Copy-number variants in clinical genome sequencing: deployment and interpretation for rare and undiagnosed disease.

PURPOSE: Current diagnostic testing for genetic disorders involves serial use of specialized assays spanning multiple technologies. In principle, genome sequencing (GS) can detect all genomic pathogenic variant types on a single platform. Here we evaluate copy-number variant (CNV) calling as part of a clinically accredited GS test. METHODS: We performed analytical validation of CNV calling on 17 reference samples, compared the sensitivity of GS-based variants with those from a clinical microarray, and set a bound on precision using orthogonal technologies. We developed a protocol for family-based analysis of GS-based CNV calls, and deployed this across a clinical cohort of 79 rare and undiagnosed cases. RESULTS: We found that CNV calls from GS are at least as sensitive as those from microarrays, while only creating a modest increase in the number of variants interpreted (~10 CNVs per case). We identified clinically significant CNVs in 15% of the first 79 cases analyzed, all of which were confirmed by an orthogonal approach. The pipeline also enabled discovery of a uniparental disomy (UPD) and a 50% mosaic trisomy 14. Directed analysis of select CNVs enabled breakpoint level resolution of genomic rearrangements and phasing of de novo CNVs. CONCLUSION: Robust identification of CNVs by GS is possible within a clinical testing environment.

Whole exome sequencing in patients with white matter abnormalities.

Here we report whole exome sequencing (WES) on a cohort of 71 patients with persistently unresolved white matter abnormalities with a suspected diagnosis of leukodystrophy or genetic leukoencephalopathy. WES analyses were performed on trio, or greater, family groups. Diagnostic pathogenic variants were identified in 35% (25 of 71) of patients. Potentially pathogenic variants were identified in clinically relevant genes in a further 7% (5 of 71) of cases, giving a total yield of clinical diagnoses in 42% of individuals. These findings provide evidence that WES can substantially decrease the number of unresolved white matter cases. Ann Neurol 2016;79:1031-1037.

Factors Associated with Uptake of Genetics Services for Hypertrophic Cardiomyopathy.

Hypertrophic cardiomyopathy (HCM) is a common cardiovascular disorder with variable expressivity and incomplete penetrance. Clinical guidelines recommend consultation with a genetics professional as part of an initial assessment for HCM, yet there remains an underutilization of genetics services. We conducted a study to assess factors associated with this underutilization within the framework of the Health Belief Model (HBM). An online survey was completed by 306 affected individuals and at risk family members. Thirty-seven percent of individuals (113/306) had visited a genetics professional for reasons related to HCM. Genetic testing was performed on 53 % (162/306). Individuals who had undergone testing were more likely to have seen a genetics professional (p < 0.001), had relatives with an HCM diagnosis (p = 0.002), and have a known familial mutation (p < 0.001). They were also more likely to agree that genetic testing would satisfy their curiosity (p < 0.001), provide reassurance (p < 0.001), aid family members in making healthcare decisions (p < 0.001), and encourage them to engage in a healthier lifestyle (p = 0.002). The HBM components of cues to action and perceived benefits and barriers had the greatest impact on uptake of genetic testing. In order to ensure optimal counseling and care for individuals and families with HCM, awareness and education around HCM and genetic services should be promoted in both physicians and patients alike.

Inter-subject synchronization of brain responses during natural music listening.

Music is a cultural universal and a rich part of the human experience. However, little is known about common brain systems that support the processing and integration of extended, naturalistic 'real-world' music stimuli. We examined this question by presenting extended excerpts of symphonic music, and two pseudomusical stimuli in which the temporal and spectral structure of the Natural Music condition were disrupted, to non-musician participants undergoing functional brain imaging and analysing synchronized spatiotemporal activity patterns between listeners. We found that music synchronizes brain responses across listeners in bilateral auditory midbrain and thalamus, primary auditory and auditory association cortex, right-lateralized structures in frontal and parietal cortex, and motor planning regions of the brain. These effects were greater for natural music compared to the pseudo-musical control conditions. Remarkably, inter-subject synchronization in the inferior colliculus and medial geniculate nucleus was also greater for the natural music condition, indicating that synchronization at these early stages of auditory processing is not simply driven by spectro-temporal features of the stimulus. Increased synchronization during music listening was also evident in a right-hemisphere fronto-parietal attention network and bilateral cortical regions involved in motor planning. While these brain structures have previously been implicated in various aspects of musical processing, our results are the first to show that these regions track structural elements of a musical stimulus over extended time periods lasting minutes. Our results show that a hierarchical distributed network is synchronized between individuals during the processing of extended musical sequences, and provide new insight into the temporal integration of complex and biologically salient auditory sequences.

Possible precision medicine implications from genetic testing using combined detection of sequence and intragenic copy number variants in a large cohort with childhood epilepsy.

OBJECTIVE: Molecular genetic etiologies in epilepsy have become better understood in recent years, creating important opportunities for precision medicine. Building on these advances, detailed studies of the complexities and outcomes of genetic testing for epilepsy can provide useful insights that inform and refine diagnostic approaches and illuminate the potential for precision medicine in epilepsy. METHODS: We used a multi-gene next-generation sequencing (NGS) panel with simultaneous sequence and exonic copy number variant detection to investigate up to 183 epilepsy-related genes in 9769 individuals. Clinical variant interpretation was performed using a semi-quantitative scoring system based on existing professional practice guidelines. RESULTS: Molecular genetic testing provided a diagnosis in 14.9%-24.4% of individuals with epilepsy, depending on the NGS panel used. More than half of these diagnoses were in children younger than 5 years. Notably, the testing had possible precision medicine implications in 33% of individuals who received definitive diagnostic results. Only 30 genes provided 80% of molecular diagnoses. While most clinically significant findings were single-nucleotide variants, ~15% were other types that are often challenging to detect with traditional methods. In addition to clinically significant variants, there were many others that initially had uncertain significance; reclassification of 1612 such variants with parental testing or other evidence contributed to 18.5% of diagnostic results overall and 6.1% of results with precision medicine implications. SIGNIFICANCE: Using an NGS gene panel with key high-yield genes and robust analytic sensitivity as a first-tier test early in the diagnostic process, especially for children younger than 5 years, can possibly enable precision medicine approaches in a significant number of individuals with epilepsy.

Brain organization underlying superior mathematical abilities in children with autism.

BACKGROUND: Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by social and communication deficits. While such deficits have been the focus of most research, recent evidence suggests that individuals with ASD may exhibit cognitive strengths in domains such as mathematics. METHODS: Cognitive assessments and functional brain imaging were used to investigate mathematical abilities in 18 children with ASD and 18 age-, gender-, and IQ-matched typically developing (TD) children. Multivariate classification and regression analyses were used to investigate whether brain activity patterns during numerical problem solving were significantly different between the groups and predictive of individual mathematical abilities. RESULTS: Children with ASD showed better numerical problem solving abilities and relied on sophisticated decomposition strategies for single-digit addition problems more frequently than TD peers. Although children with ASD engaged similar brain areas as TD children, they showed different multivariate activation patterns related to arithmetic problem complexity in ventral temporal-occipital cortex, posterior parietal cortex, and medial temporal lobe. Furthermore, multivariate activation patterns in ventral temporal-occipital cortical areas typically associated with face processing predicted individual numerical problem solving abilities in children with ASD but not in TD children. CONCLUSIONS: Our study suggests that superior mathematical information processing in children with ASD is characterized by a unique pattern of brain organization and that cortical regions typically involved in perceptual expertise may be utilized in novel ways in ASD. Our findings of enhanced cognitive and neural resources for mathematics have critical implications for educational, professional, and social outcomes for individuals with this lifelong disorder.

Default mode network in childhood autism: posteromedial cortex heterogeneity and relationship with social deficits.

BACKGROUND: The default mode network (DMN), a brain system anchored in the posteromedial cortex, has been identified as underconnected in adults with autism spectrum disorder (ASD). However, to date there have been no attempts to characterize this network and its involvement in mediating social deficits in children with ASD. Furthermore, the functionally heterogeneous profile of the posteromedial cortex raises questions regarding how altered connectivity manifests in specific functional modules within this brain region in children with ASD. METHODS: Resting-state functional magnetic resonance imaging and an anatomically informed approach were used to investigate the functional connectivity of the DMN in 20 children with ASD and 19 age-, gender-, and IQ-matched typically developing (TD) children. Multivariate regression analyses were used to test whether altered patterns of connectivity are predictive of social impairment severity. RESULTS: Compared with TD children, children with ASD demonstrated hyperconnectivity of the posterior cingulate and retrosplenial cortices with predominately medial and anterolateral temporal cortex. In contrast, the precuneus in ASD children demonstrated hypoconnectivity with visual cortex, basal ganglia, and locally within the posteromedial cortex. Aberrant posterior cingulate cortex hyperconnectivity was linked with severity of social impairments in ASD, whereas precuneus hypoconnectivity was unrelated to social deficits. Consistent with previous work in healthy adults, a functionally heterogeneous profile of connectivity within the posteromedial cortex in both TD and ASD children was observed. CONCLUSIONS: This work links hyperconnectivity of DMN-related circuits to the core social deficits in young children with ASD and highlights fundamental aspects of posteromedial cortex heterogeneity.

Brain hyperconnectivity in children with autism and its links to social deficits.

Autism spectrum disorder (ASD), a neurodevelopmental disorder affecting nearly 1 in 88 children, is thought to result from aberrant brain connectivity. Remarkably, there have been no systematic attempts to characterize whole-brain connectivity in children with ASD. Here, we use neuroimaging to show that there are more instances of greater functional connectivity in the brains of children with ASD in comparison to those of typically developing children. Hyperconnectivity in ASD was observed at the whole-brain and subsystems levels, across long- and short-range connections, and was associated with higher levels of fluctuations in regional brain signals. Brain hyperconnectivity predicted symptom severity in ASD, such that children with greater functional connectivity exhibited more severe social deficits. We replicated these findings in two additional independent cohorts, demonstrating again that at earlier ages, the brain of children with ASD is largely functionally hyperconnected in ways that contribute to social dysfunction. Our findings provide unique insights into brain mechanisms underlying childhood autism.

Salience network-based classification and prediction of symptom severity in children with autism.

IMPORTANCE: Autism spectrum disorder (ASD) affects 1 in 88 children and is characterized by a complex phenotype, including social, communicative, and sensorimotor deficits. Autism spectrum disorder has been linked with atypical connectivity across multiple brain systems, yet the nature of these differences in young children with the disorder is not well understood. OBJECTIVES: To examine connectivity of large-scale brain networks and determine whether specific networks can distinguish children with ASD from typically developing (TD) children and predict symptom severity in children with ASD. DESIGN, SETTING, AND PARTICIPANTS: Case-control study performed at Stanford University School of Medicine of 20 children 7 to 12 years old with ASD and 20 age-, sex-, and IQ-matched TD children. MAIN OUTCOMES AND MEASURES: Between-group differences in intrinsic functional connectivity of large-scale brain networks, performance of a classifier built to discriminate children with ASD from TD children based on specific brain networks, and correlations between brain networks and core symptoms of ASD. RESULTS: We observed stronger functional connectivity within several large-scale brain networks in children with ASD compared with TD children. This hyperconnectivity in ASD encompassed salience, default mode, frontotemporal, motor, and visual networks. This hyperconnectivity result was replicated in an independent cohort obtained from publicly available databases. Using maps of each individual's salience network, children with ASD could be discriminated from TD children with a classification accuracy of 78%, with 75% sensitivity and 80% specificity. The salience network showed the highest classification accuracy among all networks examined, and the blood oxygen-level dependent signal in this network predicted restricted and repetitive behavior scores. The classifier discriminated ASD from TD in the independent sample with 83% accuracy, 67% sensitivity, and 100% specificity. CONCLUSIONS AND RELEVANCE: Salience network hyperconnectivity may be a distinguishing feature in children with ASD. Quantification of brain network connectivity is a step toward developing biomarkers for objectively identifying children with ASD.

Multivariate searchlight classification of structural magnetic resonance imaging in children and adolescents with autism.

BACKGROUND: Autism spectrum disorders (ASD) are neurodevelopmental disorders with a prevalence of nearly 1:100. Structural imaging studies point to disruptions in multiple brain areas, yet the precise neuroanatomical nature of these disruptions remains unclear. Characterization of brain structural differences in children with ASD is critical for development of biomarkers that may eventually be used to improve diagnosis and monitor response to treatment. METHODS: We use voxel-based morphometry along with a novel multivariate pattern analysis approach and searchlight algorithm to classify structural magnetic resonance imaging data acquired from 24 children and adolescents with autism and 24 age-, gender-, and IQ-matched neurotypical participants. RESULTS: Despite modest voxel-based morphometry differences, multivariate pattern analysis revealed that the groups could be distinguished with accuracies of approximately 90% based on gray matter in the posterior cingulate cortex, medial prefrontal cortex, and bilateral medial temporal lobes-regions within the default mode network. Abnormalities in the posterior cingulate cortex were associated with impaired Autism Diagnostic Interview communication scores. Gray matter in additional prefrontal, lateral temporal, and subcortical structures also discriminated between groups with accuracies between 81% and 90%. White matter in the inferior fronto-occipital and superior longitudinal fasciculi, and the genu and splenium of the corpus callosum, achieved up to 85% classification accuracy. CONCLUSIONS: Multiple brain regions, including those belonging to the default mode network, exhibit aberrant structural organization in children with autism. Brain-based biomarkers derived from structural magnetic resonance imaging data may contribute to identification of the neuroanatomical basis of symptom heterogeneity and to the development of targeted early interventions.