Common genetic variants contribute to risk of rare severe neurodevelopmental disorders

Genetically transitional disease: a new concept in genomic medicine

Traditional classification of genetic diseases as monogenic and polygenic has lagged far behind scientific progress. In this opinion article, we propose and define a new terminology, genetically transitional disease (GTD), referring to cases where a large-effect mutation is necessary, but not sufficient, to cause disease. This leads to a working disease nosology based on gradients of four types of genetic architecture: monogenic, polygenic, GTD, and mixed. We present four scenarios under which GTD may occur; namely, subsets of traditionally Mendelian disease, modifiable Tier 1 monogenic conditions, variable penetrance, and situations where a genetic mutational spectrum produces qualitatively divergent pathologies. The implications of the new nosology in precision medicine are discussed, in which therapeutic options may target the molecular cause or the disease phenotype.

Histone lysine methyltransferase-related neurodevelopmental disorders: current knowledge and saRNA future therapies

Neurodevelopmental disorders encompass a group of debilitating diseases presenting with motor and cognitive dysfunction, with variable age of onset and disease severity. Advances in genetic diagnostic tools have facilitated the identification of several monogenic chromatin remodeling diseases that cause Neurodevelopmental disorders. Chromatin remodelers play a key role in the neuro-epigenetic landscape and regulation of brain development; it is therefore not surprising that mutations, leading to loss of protein function, result in aberrant neurodevelopment. Heterozygous, usually de novo mutations in histone lysine methyltransferases have been described in patients leading to haploinsufficiency, dysregulated protein levels and impaired protein function. Studies in animal models and patient-derived cell lines, have highlighted the role of histone lysine methyltransferases in the regulation of cell self-renewal, cell fate specification and apoptosis. To date, in depth studies of histone lysine methyltransferases in oncology have provided strong evidence of histone lysine methyltransferase dysregulation as a determinant of cancer progression and drug resistance. As a result, histone lysine methyltransferases have become an important therapeutic target for the treatment of different cancer forms. Despite recent advances, we still lack knowledge about the role of histone lysine methyltransferases in neuronal development. This has hampered both the study and development of precision therapies for histone lysine methyltransferases-related Neurodevelopmental disorders. In this review, we will discuss the current knowledge of the role of histone lysine methyltransferases in neuronal development and disease progression. We will also discuss how RNA-based technologies using small-activating RNAs could potentially provide a novel therapeutic approach for the future treatment of histone lysine methyltransferase haploinsufficiency in these Neurodevelopmental disorders, and how they could be first tested in state-of-the-art patient-derived neuronal models.

Oxytocin Exposure in Labor and its Relationship with Cognitive Impairment and the Genetic Architecture of Autism

Whether there is a relationship between oxytocin (OXT) use in labor and the risk of autism (ASD), and the nature of such relationship, is unclear. By integrating genetic and clinical data in a sample of 176 ASD participants, we tested the hypothesis that OXT is a marker for abnormal prenatal development which leads to impairments in the process of labor. OXT-exposed ASD had more obstetric complications (P = 0.031), earlier onset of symptoms (P = 0.027), poorer cognitive development (P = 0.011), higher mutation burden across neurodevelopment genes (P = 0.020; OR = 5.33) and lower transmission of polygenic risk for ASD (P = 0.0319), than non-exposed ASD. OXT seems to constitute a risk indicator rather than a risk factor for ASD, which is relevant for diagnostic and genetic counselling.

Exposure to Antibiotics and Neurodevelopmental Disorders: Could Probiotics Modulate the Gut-Brain Axis?

In order to develop properly, the brain requires the intricate interconnection of genetic factors and pre-and postnatal environmental events. The gut-brain axis has recently raised considerable interest for its involvement in regulating the development and functioning of the brain. Consequently, alterations in the gut microbiota composition, due to antibiotic administration, could favor the onset of neurodevelopmental disorders. Literature data suggest that the modulation of gut microbiota is often altered in individuals affected by neurodevelopmental disorders. It has been shown in animal studies that metabolites released by an imbalanced gut-brain axis, leads to alterations in brain function and deficits in social behavior. Here, we report the potential effects of antibiotic administration, before and after birth, in relation to the risk of developing neurodevelopmental disorders. We also review the potential role of probiotics in treating gastrointestinal disorders associated with gut dysbiosis after antibiotic administration, and their possible effect in ameliorating neurodevelopmental disorder symptoms.

Long read sequencing and expression studies of AHDC1 deletions in Xia-Gibbs syndrome reveal a novel genetic regulatory mechanism

Xia-Gibbs syndrome (XGS; MIM# 615829) is a rare mendelian disorder characterized by Development Delay (DD), intellectual disability (ID), and hypotonia. Individuals with XGS typically harbor de novo protein-truncating mutations in the AT-Hook DNA binding motif containing 1 (AHDC1) gene, although some missense mutations can also cause XGS. Large de novo heterozygous deletions that encompass the AHDC1 gene have also been ascribed as diagnostic for the disorder, without substantial evidence to support their pathogenicity. We analyzed 19 individuals with large contiguous deletions involving AHDC1, along with other genes. One individual bore the smallest known contiguous AHDC1 deletion (∼350 Kb), encompassing eight other genes within chr1p36.11 (Feline Gardner-Rasheed, IFI6, FAM76A, STX12, PPP1R8, THEMIS2, RPA2, SMPDL3B) and terminating within the first intron of AHDC1. The breakpoint junctions and phase of the deletion were identified using both short and long read sequencing (Oxford Nanopore). Quantification of RNA expression patterns in whole blood revealed that AHDC1 exhibited a mono-allelic expression pattern with no deficiency in overall AHDC1 expression levels, in contrast to the other deleted genes, which exhibited a 50% reduction in mRNA expression. These results suggest that AHDC1 expression in this individual is compensated by a novel regulatory mechanism and advances understanding of mutational and regulatory mechanisms in neurodevelopmental disorders.

Research models of neurodevelopmental disorders: The right model in the right place

Neurodevelopmental disorders (NDDs) are a heterogeneous group of impairments that affect the development of the central nervous system leading to abnormal brain function. NDDs affect a great percentage of the population worldwide, imposing a high societal and economic burden and thus, interest in this field has widely grown in recent years. Nevertheless, the complexity of human brain development and function as well as the limitations regarding human tissue usage make their modeling challenging. Animal models play a central role in the investigation of the implicated molecular and cellular mechanisms, however many of them display key differences regarding human phenotype and in many cases, they partially or completely fail to recapitulate them. Although in vitro two-dimensional (2D) human-specific models have been highly used to address some of these limitations, they lack crucial features such as complexity and heterogeneity. In this review, we will discuss the advantages, limitations and future applications of in vivo and in vitro models that are used today to model NDDs. Additionally, we will describe the recent development of 3-dimensional brain (3D) organoids which offer a promising approach as human-specific in vitro models to decipher these complex disorders.

Current practice in diagnostic genetic testing of the epilepsies

Epilepsy genetics is a rapidly developing field, in which novel disease-associated genes, novel mechanisms associated with epilepsy, and precision medicine approaches are continuously being identified. In the past decade, advances in genomic knowledge and analysis platforms have begun to make clinical genetic testing accessible for, in principle, people of all ages with epilepsy. For this reason, the Genetics Commission of the International League Against Epilepsy (ILAE) presents this update on clinical genetic testing practice, including current techniques, indications, yield of genetic testing, recommendations for pre- and post-test counseling, and follow-up after genetic testing is completed. We acknowledge that the resources vary across different settings but highlight that genetic diagnostic testing for epilepsy should be prioritized when the likelihood of an informative finding is high. Results of genetic testing, in particular the identification of causative genetic variants, are likely to improve individual care. We emphasize the importance of genetic testing for individuals with epilepsy as we enter the era of precision therapy.

Diverse ancestry whole-genome sequencing association study identifies TBX5 and PTK7 as susceptibility genes for posterior urethral valves

Posterior urethral valves (PUV) are the commonest cause of end-stage renal disease in children, but the genetic architecture of this rare disorder remains unknown. We performed a sequencing-based genome-wide association study (seqGWAS) in 132 unrelated male PUV cases and 23,727 controls of diverse ancestry, identifying statistically significant associations with common variants at 12q24.21 (p=7.8 × 10-12; OR 0.4) and rare variants at 6p21.1 (p=2.0 × 10-8; OR 7.2), that were replicated in an independent European cohort of 395 cases and 4151 controls. Fine mapping and functional genomic data mapped these loci to the transcription factor TBX5 and planar cell polarity gene PTK7, respectively, the encoded proteins of which were detected in the developing urinary tract of human embryos. We also observed enrichment of rare structural variation intersecting with candidate cis-regulatory elements, particularly inversions predicted to affect chromatin looping (p=3.1 × 10-5). These findings represent the first robust genetic associations of PUV, providing novel insights into the underlying biology of this poorly understood disorder and demonstrate how a diverse ancestry seqGWAS can be used for disease locus discovery in a rare disease.

Genetic correlates of phenotypic heterogeneity in autism

The substantial phenotypic heterogeneity in autism limits our understanding of its genetic etiology. To address this gap, here we investigated genetic differences between autistic individuals (nmax = 12,893) based on core and associated features of autism, co-occurring developmental disabilities and sex. We conducted a comprehensive factor analysis of core autism features in autistic individuals and identified six factors. Common genetic variants were associated with the core factors, but de novo variants were not. We found that higher autism polygenic scores (PGS) were associated with lower likelihood of co-occurring developmental disabilities in autistic individuals. Furthermore, in autistic individuals without co-occurring intellectual disability (ID), autism PGS are overinherited by autistic females compared to males. Finally, we observed higher SNP heritability for autistic males and for autistic individuals without ID. Deeper phenotypic characterization will be critical in determining how the complex underlying genetics shape cognition, behavior and co-occurring conditions in autism.

Rare variants and the oligogenic architecture of autism

Most large-scale genetic studies of autism have focused on the discovery of genes by proving an enrichment of de novo mutations (DNMs) in autism probands or characterizing polygenic risk based on the association of common variants. We present evidence in support of an oligogenic model where two or more ultrarare mutations of more modest effect are preferentially transmitted to children with autism. Such private gene-disruptive mutations are enriched in families where there are multiple affected individuals, emerged two or three generations ago, and map to genes not previously associated with autism. Although no single gene has reached statistical significance, this class of variation should be considered along with genetic and nongenetic factors to better explain the etiology of this complex trait.

Decoding the Human Face: Progress and Challenges in Understanding the Genetics of Craniofacial Morphology

Variations in the form of the human face, which plays a role in our individual identities and societal interactions, have fascinated scientists and artists alike. Here, we review our current understanding of the genetics underlying variation in craniofacial morphology and disease-associated dysmorphology, synthesizing decades of progress on Mendelian syndromes in addition to more recent results from genome-wide association studies of human facial shape and disease risk. We also discuss the various approaches used to phenotype and quantify facial shape, which are of particular importance due to the complex, multipartite nature of the craniofacial form. We close by discussing how experimental studies have contributed and will further contribute to our understanding of human genetic variation and then proposing future directions and applications for the field.

Incomplete Penetrance and Variable Expressivity: From Clinical Studies to Population Cohorts

The same genetic variant found in different individuals can cause a range of diverse phenotypes, from no discernible clinical phenotype to severe disease, even among related individuals. Such variants can be said to display incomplete penetrance, a binary phenomenon where the genotype either causes the expected clinical phenotype or it does not, or they can be said to display variable expressivity, in which the same genotype can cause a wide range of clinical symptoms across a spectrum. Both incomplete penetrance and variable expressivity are thought to be caused by a range of factors, including common variants, variants in regulatory regions, epigenetics, environmental factors, and lifestyle. Many thousands of genetic variants have been identified as the cause of monogenic disorders, mostly determined through small clinical studies, and thus, the penetrance and expressivity of these variants may be overestimated when compared to their effect on the general population. With the wealth of population cohort data currently available, the penetrance and expressivity of such genetic variants can be investigated across a much wider contingent, potentially helping to reclassify variants that were previously thought to be completely penetrant. Research into the penetrance and expressivity of such genetic variants is important for clinical classification, both for determining causative mechanisms of disease in the affected population and for providing accurate risk information through genetic counseling. A genotype-based definition of the causes of rare diseases incorporating information from population cohorts and clinical studies is critical for our understanding of incomplete penetrance and variable expressivity. This review examines our current knowledge of the penetrance and expressivity of genetic variants in rare disease and across populations, as well as looking into the potential causes of the variation seen, including genetic modifiers, mosaicism, and polygenic factors, among others. We also considered the challenges that come with investigating penetrance and expressivity.

A mathematical modelling to detect sickle cell anemia using Quantum graph theory and Aquila optimization classifier

Recently genetic disorders are the most common reason for human fatality. Sickle Cell anemia is a monogenic disorder caused by A-to-T point mutations in the β-globin gene which produces abnormal hemoglobin S (Hgb S) that polymerizes at the state of deoxygenation thus resulting in the physical deformation or erythrocytes sickling. This shortens the expectancy of human life. Thus, the early diagnosis and identification of sickle cell will aid the people in recognizing signs and to take treatments. The manual identification is a time consuming one and might outcome in the misclassification of count as there is millions of red blood cells in one spell. So as to overcome this, data mining approaches like Quantum graph theory model and classifier is effective in detecting sickle cell anemia with high precision rate. The proposed work aims at presenting a mathematical modeling using Quantum graph theory to extract elasticity properties and to distinguish them as normal cells and sickle cell anemia (SCA) in red blood cells. Initially, input DNA sequence is taken and the elasticity property features are extracted by using Quantum graph theory model at which the formation of spanning tree is made followed by graph construction and Hemoglobin quantization. After which, the extracted properties are optimized using Aquila optimization and classified using cascaded Long Short-Term memory (LSTM) to attain the classified outcome of sickle cell and normal cells. Finally, the performance assessment is made and the outcomes attained in terms of accuracy, precision, sensitivity, specificity, and AUC are compared with existing classifier to validate the proposed system effectiveness.

Autism-Risk Gene necab2 Regulates Psychomotor and Social Behavior as a Neuronal Modulator of mGluR1 Signaling

Background

De novo deletion of the neuronal calcium-binding protein 2 (NECAB2) locus is associated with idiopathic autism spectrum disorders (ASDs). The in vivo function of NECAB2 in the brain remains largely elusive.

Methods

We investigated the morphological and behavioral profiles of both necab2 knock-out and overexpression zebrafish models. The expression pattern and molecular role of necab2 were probed through a combination of in vitro and in vivo assays.

Results

We show that Necab2 is a neuronal specific, cytoplasmic, and membrane-associated protein, abundantly expressed in the telencephalon, habenula, and cerebellum. Necab2 is distributed peri-synaptically in subsets of glutamatergic and GABAergic neurons. CRISPR/Cas9-generated necab2 knock-out zebrafish display normal morphology but exhibit a decrease in locomotor activity and thigmotaxis with impaired social interaction only in males. Conversely, necab2 overexpression yields behavioral phenotypes opposite to the loss-of-function. Proteomic profiling uncovers a role of Necab2 in modulating signal transduction of G-protein coupled receptors. Specifically, co-immunoprecipitation, immunofluorescence, and confocal live-cell imaging suggest a complex containing NECAB2 and the metabotropic glutamate receptor 1 (mGluR1). In vivo measurement of phosphatidylinositol 4,5-bisphosphate further substantiates that Necab2 promotes mGluR1 signaling.

Conclusions

Necab2 regulates psychomotor and social behavior via modulating a signaling cascade downstream of mGluR1.

The contribution of common regulatory and protein-coding TYR variants to the genetic architecture of albinism

Genetic diseases have been historically segregated into rare Mendelian disorders and common complex conditions. Large-scale studies using genome sequencing are eroding this distinction and are gradually unmasking the underlying complexity of human traits. Here, we analysed data from the Genomics England 100,000 Genomes Project and from a cohort of 1313 individuals with albinism aiming to gain insights into the genetic architecture of this archetypal rare disorder. We investigated the contribution of protein-coding and regulatory variants both rare and common. We focused on TYR, the gene encoding tyrosinase, and found that a high-frequency promoter variant, TYR c.-301C>T [rs4547091], modulates the penetrance of a prevalent, albinism-associated missense change, TYR c.1205G>A (p.Arg402Gln) [rs1126809]. We also found that homozygosity for a haplotype formed by three common, functionally-relevant variants, TYR c.[-301C;575C>A;1205G>A], is associated with a high probability of receiving an albinism diagnosis (OR>82). This genotype is also associated with reduced visual acuity and with increased central retinal thickness in UK Biobank participants. Finally, we report how the combined analysis of rare and common variants can increase diagnostic yield and can help inform genetic counselling in families with albinism.

Rare genetic variants in genes and loci linked to dominant monogenic developmental disorders cause milder related phenotypes in the general population

Many rare monogenic diseases are known to be caused by deleterious variants in thousands of genes, however the same variants can also be found in people without the associated clinical phenotypes. The penetrance of these monogenic variants is generally unknown in the wider population, as they are typically identified in small clinical cohorts of affected individuals and families with highly penetrant variants. Here, we investigated the phenotypic effect of rare, potentially deleterious variants in genes and loci where similar variants are known to cause monogenic developmental disorders (DDs) in a large population cohort. We used UK Biobank to investigate phenotypes associated with rare protein-truncating and missense variants in 599 monoallelic DDG2P genes by using whole-exome-sequencing data from ∼200,000 individuals and rare copy-number variants overlapping known DD loci by using SNP-array data from ∼500,000 individuals. We found that individuals with these likely deleterious variants had a mild DD-related phenotype, including lower fluid intelligence, slower reaction times, lower numeric memory scores, and longer pairs matching times compared to the rest of the UK Biobank cohort. They were also shorter, had a higher BMI, and had significant socioeconomic disadvantages: they were less likely to be employed or be able to work and had a lower income and higher deprivation index. Our findings suggest that many genes routinely tested within pediatric genetics have deleterious variants with intermediate penetrance that may cause lifelong sub-clinical phenotypes in the general adult population.

The role of common genetic variation in presumed monogenic epilepsies

BACKGROUND

The developmental and epileptic encephalopathies (DEEs) are the most severe group of epilepsies which co-present with developmental delay and intellectual disability (ID). DEEs usually occur in people without a family history of epilepsy and have emerged as primarily monogenic, with damaging rare mutations found in 50% of patients. Little is known about the genetic architecture of patients with DEEs in whom no pathogenic variant is identified. Polygenic risk scoring (PRS) is a method that measures a person's common genetic burden for a trait or condition. Here, we used PRS to test whether genetic burden for epilepsy is relevant in individuals with DEEs, and other forms of epilepsy with ID.

METHODS

Genetic data on 2,759 cases with DEEs, or epilepsy with ID presumed to have a monogenic basis, and 447,760 population-matched controls were analysed. We compared PRS for 'all epilepsy', 'focal epilepsy', and 'genetic generalised epilepsy' (GGE) between cases and controls. We performed pairwise comparisons between cases stratified for identifiable rare deleterious genetic variants and controls.

FINDINGS

Cases of presumed monogenic severe epilepsy had an increased PRS for 'all epilepsy' (p<0.0001), 'focal epilepsy' (p<0.0001), and 'GGE' (p=0.0002) relative to controls, which explain between 0.08% and 3.3% of phenotypic variance. PRS was increased in cases both with and without an identified deleterious variant of major effect, and there was no significant difference in PRS between the two groups.

INTERPRETATION

We provide evidence that common genetic variation contributes to the aetiology of DEEs and other forms of epilepsy with ID, even when there is a known pathogenic variant of major effect. These results provide insight into the genetic underpinnings of the severe epilepsies and warrant a shift in our understanding of the aetiology of the DEEs as complex, rather than monogenic, disorders.

FUNDING

Science foundation Ireland, Human Genome Research Institute; National Heart, Lung, and Blood Institute; German Research Foundation.

The female protective effect against autism spectrum disorder

Autism spectrum disorder (ASD) is diagnosed three to four times more frequently in males than in females. Genetic studies of rare variants support a female protective effect (FPE) against ASD. However, sex differences in common inherited genetic risk for ASD are less studied, particularly within families. Leveraging the Danish iPSYCH resource, we found siblings of female ASD cases (n = 1,707) had higher rates of ASD than siblings of male ASD cases (n = 6,270; p < 1.0 × 10-10). In the Simons Simplex and SPARK collections, mothers of ASD cases (n = 7,436) carried more polygenic risk for ASD than fathers of ASD cases (n = 5,926; 0.08 polygenic risk score [PRS] SD; p = 7.0 × 10-7). Further, male unaffected siblings under-inherited polygenic risk (n = 1,519; p = 0.03). Using both epidemiologic and genetic approaches, our findings strongly support an FPE against ASD's common inherited influences.

Common risk variants for epilepsy are enriched in families previously targeted for rare monogenic variant discovery

BACKGROUND

The epilepsies are highly heritable conditions that commonly follow complex inheritance. While monogenic causes have been identified in rare familial epilepsies, most familial epilepsies remain unsolved. We aimed to determine (1) whether common genetic variation contributes to familial epilepsy risk, and (2) whether that genetic risk is enriched in familial compared with non-familial (sporadic) epilepsies.

METHODS

Using common variants derived from the largest epilepsy genome-wide association study, we calculated polygenic risk scores (PRS) for patients with familial epilepsy (n = 1,818 from 1,181 families), their unaffected relatives (n = 771), sporadic patients (n = 1,182), and population controls (n = 15,929). We also calculated separate PRS for genetic generalised epilepsy (GGE) and focal epilepsy. Statistical analyses used mixed-effects regression models to account for familial relatedness, sex, and ancestry.

FINDINGS

Patients with familial epilepsies had higher epilepsy PRS compared to population controls (OR 1·20, p_adj = 5×10^-9), sporadic patients (OR 1·11, p_adj = 0.008), and their own unaffected relatives (OR 1·12, p_adj = 0.01). The top 1% of the PRS distribution was enriched 3.8-fold for individuals with familial epilepsy when compared to the lowest decile (p_adj = 5×10^-11). Familial PRS enrichment was consistent across epilepsy type; overall, polygenic risk was greatest for the GGE clinical group. There was no significant PRS difference in familial cases with established rare variant genetic etiologies compared to unsolved familial cases.

INTERPRETATION

The aggregate effects of common genetic variants, measured as polygenic risk scores, play an important role in explaining why some families develop epilepsy, why specific family members are affected while their relatives are not, and why families manifest specific epilepsy types. Polygenic risk contributes to the complex inheritance of the epilepsies, including in individuals with a known genetic etiology.

FUNDING

National Health and Medical Research Council of Australia, National Institutes of Health, American Academy of Neurology, Thomas B and Jeannette E Laws McCabe Fund, Mirowski Family Foundation.

TT-Mars: structural variants assessment based on haplotype-resolved assemblies

Variant benchmarking is often performed by comparing a test callset to a gold standard set of variants. In repetitive regions of the genome, it may be difficult to establish what is the truth for a call, for example, when different alignment scoring metrics provide equally supported but different variant calls on the same data. Here, we provide an alternative approach, TT-Mars, that takes advantage of the recent production of high-quality haplotype-resolved genome assemblies by providing false discovery rates for variant calls based on how well their call reflects the content of the assembly, rather than comparing calls themselves.

The impact of educational attainment, intelligence and intellectual disability on schizophrenia: a Swedish population-based register and genetic study

Schizophrenia (SCZ) is highly heterogenous and no subtypes characterizing treatment response or longitudinal course well. Cognitive impairment is a core clinical feature of SCZ and a determinant of poorer outcome. Genetic overlap between SCZ and cognitive traits is complex, with limited studies of comprehensive epidemiological and genomic evidence. To examine the relation between SCZ and three cognitive traits, educational attainment (EDU), premorbid cognitive ability, and intellectual disability (ID), we used two Swedish samples: a national cohort (14,230 SCZ cases and 3,816,264 controls) and a subsample with comprehensive genetic data (4992 cases and 6009 controls). Population-based analyses confirmed worse cognition as a risk factor for SCZ, and the pedigree and SNP-based genetic correlations were comparable. In the genotyped cases, those with high EDU and premorbid cognitive ability tended to have higher polygenetic risk scores (PRS) of EDU and intelligence and fewer rare exonic variants. Finally, by applying an empirical clustering method, we dissected SCZ cases into four replicable subgroups characterized by EDU and ID. In particular, the subgroup with higher EDU in the national cohort had fewer adverse outcomes including long hospitalization and death. In the genotyped subsample, this subgroup had higher PRS of EDU and no excess of rare genetic burdens than controls. In conclusion, we found extensive evidence of a robust relation between cognitive traits and SCZ, underscoring the importance of cognition in dissecting the heterogeneity of SCZ.

Phenotypic Spectrum in a Family Sharing a Heterozygous KCNQ3 Variant

BACKGROUND AND PURPOSE

Mutations in KCNQ3 have classically been associated with benign familial neonatal and infantile seizures and more recently identified in patients with neurodevelopmental disorders and abnormal electroencephalogram (EEG) findings. We present 4 affected patients from a family with a pathogenic mutation in KCNQ3 with a unique constellation of clinical findings.

METHODS

A family of 3 affected siblings and mother sharing a KCNQ3 pathogenic variant are described, including clinical history, genetic results, and EEG and magnetic resonance imaging (MRI) findings.

RESULTS

This family shows a variety of clinical manifestations, including neonatal seizures, developmental delays, autism spectrum disorder, and anxiety. One child developed absence epilepsy, 2 children have infrequent convulsive seizures that have persisted into childhood, and their parent developed adult-onset epilepsy. An underlying c.1091G>A (R364H) variant in KCNQ3 was found in all affected individuals.

CONCLUSIONS

The phenotypic variability of KCNQ3 channelopathies continues to expand as more individuals and families are described, and the variant identified in this family adds to the understanding of the manifestations of KCNQ3-related disorders.

Multidisciplinary Consulting Team for Complicated Cases of Neurodevelopmental and Neurobehavioral Disorders: Assessing the Opportunities and Challenges of Integrating Pharmacogenomics into a Team Setting

Neurodevelopmental disorders have steadily increased in incidence in the United States. Over the past decade, there have been significant changes in clinical diagnoses and treatments some of which are due to the increasing adoption of pharmacogenomics (PGx) by clinicians. In this pilot study, a multidisciplinary team at the Arkansas Children’s Hospital North West consulted on 27 patients referred for difficult-to-manage neurodevelopmental and/or neurobehavioral disorders. The 27 patients were evaluated by the team using records review, team discussion, and pharmacogenetic testing. OneOme RightMed^® (Minneapolis, MN, USA) and the Arkansas Children’s Hospital comprehensive PGx test were used for drug prescribing guidance. Of the 27 patients’ predicted phenotypes, the normal metabolizer was 11 (40.8%) for CYP2C19 and 16 (59.3%) for CYP2D6. For the neurodevelopmental disorders, the most common comorbid conditions included attention-deficit hyperactivity disorder (66.7%), anxiety disorder (59.3%), and autism (40.7%). Following the team assessment and PGx testing, 66.7% of the patients had actionable medication recommendations. This included continuing current therapy, suggesting an appropriate alternative medication, starting a new therapy, or adding adjunct therapy (based on their current medication use). Moreover, 25.9% of patients phenoconverted to a CYP2D6 poor metabolizer. This retrospective chart review pilot study highlights the value of a multidisciplinary treatment approach to deliver precision healthcare by improving physician clinical decisions and potentially impacting patient outcomes. It also shows the feasibility to implement PGx testing in neurodevelopmental/neurobehavioral disorders.

Complex Changes in the Efficiency of the Expression of Many Genes in Monogenic Diseases, Mucopolysaccharidoses, May Arise from Significant Disturbances in the Levels of Factors Involved in the Gene Expression Regulation Processes

Monogenic diseases are primarily caused by mutations in a single gene; thus, they are commonly recognized as genetic disorders with the simplest mechanisms. However, recent studies have indicated that the molecular mechanisms of monogenic diseases can be unexpectedly complicated, and their understanding requires complex studies at the molecular level. Previously, we have demonstrated that in mucopolysaccharidoses (MPS), a group of monogenic lysosomal storage diseases, several hundreds of genes reveal significant changes in the expression of various genes. Although the secondary effects of the primary biochemical defect and the inefficient degradation of glycosaminoglycans (GAGs) might be considered, the scale of the changes in the expression of a large fraction of genes cannot be explained by a block in one biochemical pathway. Here, we demonstrate that in cellular models of 11 types of MPS, the expression of genes coding for proteins involved in the regulation of the expression of many other genes at various stages (such as signal transduction, transcription, splicing, RNA degradation, translation, and others) is significantly disturbed relative to the control cells. This conclusion was based on transcriptomic studies, supported by biochemical analyses of levels of selected proteins encoded by genes revealing an especially high level of dysregulation in MPS (EXOSC9, SRSF10, RPL23, and NOTCH3 proteins were investigated). Interestingly, the reduction in GAGs levels, through the inhibition of their synthesis normalized the amounts of EXOSC9, RPL23, and NOTCH3 in some (but not all) MPS types, while the levels of SRSF10 could not be corrected in this way. These results indicate that different mechanisms are involved in the dysregulation of the expression of various genes in MPS, pointing to a potential explanation for the inability of some therapies (such as enzyme replacement therapy or substrate reduction therapy) to fully correct the physiology of MPS patients. We suggest that the disturbed expression of some genes, which appears as secondary or tertiary effects of GAG storage, might not be reversible, even after a reduction in the amounts of the storage material.

Evaluating risk for alcohol use disorder: Polygenic risk scores and family history

BACKGROUND

Early identification of individuals at high risk for alcohol use disorder (AUD) coupled with prompt interventions could reduce the incidence of AUD. In this study, we investigated whether Polygenic Risk Scores (PRS) can be used to evaluate the risk for AUD and AUD severity (as measured by the number of DSM-5 AUD diagnostic criteria met) and compared their performance with a measure of family history of AUD.

METHODS

We studied individuals of European ancestry from the Collaborative Study on the Genetics of Alcoholism (COGA). DSM-5 diagnostic criteria were available for 7203 individuals, of whom 3451 met criteria for DSM-IV alcohol dependence or DSM-5 AUD and 1616 were alcohol-exposed controls aged ≥21 years with no history of AUD or drug dependence. Further, 4842 individuals had a positive first-degree family history of AUD (FH+), 2722 had an unknown family history (FH?), and 336 had a negative family history (FH-). PRS were derived from a meta-analysis of a genome-wide association study of AUD from the Million Veteran Program and scores from the problem subscale of the Alcohol Use Disorders Identification Test in the UK Biobank. We used mixed models to test the association between PRS and risk for AUD and AUD severity.

RESULTS

AUD cases had higher PRS than controls with PRS increasing as the number of DSM-5 diagnostic criteria increased (p-values ≤ 1.85E-05 ) in the full COGA sample, the FH+ subsample, and the FH? subsample. Individuals in the top decile of PRS had odds ratios (OR) for developing AUD of 1.96 (95% CI: 1.54 to 2.51, p-value = 7.57E-08 ) and 1.86 (95% CI: 1.35 to 2.56, p-value = 1.32E-04 ) in the full sample and the FH+ subsample, respectively. These values are comparable to previously reported ORs for a first-degree family history (1.91 to 2.38) estimated from national surveys. PRS were also significantly associated with the DSM-5 AUD diagnostic criterion count in the full sample, the FH+ subsample, and the FH? subsample (p-values ≤6.7E-11 ). PRS remained significantly associated with AUD and AUD severity after accounting for a family history of AUD (p-values ≤6.8E-10 ).

CONCLUSIONS

Both PRS and family history were associated with AUD and AUD severity, indicating that these risk measures assess distinct aspects of liability to AUD traits.

Interaction Testing and Polygenic Risk Scoring to Estimate the Association of Common Genetic Variants With Treatment Resistance in Schizophrenia

IMPORTANCE

About 20% to 30% of people with schizophrenia have psychotic symptoms that do not respond adequately to first-line antipsychotic treatment. This clinical presentation, chronic and highly disabling, is known as treatment-resistant schizophrenia (TRS). The causes of treatment resistance and their relationships with causes underlying schizophrenia are largely unknown. Adequately powered genetic studies of TRS are scarce because of the difficulty in collecting data from well-characterized TRS cohorts.

OBJECTIVE

To examine the genetic architecture of TRS through the reassessment of genetic data from schizophrenia studies and its validation in carefully ascertained clinical samples.

DESIGN, SETTING, AND PARTICIPANTS

Two case-control genome-wide association studies (GWASs) of schizophrenia were performed in which the case samples were defined as individuals with TRS (n = 10 501) and individuals with non-TRS (n = 20 325). The differences in effect sizes for allelic associations were then determined between both studies, the reasoning being such differences reflect treatment resistance instead of schizophrenia. Genotype data were retrieved from the CLOZUK and Psychiatric Genomics Consortium (PGC) schizophrenia studies. The output was validated using polygenic risk score (PRS) profiling of 2 independent schizophrenia cohorts with TRS and non-TRS: a prevalence sample with 817 individuals (Cardiff Cognition in Schizophrenia [CardiffCOGS]) and an incidence sample with 563 individuals (Genetics Workstream of the Schizophrenia Treatment Resistance and Therapeutic Advances [STRATA-G]).

MAIN OUTCOMES AND MEASURES

GWAS of treatment resistance in schizophrenia. The results of the GWAS were compared with complex polygenic traits through a genetic correlation approach and were used for PRS analysis on the independent validation cohorts using the same TRS definition.

RESULTS

The study included a total of 85 490 participants (48 635 [56.9%] male) in its GWAS stage and 1380 participants (859 [62.2%] male) in its PRS validation stage. Treatment resistance in schizophrenia emerged as a polygenic trait with detectable heritability (1% to 4%), and several traits related to intelligence and cognition were found to be genetically correlated with it (genetic correlation, 0.41-0.69). PRS analysis in the CardiffCOGS prevalence sample showed a positive association between TRS and a history of taking clozapine (r2 = 2.03%; P = .001), which was replicated in the STRATA-G incidence sample (r2 = 1.09%; P = .04).

CONCLUSIONS AND RELEVANCE

In this GWAS, common genetic variants were differentially associated with TRS, and these associations may have been obscured through the amalgamation of large GWAS samples in previous studies of broadly defined schizophrenia. Findings of this study suggest the validity of meta-analytic approaches for studies on patient outcomes, including treatment resistance.

GWAS identifies candidate susceptibility loci and microRNA biomarkers for acute encephalopathy with biphasic seizures and late reduced diffusion

Acute encephalopathy with biphasic seizures and late reduced diffusion (AESD) is a severe encephalopathy preceded by viral infections with high fever. AESD is a multifactorial disease, however, few disease susceptibility genes have previously been identified. Here, we conducted a genome-wide association study (GWAS) and assessed functional variants in non-coding regions to study genetic susceptibility in AESD using 254 Japanese children with AESD and 799 adult healthy controls. We also performed a microRNA enrichment analysis using GWAS statistics to search for candidate biomarkers in AESD. The variant with the lowest p-value, rs1850440, was located in the intron of serine/threonine kinase 39 gene (STK39) on chromosome 2q24.3 (p = 2.44 × 10^-7, odds ratio = 1.71). The minor allele T of rs1850440 correlated with the stronger expression of STK39 in peripheral blood. This variant possessed enhancer histone modification marks in STK39, the encoded protein of which activates the p38 mitogen-activated protein kinase (MAPK) pathway. In the replication study, the odds ratios of three SNPs, including rs1850440, showed the same direction of association with that in the discovery stage GWAS. One of the candidate microRNAs identified by the microRNA enrichment analysis was associated with inflammatory responses regulated by the MAPK pathway. This study identified STK39 as a novel susceptibility locus of AESD, found microRNAs as potential biomarkers, and implicated immune responses and the MAPK cascade in its pathogenesis.

Common Genetic Variants Contribute to Risk of Transposition of the Great Arteries

RATIONALE

Dextro-transposition of the great arteries (D-TGA) is a severe congenital heart defect which affects approximately 1 in 4,000 live births. While there are several reports of D-TGA patients with rare variants in individual genes, the majority of D-TGA cases remain genetically elusive. Familial recurrence patterns and the observation that most cases with D-TGA are sporadic suggest a polygenic inheritance for the disorder, yet this remains unexplored.

OBJECTIVE

We sought to study the role of common single nucleotide polymorphisms (SNPs) in risk for D-TGA.

METHODS AND RESULTS

We conducted a genome-wide association study in an international set of 1,237 patients with D-TGA and identified a genome-wide significant susceptibility locus on chromosome 3p14.3, which was subsequently replicated in an independent case-control set (rs56219800, meta-analysis P=8.6x10-10, OR=0.69 per C allele). SNP-based heritability analysis showed that 25% of variance in susceptibility to D-TGA may be explained by common variants. A genome-wide polygenic risk score derived from the discovery set was significantly associated to D-TGA in the replication set (P=4x10-5). The genome-wide significant locus (3p14.3) co-localizes with a putative regulatory element that interacts with the promoter of WNT5A, which encodes the Wnt Family Member 5A protein known for its role in cardiac development in mice. We show that this element drives reporter gene activity in the developing heart of mice and zebrafish and is bound by the developmental transcription factor TBX20. We further demonstrate that TBX20 attenuates Wnt5a expression levels in the developing mouse heart.

CONCLUSIONS

This work provides support for a polygenic architecture in D-TGA and identifies a susceptibility locus on chromosome 3p14.3 near WNT5A. Genomic and functional data support a causal role of WNT5A at the locus.

Modeling Somatic Mutations Associated With Neurodevelopmental Disorders in Human Brain Organoids

Neurodevelopmental disorders (NDDs) are a collection of diseases with early life onset that often present with developmental delay, cognitive deficits, and behavioral conditions. In some cases, severe outcomes such as brain malformations and intractable epilepsy can occur. The mutations underlying NDDs may be inherited or de novo, can be gain- or loss-of-function, and can affect one or more genes. Recent evidence indicates that brain somatic mutations contribute to several NDDs, in particular malformations of cortical development. While advances in sequencing technologies have enabled the detection of these somatic mutations, the mechanisms by which they alter brain development and function are not well understood due to limited model systems that recapitulate these events. Human brain organoids have emerged as powerful models to study the early developmental events of the human brain. Brain organoids capture the developmental progression of the human brain and contain human-enriched progenitor cell types. Advances in human stem cell and genome engineering provide an opportunity to model NDD-associated somatic mutations in brain organoids. These organoids can be tracked throughout development to understand the impact of somatic mutations on early human brain development and function. In this review, we discuss recent evidence that somatic mutations occur in the developing human brain, that they can lead to NDDs, and discuss how they could be modeled using human brain organoids.

Development of prefrontal cortex

During evolution, the cerebral cortex advances by increasing in surface and the introduction of new cytoarchitectonic areas among which the prefrontal cortex (PFC) is considered to be the substrate of highest cognitive functions. Although neurons of the PFC are generated before birth, the differentiation of its neurons and development of synaptic connections in humans extend to the 3rd decade of life. During this period, synapses as well as neurotransmitter systems including their receptors and transporters, are initially overproduced followed by selective elimination. Advanced methods applied to human and animal models, enable investigation of the cellular mechanisms and role of specific genes, non-coding regulatory elements and signaling molecules in control of prefrontal neuronal production and phenotypic fate, as well as neuronal migration to establish layering of the PFC. Likewise, various genetic approaches in combination with functional assays and immunohistochemical and imaging methods reveal roles of neurotransmitter systems during maturation of the PFC. Disruption, or even a slight slowing of the rate of neuronal production, migration and synaptogenesis by genetic or environmental factors, can induce gross as well as subtle changes that eventually can lead to cognitive impairment. An understanding of the development and evolution of the PFC provide insight into the pathogenesis and treatment of congenital neuropsychiatric diseases as well as idiopathic developmental disorders that cause intellectual disabilities.

Contribution of schizophrenia polygenic burden to longitudinal phenotypic variance in 22q11.2 deletion syndrome

While the recurrent 22q11.2 deletion is one of the strongest genetic risk factors for schizophrenia (SCZ), variability of its associated neuropsychiatric endophenotypes reflects its incomplete penetrance for psychosis development. To assess whether this phenotypic variability is linked to common variants associated with SCZ, we studied the association between SCZ polygenic risk score (PRS) and longitudinally acquired phenotypic information of the Swiss 22q11.2DS cohort (n = 97, 50% females, mean age 17.7 yr, mean visit interval 3.8 yr). The SCZ PRS with the best predictive performance was ascertained in the Estonian Biobank (n = 201,146) with LDpred. The infinitesimal SCZ PRS model showed the strongest capacity in discriminating SCZ cases from controls with one SD difference in SCZ PRS corresponding to an odds ratio (OR) of 1.73 (95% CI 1.57-1.90, P = 1.47 × 10^-29). In 22q11.2 patients, random-effects ordinal regression modelling using longitudinal data showed SCZ PRS to have the strongest effect on social anhedonia (OR = 2.09, P = 0.0002), and occupational functioning (OR = 1.82, P = 0.0003) within the negative symptoms course, and dysphoric mood (OR = 2.00, P = 0.002) and stress intolerance (OR = 1.76, P = 0.0002) within the general symptoms course. Genetic liability for SCZ was additionally associated with full scale cognitive decline (β = -0.25, P = 0.02) and with longitudinal volumetric reduction of the right and left hippocampi (β = -0.28, P = 0.005; β = -0.23, P = 0.02, respectively). Our results indicate that the polygenic contribution to SCZ acts upon the threshold-lowering first hit (i.e., the deletion). It modifies the endophenotypes of 22q11.2DS and augments the derailment of developmental trajectories of negative and general symptoms, cognition, and hippocampal volume.

Application and research progress of machine learning in the diagnosis and treatment of neurodevelopmental disorders in children

The prevalence of neurodevelopment disorders (NDDs) among children has been on the rise. This has affected the health and social life of children. This condition has also imposed a huge economic burden on families and health care systems. Currently, it is difficult to perform early diagnosis of NDDs, which results in delayed intervention. For this reason, patients with NDDs have a prognosis. In recent years, machine learning (ML) technology, which integrates artificial intelligence technology and medicine, has been applied in the early detection and prediction of diseases based on data mining. This paper reviews the progress made in the application of ML in the diagnosis and treatment of NDDs in children based on supervised and unsupervised learning tools. The data reviewed here provide new perspectives on early diagnosis and treatment of NDDs.

Rare variants in the outcome of social skills group training for autism

Exome sequencing has been proposed as the first-tier genetic testing in autism spectrum disorder (ASD). Here, we performed exome sequencing in autistic individuals with average to high intellectual abilities (N = 207) to identify molecular diagnoses and genetic modifiers of intervention outcomes of social skills group training (SSGT) or standard care. We prioritized variants of clinical significance (VCS), variants of uncertain significance (VUS) and generated a pilot scheme to calculate genetic scores of rare and common variants in ASD-related gene pathways. Mixed linear models were used to test the association between the carrier status of VCS/VUS or the genetic scores with intervention outcomes measured by the social responsiveness scale. Additionally, we combined behavioral and genetic features using a machine learning (ML) model to predict the individual response. We showed a rate of 4.4% and 11.3% of VCS and VUS in the cohort, respectively. Individuals with VCS or VUS had improved significantly less after standard care than non-carriers at post-intervention (β = 9.35; p = 0.036), while no such association was observed for SSGT (β = -2.50; p = 0.65). Higher rare variant genetic scores for synaptic transmission and regulation of transcription from RNA polymerase II were separately associated with less beneficial (β = 8.30, p = 0.0044) or more beneficial (β = -6.79, p = 0.014) effects after SSGT compared with standard care at follow-up, respectively. Our ML model showed the importance of rare variants for outcome prediction. Further studies are needed to understand genetic predisposition to intervention outcomes in ASD.

AUTS2 Gene: Keys to Understanding the Pathogenesis of Neurodevelopmental Disorders

Neurodevelopmental disorders (NDDs), including autism spectrum disorders (ASD) and intellectual disability (ID), are a large group of neuropsychiatric illnesses that occur during early brain development, resulting in a broad spectrum of syndromes affecting cognition, sociability, and sensory and motor functions. Despite progress in the discovery of various genetic risk factors thanks to the development of novel genomics technologies, the precise pathological mechanisms underlying the onset of NDDs remain elusive owing to the profound genetic and phenotypic heterogeneity of these conditions. Autism susceptibility candidate 2 (AUTS2) has emerged as a crucial gene associated with a wide range of neuropsychological disorders, such as ASD, ID, schizophrenia, and epilepsy. AUTS2 has been shown to be involved in multiple neurodevelopmental processes; in cell nuclei, it acts as a key transcriptional regulator in neurodevelopment, whereas in the cytoplasm, it participates in cerebral corticogenesis, including neuronal migration and neuritogenesis, through the control of cytoskeletal rearrangements. Postnatally, AUTS2 regulates the number of excitatory synapses to maintain the balance between excitation and inhibition in neural circuits. In this review, we summarize the knowledge regarding AUTS2, including its molecular and cellular functions in neurodevelopment, its genetics, and its role in behaviors.

Intellectual disability genomics: current state, pitfalls and future challenges

Intellectual disability (ID) can be caused by non-genetic and genetic factors, the latter being responsible for more than 1700 ID-related disorders. The broad ID phenotypic and genetic heterogeneity, as well as the difficulty in the establishment of the inheritance pattern, often result in a delay in the diagnosis. It has become apparent that massive parallel sequencing can overcome these difficulties. In this review we address: (i) ID genetic aetiology, (ii) clinical/medical settings testing, (iii) massive parallel sequencing, (iv) variant filtering and prioritization, (v) variant classification guidelines and functional studies, and (vi) ID diagnostic yield. Furthermore, the need for a constant update of the methodologies and functional tests, is essential. Thus, international collaborations, to gather expertise, data and resources through multidisciplinary contributions, are fundamental to keep track of the fast progress in ID gene discovery.

The Epigenome in Neurodevelopmental Disorders

Neurodevelopmental diseases (NDDs), such as autism spectrum disorders, epilepsy, and schizophrenia, are characterized by diverse facets of neurological and psychiatric symptoms, differing in etiology, onset and severity. Such symptoms include mental delay, cognitive and language impairments, or restrictions to adaptive and social behavior. Nevertheless, all have in common that critical milestones of brain development are disrupted, leading to functional deficits of the central nervous system and clinical manifestation in child- or adulthood. To approach how the different development-associated neuropathologies can occur and which risk factors or critical processes are involved in provoking higher susceptibility for such diseases, a detailed understanding of the mechanisms underlying proper brain formation is required. NDDs rely on deficits in neuronal identity, proportion or function, whereby a defective development of the cerebral cortex, the seat of higher cognitive functions, is implicated in numerous disorders. Such deficits can be provoked by genetic and environmental factors during corticogenesis. Thereby, epigenetic mechanisms can act as an interface between external stimuli and the genome, since they are known to be responsive to external stimuli also in cortical neurons. In line with that, DNA methylation, histone modifications/variants, ATP-dependent chromatin remodeling, as well as regulatory non-coding RNAs regulate diverse aspects of neuronal development, and alterations in epigenomic marks have been associated with NDDs of varying phenotypes. Here, we provide an overview of essential steps of mammalian corticogenesis, and discuss the role of epigenetic mechanisms assumed to contribute to pathophysiological aspects of NDDs, when being disrupted.

Computational identification of variables in neonatal vocalizations predictive for postpubertal social behaviors in a mouse model of 16p11.2 deletion

Autism spectrum disorder (ASD) is often signaled by atypical cries during infancy. Copy number variants (CNVs) provide genetically identifiable cases of ASD, but how early atypical cries predict a later onset of ASD among CNV carriers is not understood in humans. Genetic mouse models of CNVs have provided a reliable tool to experimentally isolate the impact of CNVs and identify early predictors for later abnormalities in behaviors relevant to ASD. However, many technical issues have confounded the phenotypic characterization of such mouse models, including systematically biased genetic backgrounds and weak or absent behavioral phenotypes. To address these issues, we developed a coisogenic mouse model of human proximal 16p11.2 hemizygous deletion and applied computational approaches to identify hidden variables within neonatal vocalizations that have predictive power for postpubertal dimensions relevant to ASD. After variables of neonatal vocalizations were selected by least absolute shrinkage and selection operator (Lasso), random forest, and Markov model, regression models were constructed to predict postpubertal dimensions relevant to ASD. While the average scores of many standard behavioral assays designed to model dimensions did not differentiate a model of 16p11.2 hemizygous deletion and wild-type littermates, specific call types and call sequences of neonatal vocalizations predicted individual variability of postpubertal reciprocal social interaction and olfactory responses to a social cue in a genotype-specific manner. Deep-phenotyping and computational analyses identified hidden variables within neonatal social communication that are predictive of postpubertal behaviors.

Prediction of primary venous thromboembolism based on clinical and genetic factors within the U.K. Biobank

Both clinical and genetic factors drive the risk of venous thromboembolism. However, whether clinically recorded risk factors and genetic variants can be combined into a clinically applicable predictive score remains unknown. Using Cox proportional-hazard models, we analyzed the association of risk factors with the likelihood of venous thromboembolism in U.K. Biobank, a large prospective cohort. We then created a polygenic risk score of 36 single nucleotide polymorphisms and a clinical score determined by age, sex, body mass index, previous cancer diagnosis, smoking status, and fracture in the last 5 years. Participants were at significantly increased risk of venous thromboembolism if they were at high clinical risk (subhazard ratio, 4.37 [95% CI, 3.85-4.97]) or high genetic risk (subhazard ratio, 3.02 [95% CI, 2.63-3.47]) relative to participants at low clinical or genetic risk, respectively. The combined model, consisting of clinical and genetic components, was significantly better than either the clinical or the genetic model alone (P < 0.001). Participants at high risk in the combined score had nearly an eightfold increased risk of venous thromboembolism relative to participants at low risk (subhazard ratio, 7.51 [95% CI, 6.28-8.98]). This risk score can be used to guide decisions regarding venous thromboembolism prophylaxis, although external validation is needed.

Combinatorial patterns of gene expression changes contribute to variable expressivity of the developmental delay-associated 16p12.1 deletion

BACKGROUND

Recent studies have suggested that individual variants do not sufficiently explain the variable expressivity of phenotypes observed in complex disorders. For example, the 16p12.1 deletion is associated with developmental delay and neuropsychiatric features in affected individuals, but is inherited in > 90% of cases from a mildly-affected parent. While children with the deletion are more likely to carry additional "second-hit" variants than their parents, the mechanisms for how these variants contribute to phenotypic variability are unknown.

METHODS

We performed detailed clinical assessments, whole-genome sequencing, and RNA sequencing of lymphoblastoid cell lines for 32 individuals in five large families with multiple members carrying the 16p12.1 deletion. We identified contributions of the 16p12.1 deletion and "second-hit" variants towards a range of expression changes in deletion carriers and their family members, including differential expression, outlier expression, alternative splicing, allele-specific expression, and expression quantitative trait loci analyses.

RESULTS

We found that the deletion dysregulates multiple autism and brain development genes such as FOXP1, ANK3, and MEF2. Carrier children also showed an average of 5323 gene expression changes compared with one or both parents, which matched with 33/39 observed developmental phenotypes. We identified significant enrichments for 13/25 classes of "second-hit" variants in genes with expression changes, where 4/25 variant classes were only enriched when inherited from the noncarrier parent, including loss-of-function SNVs and large duplications. In 11 instances, including for ZEB2 and SYNJ1, gene expression was synergistically altered by both the deletion and inherited "second-hits" in carrier children. Finally, brain-specific interaction network analysis showed strong connectivity between genes carrying "second-hits" and genes with transcriptome alterations in deletion carriers.

CONCLUSIONS

Our results suggest a potential mechanism for how "second-hit" variants modulate expressivity of complex disorders such as the 16p12.1 deletion through transcriptomic perturbation of gene networks important for early development. Our work further shows that family-based assessments of transcriptome data are highly relevant towards understanding the genetic mechanisms associated with complex disorders.

How rare and common risk variation jointly affect liability for autism spectrum disorder

BACKGROUND

Genetic studies have implicated rare and common variations in liability for autism spectrum disorder (ASD). Of the discovered risk variants, those rare in the population invariably have large impact on liability, while common variants have small effects. Yet, collectively, common risk variants account for the majority of population-level variability. How these rare and common risk variants jointly affect liability for individuals requires further study.

METHODS

To explore how common and rare variants jointly affect liability, we assessed two cohorts of ASD families characterized for rare and common genetic variations (Simons Simplex Collection and Population-Based Autism Genetics and Environment Study). We analyzed data from 3011 affected subjects, as well as two cohorts of unaffected individuals characterized for common genetic variation: 3011 subjects matched for ancestry to ASD subjects and 11,950 subjects for estimating allele frequencies. We used genetic scores, which assessed the relative burden of common genetic variation affecting risk of ASD (henceforth "burden"), and determined how this burden was distributed among three subpopulations: ASD subjects who carry a potentially damaging variant implicated in risk of ASD ("PDV carriers"); ASD subjects who do not ("non-carriers"); and unaffected subjects who are assumed to be non-carriers.

RESULTS

Burden harbored by ASD subjects is stochastically greater than that harbored by control subjects. For PDV carriers, their average burden is intermediate between non-carrier ASD and control subjects. Both carrier and non-carrier ASD subjects have greater burden, on average, than control subjects. The effects of common and rare variants likely combine additively to determine individual-level liability.

LIMITATIONS

Only 305 ASD subjects were known PDV carriers. This relatively small subpopulation limits this study to characterizing general patterns of burden, as opposed to effects of specific PDVs or genes. Also, a small fraction of subjects that are categorized as non-carriers could be PDV carriers.

CONCLUSIONS

Liability arising from common and rare risk variations likely combines additively to determine risk of any individual diagnosed with ASD. On average, ASD subjects carry a substantial burden of common risk variation, even if they also carry a rare PDV affecting risk.

Insights into attention-deficit/hyperactivity disorder from recent genetic studies

Attention-deficit/hyperactivity disorder (ADHD) is a common and highly heritable neurodevelopmental disorder (NDD). In this narrative review, we summarize recent advances in quantitative and molecular genetic research from the past 5-10 years. Combined with large-scale international collaboration, these advances have resulted in fast-paced progress in understanding the etiology of ADHD and how genetic risk factors map on to clinical heterogeneity. Studies are converging on a number of key insights. First, ADHD is a highly polygenic NDD with a complex genetic architecture encompassing risk variants across the spectrum of allelic frequencies, which are implicated in neurobiological processes. Second, genetic studies strongly suggest that ADHD diagnosis shares a large proportion of genetic risks with continuously distributed traits of ADHD in the population, with shared genetic risks also seen across development and sex. Third, ADHD genetic risks are shared with those implicated in many other neurodevelopmental, psychiatric and somatic phenotypes. As sample sizes and the diversity of genetic studies continue to increase through international collaborative efforts, we anticipate further success with gene discovery, characterization of how the ADHD phenotype relates to other human traits and growing potential to use genomic risk factors for understanding clinical trajectories and for precision medicine approaches.

Transitions from short to long-term outcomes in pediatric critical care: considerations for clinical practice

Most children are surviving critical illness in highly resourced pediatric intensive care units (PICUs). However, in research studies, many of these children survive with multi-domain health sequelae that has the potential to affect development over many years, termed post-intensive care syndrome-pediatrics (PICS-p). Clinically, there are no recommendations for the assessment and follow-up of children with critical illness as exists for the premature neonatal and congenital heart disease populations. In research studies, primary and secondary outcomes are largely assessed at or prior to hospital discharge, disregarding post-hospital outcomes important to PICU stakeholders. Incorporating longer term outcomes into clinical and research programs, however, can no longer be overlooked. Barriers to outcomes assessments are varied and generalized vs. individualized, but some PICU centers are discovering how to overcome them and are providing this service to families-sometimes specific populations-in need. Research programs and funders are increasingly recognizing the value and need to assess long-term outcomes post-PICU. Finally, we should seek the strong backing of the PICU community and families to insist that long-term outcomes become our new clinical standard of care. PICUs should consider development of a multicenter, multinational collaborative to assess clinical outcomes and optimize care delivery and patient and family outcomes. The aim of this review is to present the potential considerations of implementing long-term clinical follow-up following pediatric critical illness.

Discovery and implications of polygenicity of common diseases

The sequencing of the human genome has allowed the study of the genetic architecture of common diseases: the number of genomic variants that contribute to risk of disease and their joint frequency and effect size distribution. Common diseases are polygenic, with many loci contributing to phenotype, and the cumulative burden of risk alleles determines individual risk in conjunction with environmental factors. Most risk loci occur in noncoding regions of the genome regulating cell- and context-specific gene expression. Although the effect sizes of most risk alleles are small, their cumulative effects in individuals, quantified as a polygenic (risk) score, can identify people at increased risk of disease, thereby facilitating prevention or early intervention.

The Meaning of "Cause" in Genetics

Causation has multiple distinct meanings in genetics. One reason for this is meaning slippage between two concepts of the gene: Mendelian and molecular. Another reason is that a variety of genetic methods address different kinds of causal relationships. Some genetic studies address causes of traits in individuals, which can only be assessed when single genes follow predictable inheritance patterns that reliably cause a trait. A second sense concerns the causes of trait differences within a population. Whereas some single genes can be said to cause population-level differences, most often these claims concern the effects of many genes. Polygenic traits can be understood using heritability estimates, which estimate the relative influences of genetic and environmental differences to trait differences within a population. Attempts to understand the molecular mechanisms underlying polygenic traits have been developed, although causal inference based on these results remains controversial. Genetic variation has also recently been leveraged as a randomizing factor to identify environmental causes of trait differences. This technique-Mendelian randomization-offers some solutions to traditional epidemiological challenges, although it is limited to the study of environments with known genetic influences.

Genome-Wide Association Study Identifies Genetic Risk Factors for Spastic Cerebral Palsy

BACKGROUND

Although many clinical risk factors of spastic cerebral palsy (CP) have been identified, the genetic basis of spastic CP is largely unknown. Here, using whole-genome genetic information linked to a deidentified electronic health record (BioVU) with replication in the UK Biobank and FinnGen, we perform the first genome-wide association study (GWAS) for spastic CP.

OBJECTIVE

To define the genetic basis of spastic CP.

METHODS

Whole-genome data were obtained using the multi-ethnic genotyping array (MEGA) genotyping array capturing single-nucleotide polymorphisms (SNPs), minor allele frequency (MAF) > 0.01, and imputation quality score (r2) > 0.3, imputed based on the 1000 genomes phase 3 reference panel. Threshold for genome-wide significance was defined after Bonferroni correction for the total number of SNPs tested (P < 5.0 × 10-8). Replication analysis (defined as P < .05) was performed in the UK Biobank and FinnGen.

RESULTS

We identify 1 SNP (rs78686911) reaching genome-wide significance with spastic CP. Expression quantitative trait loci (eQTL) analysis suggests that rs78686911 decreases expression of GRIK4, a gene that encodes a high-affinity kainate glutamatergic receptor of largely unknown function. Replication analysis in the UK Biobank and FinnGen reveals additional SNPs in the GRIK4 loci associated with CP.

CONCLUSION

To our knowledge, we perform the first GWAS of spastic CP. Our study indicates that genetic variation contributes to CP risk.

How does genetic variation modify ND-CNV phenotypes?

Rare copy-number variants (CNVs) associated with neurodevelopmental disorders (NDDs), i.e., ND-CNVs, provide an insight into the neurobiology of NDDs and, potentially, a link between biology and clinical outcomes. However, ND-CNVs are characterised by incomplete penetrance resulting in heterogeneous carrier phenotypes, ranging from non-affected to multimorbid psychiatric, neurological, and physical phenotypes. Recent evidence indicates that other variants in the genome, or 'other hits', may partially explain the variable expressivity of ND-CNVs. These may be other rare variants or the aggregated effects of common variants that modify NDD risk. Here we discuss the recent findings, current questions, and future challenges relating to other hits research in the context of ND-CNVs and their potential for improved clinical diagnostics and therapeutics for ND-CNV carriers.

Genetic contributors to risk of schizophrenia in the presence of a 22q11.2 deletion

Schizophrenia occurs in about one in four individuals with 22q11.2 deletion syndrome (22q11.2DS). The aim of this International Brain and Behavior 22q11.2DS Consortium (IBBC) study was to identify genetic factors that contribute to schizophrenia, in addition to the ~20-fold increased risk conveyed by the 22q11.2 deletion. Using whole-genome sequencing data from 519 unrelated individuals with 22q11.2DS, we conducted genome-wide comparisons of common and rare variants between those with schizophrenia and those with no psychotic disorder at age ≥25 years. Available microarray data enabled direct comparison of polygenic risk for schizophrenia between 22q11.2DS and independent population samples with no 22q11.2 deletion, with and without schizophrenia (total n = 35,182). Polygenic risk for schizophrenia within 22q11.2DS was significantly greater for those with schizophrenia (padj = 6.73 × 10-6). Novel reciprocal case-control comparisons between the 22q11.2DS and population-based cohorts showed that polygenic risk score was significantly greater in individuals with psychotic illness, regardless of the presence of the 22q11.2 deletion. Within the 22q11.2DS cohort, results of gene-set analyses showed some support for rare variants affecting synaptic genes. No common or rare variants within the 22q11.2 deletion region were significantly associated with schizophrenia. These findings suggest that in addition to the deletion conferring a greatly increased risk to schizophrenia, the risk is higher when the 22q11.2 deletion and common polygenic risk factors that contribute to schizophrenia in the general population are both present.

Quantitative neurogenetics: applications in understanding disease

Neurodevelopmental and neurodegenerative disorders (NNDs) are a group of conditions with a broad range of core and co-morbidities, associated with dysfunction of the central nervous system. Improvements in high throughput sequencing have led to the detection of putative risk genetic loci for NNDs, however, quantitative neurogenetic approaches need to be further developed in order to establish causality and underlying molecular genetic mechanisms of pathogenesis. Here, we discuss an approach for prioritizing the contribution of genetic risk loci to complex-NND pathogenesis by estimating the possible impacts of these loci on gene regulation. Furthermore, we highlight the use of a tissue-specificity gene expression index and the application of artificial intelligence (AI) to improve the interpretation of the role of genetic risk elements in NND pathogenesis. Given that NND symptoms are associated with brain dysfunction, risk loci with direct, causative actions would comprise genes with essential functions in neural cells that are highly expressed in the brain. Indeed, NND risk genes implicated in brain dysfunction are disproportionately enriched in the brain compared with other tissues, which we refer to as brain-specific expressed genes. In addition, the tissue-specificity gene expression index can be used as a handle to identify non-brain contexts that are involved in NND pathogenesis. Lastly, we discuss how using an AI approach provides the opportunity to integrate the biological impacts of risk loci to identify those putative combinations of causative relationships through which genetic factors contribute to NND pathogenesis.