCA13 gene. Our findings enrich the role of ABCA13 gene may play a common role in the landscape of neuropsychiatric disorders.

Clinical and Genetic Analysis of A Father-Son Duo with Monomelic Amyotrophy: Case Report

Monomelic Amyotrophy (MMA) is a rare neurological disorder restricted to one upper limb, predominantly affecting young males with an unknown aetiopathogenesis. We report a familial case of father-son duo affected by MMA. Whole exome sequencing identified genetic variations in SLIT1, RYR3 and ARPP21 involved in axon guidance, calcium homeostasis and regulation of calmodulin signaling respectively. This is the first attempt to define genetic modifiers associated with MMA from India and advocates to extend genetic screening to a larger cohort. Deciphering the functional consequences of variations in these genes will be crucial for unravelling the pathogenesis of MMA.

Clinical and Genetic Characteristics of Patients with Unexplained Intellectual Disability/Developmental Delay without Epilepsy

Introduction

Global developmental delay (DD), intellectual disability (ID), and autism spectrum disorder (ASD) are mainly evaluated under the neurodevelopmental disorder framework. In this study, we aimed to determine the genetic diagnosis yield using step-by-step genetic analysis in 38 patients with unexplained ID/DD and/or ASD.

Methods

In 38 cases (27 male, 11 female) with unexplained ID/DD and/or ASD, chromosomal microarray (CMA) analysis, clinical exome sequencing (CES), and whole-exome sequencing (WES) analysis were applied, respectively.

Results

We found a diagnostic rate of only CMA analysis as 21% (8/38) presenting 8 pathogenic and likely pathogenic CNVs. The rate of patients diagnosed with CES/WES methods was 32.2% (10/31). When all pathogenic and likely pathogenic variants were evaluated, the diagnosis rate was 44.7% (17/38). A dual diagnosis was obtained in a case with 16p11.2 microduplication and de novo SNV. We identified eight novel variants: TUBA1A (c.787C>G), TMEM63A (c.334-2A>G), YY1AP1 (c.2051_2052del), ABCA13 (c.12064C>T), ABCA13 (c.13187G>A), USP9X (c.1189T>C), ANKRD17 (c.328_330dup), and GRIA4 (c.17G>A).

Conclusion

We present diagnostic rates of a complementary approach to genetic analysis (CMA, CES, and WES). The combined use of genetic analysis methods in unexplained ID/DD and/or ASD cases has contributed significantly to diagnosis rates. Also, we present detailed clinical characteristics to improve genotype-phenotype correlation in the literature for rare and novel variants.

Cerebellum and neurodevelopmental disorders: RORα is a unifying force

Errors of cerebellar development are increasingly acknowledged as risk factors for neuro-developmental disorders (NDDs), such as attention deficit hyperactivity disorder (ADHD), autism spectrum disorder (ASD), and schizophrenia. Evidence has been assembled from cerebellar abnormalities in autistic patients, as well as a range of genetic mutations identified in human patients that affect the cerebellar circuit, particularly Purkinje cells, and are associated with deficits of motor function, learning and social behavior; traits that are commonly associated with autism and schizophrenia. However, NDDs, such as ASD and schizophrenia, also include systemic abnormalities, e.g., chronic inflammation, abnormal circadian rhythms etc., which cannot be explained by lesions that only affect the cerebellum. Here we bring together phenotypic, circuit and structural evidence supporting the contribution of cerebellar dysfunction in NDDs and propose that the transcription factor Retinoid-related Orphan Receptor alpha (RORα) provides the missing link underlying both cerebellar and systemic abnormalities observed in NDDs. We present the role of RORα in cerebellar development and how the abnormalities that occur due to RORα deficiency could explain NDD symptoms. We then focus on how RORα is linked to NDDs, particularly ASD and schizophrenia, and how its diverse extra-cerebral actions can explain the systemic components of these diseases. Finally, we discuss how RORα-deficiency is likely a driving force for NDDs through its induction of cerebellar developmental defects, which in turn affect downstream targets, and its regulation of extracerebral systems, such as inflammation, circadian rhythms, and sexual dimorphism.

Attention-deficit/hyperactive disorder updates

Background

Attention-deficit/hyperactive disorder (ADHD) is a neurodevelopmental disorder that commonly occurs in children with a prevalence ranging from 3.4 to 7.2%. It profoundly affects academic achievement, well-being, and social interactions. As a result, this disorder is of high cost to both individuals and society. Despite the availability of knowledge regarding the mechanisms of ADHD, the pathogenesis is not clear, hence, the existence of many challenges especially in making correct early diagnosis and provision of accurate management.

Objectives

We aimed to review the pathogenic pathways of ADHD in children. The major focus was to provide an update on the reported etiologies in humans, animal models, modulators, therapies, mechanisms, epigenetic changes, and the interaction between genetic and environmental factors.

Methods

References for this review were identified through a systematic search in PubMed by using special keywords for all years until January 2022.

Results

Several genes have been reported to associate with ADHD: DRD1, DRD2, DRD4, DAT1, TPH2, HTR1A, HTR1B, SLC6A4, HTR2A, DBH, NET1, ADRA2A, ADRA2C, CHRNA4, CHRNA7, GAD1, GRM1, GRM5, GRM7, GRM8, TARBP1, ADGRL3, FGF1, MAOA, BDNF, SNAP25, STX1A, ATXN7, and SORCS2. Some of these genes have evidence both from human beings and animal models, while others have evidence in either humans or animal models only. Notably, most of these animal models are knockout and do not generate the genetic alteration of the patients. Besides, some of the gene polymorphisms reported differ according to the ethnic groups. The majority of the available animal models are related to the dopaminergic pathway. Epigenetic changes including SUMOylation, methylation, and acetylation have been reported in genes related to the dopaminergic pathway.

Conclusion

The dopaminergic pathway remains to be crucial in the pathogenesis of ADHD. It can be affected by environmental factors and other pathways. Nevertheless, it is still unclear how environmental factors relate to all neurotransmitter pathways; thus, more studies are needed. Although several genes have been related to ADHD, there are few animal model studies on the majority of the genes, and they do not generate the genetic alteration of the patients. More animal models and epigenetic studies are required.

Exome sequencing analysis of Japanese autism spectrum disorder case-control sample supports an increased burden of synaptic function-related genes

Autism spectrum disorder (ASD) is a highly heritable, complex disorder in which rare variants contribute significantly to disease risk. Although many genes have been associated with ASD, there have been few genetic studies of ASD in the Japanese population. In whole exomes from a Japanese ASD sample of 309 cases and 299 controls, rare variants were associated with ASD within specific neurodevelopmental gene sets, including highly constrained genes, fragile X mental retardation protein target genes, and genes involved in synaptic function, with the strongest enrichment in trans-synaptic signaling (p = 4.4 × 10^-4, Q-value = 0.06). In particular, we strengthen the evidence regarding the role of ABCA13, a synaptic function-related gene, in Japanese ASD. The overall results of this case-control exome study showed that rare variants related to synaptic function are associated with ASD susceptibility in the Japanese population.

Genetics in the ADHD Clinic: How Can Genetic Testing Support the Current Clinical Practice?

Attention-deficit/hyperactivity disorder (ADHD) is a neurodevelopmental disorder with a childhood prevalence of 5%. In about two-thirds of the cases, ADHD symptoms persist into adulthood and often cause significant functional impairment. Based on the results of family and twin studies, the estimated heritability of ADHD approximates 80%, suggests a significant genetic component in the etiological background of the disorder; however, the potential genetic effects on disease risk, symptom severity, and persistence are unclear. This article provides a brief review of the genome-wide and candidate gene association studies with a focus on the clinical aspects, summarizing findings of ADHD disease risk, ADHD core symptoms as dimensional traits, and other traits frequently associated with ADHD, which may contribute to the susceptibility to other comorbid psychiatric disorders. Furthermore, neuropsychological impairment and measures from neuroimaging and electrophysiological paradigms, emerging as potential biomarkers, also provide a prominent target for molecular genetic studies, since they lie in the pathway from genes to behavior; therefore, they can contribute to the understanding of the underlying neurobiological mechanisms and the interindividual heterogeneity of clinical symptoms. Beyond the aforementioned aspects, throughout the review, we also give a brief summary of the genetic results, including polygenic risk scores that can potentially predict individual response to different treatment options and may offer a possibility for personalized treatment for the therapy of ADHD in the future.

Application of deep learning algorithm on whole genome sequencing data uncovers structural variants associated with multiple mental disorders in African American patients

Mental disorders present a global health concern, while the diagnosis of mental disorders can be challenging. The diagnosis is even harder for patients who have more than one type of mental disorder, especially for young toddlers who are not able to complete questionnaires or standardized rating scales for diagnosis. In the past decade, multiple genomic association signals have been reported for mental disorders, some of which present attractive drug targets. Concurrently, machine learning algorithms, especially deep learning algorithms, have been successful in the diagnosis and/or labeling of complex diseases, such as attention deficit hyperactivity disorder (ADHD) or cancer. In this study, we focused on eight common mental disorders, including ADHD, depression, anxiety, autism, intellectual disabilities, speech/language disorder, delays in developments, and oppositional defiant disorder in the ethnic minority of African Americans. Blood-derived whole genome sequencing data from 4179 individuals were generated, including 1384 patients with the diagnosis of at least one mental disorder. The burden of genomic variants in coding/non-coding regions was applied as feature vectors in the deep learning algorithm. Our model showed ~65% accuracy in differentiating patients from controls. Ability to label patients with multiple disorders was similarly successful, with a hamming loss score less than 0.3, while exact diagnostic matches are around 10%. Genes in genomic regions with the highest weights showed enrichment of biological pathways involved in immune responses, antigen/nucleic acid binding, chemokine signaling pathway, and G-protein receptor activities. A noticeable fact is that variants in non-coding regions (e.g., ncRNA, intronic, and intergenic) performed equally well as variants in coding regions; however, unlike coding region variants, variants in non-coding regions do not express genomic hotspots whereas they carry much more narrow standard deviations, indicating they probably serve as alternative markers.

Deep learning prediction of attention-deficit hyperactivity disorder in African Americans by copy number variation

Current understanding of the underlying molecular network and mechanism for attention-deficit hyperactivity disorder (ADHD) is lacking and incomplete. Previous studies suggest that genomic structural variations play an important role in the pathogenesis of ADHD. For effective modeling, deep learning approaches have become a method of choice, with ability to predict the impact of genetic variations involving complicated mechanisms. In this study, we examined copy number variation in whole genome sequencing from 116 African Americans ADHD children and 408 African American controls. We divided the human genome into 150 regions, and the variation intensity in each region was applied as feature vectors for deep learning modeling to classify ADHD patients. The accuracy of deep learning for predicting ADHD diagnosis is consistently around 78% in a two-fold shuffle test, compared with ∼50% by traditional k-mean clustering methods. Additional whole genome sequencing data from 351 European Americans children, including 89 ADHD cases and 262 controls, were applied as independent validation using feature vectors obtained from the African American ethnicity analysis. The accuracy of ADHD labeling was lower in this setting (∼70-75%) but still above the results from traditional methods. The regions with highest weight overlapped with the previously reported ADHD-associated copy number variation regions, including genes such as GRM1 and GRM8, key drivers of metabotropic glutamate receptor signaling. A notable discovery is that structural variations in non-coding genomic (intronic/intergenic) regions show prediction weights that can be as high as prediction weight from variations in coding regions, results that were unexpected.