Clinical evaluation of patients with a neuropsychiatric risk copy number variant

Behavioral Phenotypes and Comorbidity in 3q29 Deletion Syndrome: Results from the 3q29 Registry

3q29 deletion syndrome (3q29del) is associated with a significantly increased risk for neurodevelopmental and neuropsychiatric disorders. However, the full spectrum of behavioral phenotypes associated with 3q29del is still evolving. Individuals with 3q29del (n = 96, 60.42% male) or their guardian completed the Achenbach Child or Adult Behavior Checklist (CBCL/ABCL) via the online 3q29 registry (3q29deletion.org). Typically developing controls (n = 57, 49.12% male) were ascertained as a comparison group. We analyzed mean performance on the CBCL/ABCL for individuals with 3q29del and controls across composite, DSM-keyed, and developmental scales; and the relationship between CBCL/ABCL performance and clinical and developmental phenotypes for individuals with 3q29del. Individuals with 3q29del showed significantly elevated behavioral and developmental impairment relative to controls across CBCL/ABCL domains. A substantial proportion of study participants with 3q29del scored in the Borderline or Clinical range for composite and DSM-keyed scales, indicating significant behavioral problems that may require clinical evaluation. We found that the preschool CBCL DSM-keyed autism spectrum problems scale is a potential screening tool for autism spectrum disorder (ASD) for individuals with 3q29del; CBCL/ABCL DSM-keyed scales were not accurate screeners for anxiety disorders or attention-deficit/hyperactivity disorder (ADHD) in our study sample. We identified a high degree of psychiatric comorbidity in individuals with 3q29del, with 60.42% (n = 58) of individuals with 3q29del scoring in the Borderline or Clinical range on two or more DSM-keyed CBCL/ABCL scales. Finally, we found that the degree of developmental delay in participants with 3q29del does not explain the increased behavioral problems observed on the CBCL/ABCL. The CBCL/ABCL can be used as screening tools in populations such as 3q29del, even in the presence of substantial psychiatric comorbidity. These results expand our understanding of the phenotypic spectrum of 3q29del and demonstrate an effective method for recruiting and phenotyping a large sample of individuals with a rare genetic disorder.

Rare CNVs and phenome-wide profiling highlight brain structural divergence and phenotypical convergence

Copy number variations (CNVs) are rare genomic deletions and duplications that can affect brain and behaviour. Previous reports of CNV pleiotropy imply that they converge on shared mechanisms at some level of pathway cascades, from genes to large-scale neural circuits to the phenome. However, existing studies have primarily examined single CNV loci in small clinical cohorts. It remains unknown, for example, how distinct CNVs escalate vulnerability for the same developmental and psychiatric disorders. Here we quantitatively dissect the associations between brain organization and behavioural differentiation across 8 key CNVs. In 534 CNV carriers, we explored CNV-specific brain morphology patterns. CNVs were characteristic of disparate morphological changes involving multiple large-scale networks. We extensively annotated these CNV-associated patterns with ~1,000 lifestyle indicators through the UK Biobank resource. The resulting phenotypic profiles largely overlap and have body-wide implications, including the cardiovascular, endocrine, skeletal and nervous systems. Our population-level investigation established brain structural divergences and phenotypical convergences of CNVs, with direct relevance to major brain disorders.

Identifying the neurodevelopmental and psychiatric signatures of genomic disorders associated with intellectual disability: a machine learning approach

BACKGROUND

Genomic conditions can be associated with developmental delay, intellectual disability, autism spectrum disorder, and physical and mental health symptoms. They are individually rare and highly variable in presentation, which limits the use of standard clinical guidelines for diagnosis and treatment. A simple screening tool to identify young people with genomic conditions associated with neurodevelopmental disorders (ND-GCs) who could benefit from further support would be of considerable value. We used machine learning approaches to address this question.

METHOD

A total of 493 individuals were included: 389 with a ND-GC, mean age = 9.01, 66% male) and 104 siblings without known genomic conditions (controls, mean age = 10.23, 53% male). Primary carers completed assessments of behavioural, neurodevelopmental and psychiatric symptoms and physical health and development. Machine learning techniques (penalised logistic regression, random forests, support vector machines and artificial neural networks) were used to develop classifiers of ND-GC status and identified limited sets of variables that gave the best classification performance. Exploratory graph analysis was used to understand associations within the final variable set.

RESULTS

All machine learning methods identified variable sets giving high classification accuracy (AUROC between 0.883 and 0.915). We identified a subset of 30 variables best discriminating between individuals with ND-GCs and controls which formed 5 dimensions: conduct, separation anxiety, situational anxiety, communication and motor development.

LIMITATIONS

This study used cross-sectional data from a cohort study which was imbalanced with respect to ND-GC status. Our model requires validation in independent datasets and with longitudinal follow-up data for validation before clinical application.

CONCLUSIONS

In this study, we developed models that identified a compact set of psychiatric and physical health measures that differentiate individuals with a ND-GC from controls and highlight higher-order structure within these measures. This work is a step towards developing a screening instrument to identify young people with ND-GCs who might benefit from further specialist assessment.

Psychopathology in adults with copy number variants

BACKGROUND

Copy number variants (CNVs) have been associated with the risk of schizophrenia, autism and intellectual disability. However, little is known about their spectrum of psychopathology in adulthood.

METHODS

We investigated the psychiatric phenotypes of adult CNV carriers and compared probands, who were ascertained through clinical genetics services, with carriers who were not. One hundred twenty-four adult participants (age 18-76), each bearing one of 15 rare CNVs, were recruited through a variety of sources including clinical genetics services, charities for carriers of genetic variants, and online advertising. A battery of psychiatric assessments was used to determine psychopathology.

RESULTS

The frequencies of psychopathology were consistently higher for the CNV group compared to general population rates. We found particularly high rates of neurodevelopmental disorders (NDDs) (48%), mood disorders (42%), anxiety disorders (47%) and personality disorders (73%) as well as high rates of psychiatric multimorbidity (median number of diagnoses: 2 in non-probands, 3 in probands). NDDs [odds ratio (OR) = 4.67, 95% confidence interval (CI) 1.32-16.51; p = 0.017) and psychotic disorders (OR = 6.8, 95% CI 1.3-36.3; p = 0.025) occurred significantly more frequently in probands (N = 45; NDD: 39[87%]; psychosis: 8[18%]) than non-probands (N = 79; NDD: 20 [25%]; psychosis: 3[4%]). Participants also had somatic diagnoses pertaining to all organ systems, particularly conotruncal cardiac malformations (in individuals with 22q11.2 deletion syndrome specifically), musculoskeletal, immunological, and endocrine diseases.

CONCLUSIONS

Adult CNV carriers had a markedly increased rate of anxiety and personality disorders not previously reported and high rates of psychiatric multimorbidity. Our findings support in-depth psychiatric and medical assessments of carriers of CNVs and the establishment of multidisciplinary clinical services.

Sleep disturbance as a transdiagnostic marker of psychiatric risk in children with neurodevelopmental risk genetic conditions

Children with rare neurodevelopmental genetic conditions (ND-GCs) are at high risk for a range of neuropsychiatric conditions. Sleep symptomatology may represent a transdiagnostic risk indicator within this patient group. Here we present data from 629 children with ND-GCs, recruited via the United Kingdom's National Health Service medical genetic clinics. Sibling controls (183) were also invited to take part. Detailed assessments were conducted to characterise the sleep phenotype of children with ND-GCs in comparison to controls. Latent class analysis was conducted to derive subgroups of children with an ND-GC based on sleep symptomatology. Assessment of cognition and psychopathology allowed investigation of whether the sleep phenotypic subgroup was associated with neuropsychiatric outcomes. We found that children with an ND-GC, when compared to control siblings, were at elevated risk of insomnia (ND-GC = 41% vs Controls = 17%, p < 0.001) and of experiencing at least one sleep symptom (ND-GC = 66% vs Controls = 39%, p < 0.001). On average, insomnia was found to have an early onset (2.8 years) in children with an ND-GC and to impact across multiple contexts. Children in subgroups linked to high sleep symptomatology were also at high risk of psychiatric outcomes (OR ranging from 2.0 to 21.5 depending on psychiatric condition). Our findings demonstrate that children with high genetic vulnerability for neurodevelopmental outcomes exhibit high rates of insomnia and sleep symptomatology. Sleep disruption has wide-ranging impacts on psychosocial function, and indexes those children at greater neuropsychiatric risk. Insomnia was found to onset in early childhood, highlighting the potential for early intervention strategies for psychiatric risk informed by sleep profile.

Genes To Mental Health (G2MH): A Framework to Map the Combined Effects of Rare and Common Variants on Dimensions of Cognition and Psychopathology

Rare genomic disorders (RGDs) confer elevated risk for neurodevelopmental psychiatric disorders. In this era of intense genomics discoveries, the landscape of RGDs is rapidly evolving. However, there has not been comparable progress to date in scalable, harmonized phenotyping methods. As a result, beyond associations with categorical diagnoses, the effects on dimensional traits remain unclear for many RGDs. The nature and specificity of RGD effects on cognitive and behavioral traits is an area of intense investigation: RGDs are frequently associated with more than one psychiatric condition, and those studied to date affect, to varying degrees, a broad range of developmental and cognitive functions. Although many RGDs have large effects, phenotypic expression is typically influenced by additional genomic and environmental factors. There is emerging evidence that using polygenic risk scores in individuals with RGDs offers opportunities to refine prediction, thus allowing for the identification of those at greatest risk of psychiatric illness. However, translation into the clinic is hindered by roadblocks, which include limited genetic testing in clinical psychiatry, and the lack of guidelines for following individuals with RGDs, who are at high risk of developing psychiatric symptoms. The Genes to Mental Health Network (G2MH) is a newly funded National Institute of Mental Health initiative that will collect, share, and analyze large-scale data sets combining genomics and dimensional measures of psychopathology spanning diverse populations and geography. The authors present here the most recent understanding of the effects of RGDs on dimensional behavioral traits and risk for psychiatric conditions and discuss strategies that will be pursued within the G2MH network, as well as how expected results can be translated into clinical practice to improve patient outcomes.

Autism: A model of neurodevelopmental diversity informed by genomics

Definitions of autism are constantly in flux and the validity and utility of diagnostic criteria remain hotly debated. The boundaries of autism are unclear and there is considerable heterogeneity within autistic individuals. Autistic individuals experience a range of co-occurring conditions notably including other childhood onset neurodevelopmental conditions such as intellectual disability, epilepsy and ADHD, but also other neuropsychiatric conditions. Recently, the neurodiversity movement has challenged the conception of autism as a medical syndrome defined by functional deficits. Whereas others have argued that autistic individuals with the highest support needs, including those with intellectual disability and limited functional communication, are better represented by a medical model. Genomic research indicates that, rather than being a circumscribed biological entity, autism can be understood in relation to two continua. On the one hand, it can be conceived as lying on a continuum of population variation in social and adaptive functioning traits, reflecting in large part the combination of multiple alleles of small effect. On the other, it can be viewed as lying on a broader neurodevelopmental continuum whereby rare genetic mutations and environmental risk factors impact the developing brain, resulting in a diverse spectrum of outcomes including childhood-onset neurodevelopmental conditions as well as adult-onset psychiatric conditions such as schizophrenia. This model helps us understand heterogeneity within autism and to reconcile the view that autism is a part of natural variability, as advocated by the neurodiversity movement, with the presence of co-occurring disabilities and impairments of function in some autistic individuals.