Whole-exome sequencing identifies genes associated with Tourette’s disorder in multiplex families

Tourette’s Disorder (TD) is a neurodevelopmental disorder (NDD) that affects about 0.7% of the population and is one of the most heritable NDDs. Nevertheless, because of its polygenic nature and genetic heterogeneity, the genetic etiology of TD is not well understood. In this study, we combined the segregation information in 13 TD multiplex families with high-throughput sequencing and genotyping to identify genes associated with TD. Using whole-exome sequencing and genotyping array data, we identified both small and large genetic variants within the individuals. We then combined multiple types of evidence to prioritize candidate genes for TD, including variant segregation pattern, variant function prediction, candidate gene expression, protein–protein interaction network, candidate genes from previous studies, etc. From the 13 families, 71 strong candidate genes were identified, including both known genes for NDDs and novel genes, such as HtrA Serine Peptidase 3 (HTRA3), Cadherin-Related Family Member 1 (CDHR1), and Zinc Finger DHHC-Type Palmitoyltransferase 17 (ZDHHC17). The candidate genes are enriched in several Gene Ontology categories, such as dynein complex and synaptic membrane. Candidate genes and pathways identified in this study provide biological insight into TD etiology and potential targets for future studies.This study was supported by a grant from the National Institute of Mental Health (R01MH092293 to GAH and JAT) and by a grant from the New Jersey Center for Tourette Syndrome (to GAH and JAT). This study was also supported by grants from the National Institute of Mental Health (K08MH099424 to TVF) and the National Institute for Environmental Health Science (R01 ES021462 for YSK and BLL). PM has received grants from the Instituto de Salud Carlos III (PI10/01674, PI13/01461), the Consejería de Economía, Innovación, Ciencia y Empresa de la Junta de Andalucía (CVI-02526, CTS-7685), the Consejería de Salud y Bienestar Social de la Junta de Andalucía (PI-0741/2010, PI-0437-2012, PI-0471-2013), the Sociedad Andaluza de Neurología, the Fundación Alicia Koplowitz, the Fundación Mutua Madrileña, and the Jaques and Gloria Gossweiler Foundation. AM has received grants from the Fundacion Alicia Koplowitz and belongs to the research group of the Comissionat per Universitats i Recerca del Departmanent d’Innovacio (DIUE) 2009SGR1119. AM has received grants from the Deutsche Forschungsgemeinschaft (DFG: MU 1692/3-1, MU 1692/4-1, and FOR 2698). AJW received a Young Investigator Award from Tourette Association of America. IH declares that all research at Great Ormond Street Hospital NHS Foundation Trust and UCL Great Ormond Street Institute of Child Health is made possible by the NIHR Great Ormond Street Hospital Biomedical Research Centre

Long-term influence of normal variation in neonatal characteristics on human brain development

It is now recognized that a number of cognitive, behavioral, and mental health outcomes across the lifespan can be traced to fetal development. Although the direct mediation is unknown, the substantial variance in fetal growth, most commonly indexed by birth weight, may affect lifespan brain development. We investigated effects of normal variance in birth weight on MRI-derived measures of brain development in 628 healthy children, adolescents, and young adults in the large-scale multicenter Pediatric Imaging, Neurocognition, and Genetics study. This heterogeneous sample was recruited through geographically dispersed sites in the United States. The influence of birth weight on cortical thickness, surface area, and striatal and total brain volumes was investigated, controlling for variance in age, sex, household income, and genetic ancestry factors. Birth weight was found to exert robust positive effects on regional cortical surface area in multiple regions as well as total brain and caudate volumes. These effects were continuous across birth weight ranges and ages and were not confined to subsets of the sample. The findings show that (i) aspects of later child and adolescent brain development are influenced at birth and (ii) relatively small differences in birth weight across groups and conditions typically compared in neuropsychiatric research (e.g., Attention Deficit Hyperactivity Disorder, schizophrenia, and personality disorders) may influence group differences observed in brain parameters of interest at a later stage in life. These findings should serve to increase our attention to early influences

Multimodal imaging of the self-regulating developing brain

Self-regulation refers to the ability to control behavior, cognition, and emotions, and self-regulation failure is related to a range of neuropsychiatric problems. It is poorly understood how structural maturation of the brain brings about the gradual improvement in self-regulation during childhood. In a large-scale multicenter effort, 735 children (4-21 y) underwent structural MRI for quantification of cortical thickness and surface area and diffusion tensor imaging for quantification of the quality of major fiber connections. Brain development was related to a standardized measure of cognitive control (the flanker task from the National Institutes of Health Toolbox), a critical component of self-regulation. Ability to inhibit responses and impose cognitive control increased rapidly during preteen years. Surface area of the anterior cingulate cortex accounted for a significant proportion of the variance in cognitive performance. This finding is intriguing, because characteristics of the anterior cingulum are shown to be related to impulse, attention, and executive problems in neurodevelopmental disorders, indicating a neural foundation for self-regulation abilities along a continuum from normality to pathology. The relationship was strongest in the younger children. Properties of large-fiber connections added to the picture by explaining additional variance in cognitive control. Although cognitive control was related to surface area of the anterior cingulate independently of basic processes of mental speed, the relationship between white matter quality and cognitive control could be fully accounted for by speed. The results underscore the need for integration of different aspects of brain maturation to understand the foundations of cognitive development

Analysis of shared heritability in common disorders of the brain

ience, this issue p. eaap8757 Structured Abstract INTRODUCTION Brain disorders may exhibit shared symptoms and substantial epidemiological comorbidity, inciting debate about their etiologic overlap. However, detailed study of phenotypes with different ages of onset, severity, and presentation poses a considerable challenge. Recently developed heritability methods allow us to accurately measure correlation of genome-wide common variant risk between two phenotypes from pools of different individuals and assess how connected they, or at least their genetic risks, are on the genomic level. We used genome-wide association data for 265,218 patients and 784,643 control participants, as well as 17 phenotypes from a total of 1,191,588 individuals, to quantify the degree of overlap for genetic risk factors of 25 common brain disorders. RATIONALE Over the past century, the classification of brain disorders has evolved to reflect the medical and scientific communities' assessments of the presumed root causes of clinical phenomena such as behavioral change, loss of motor function, or alterations of consciousness. Directly observable phenomena (such as the presence of emboli, protein tangles, or unusual electrical activity patterns) generally define and separate neurological disorders from psychiatric disorders. Understanding the genetic underpinnings and categorical distinctions for brain disorders and related phenotypes may inform the search for their biological mechanisms. RESULTS Common variant risk for psychiatric disorders was shown to correlate significantly, especially among attention deficit hyperactivity disorder (ADHD), bipolar disorder, major depressive disorder (MDD), and schizophrenia. By contrast, neurological disorders appear more distinct from one another and from the psychiatric disorders, except for migraine, which was significantly correlated to ADHD, MDD, and Tourette syndrome. We demonstrate that, in the general population, the personality trait neuroticism is significantly correlated with almost every psychiatric disorder and migraine. We also identify significant genetic sharing between disorders and early life cognitive measures (e.g., years of education and college attainment) in the general population, demonstrating positive correlation with several psychiatric disorders (e.g., anorexia nervosa and bipolar disorder) and negative correlation with several neurological phenotypes (e.g., Alzheimer's disease and ischemic stroke), even though the latter are considered to result from specific processes that occur later in life. Extensive simulations were also performed to inform how statistical power, diagnostic misclassification, and phenotypic heterogeneity influence genetic correlations. CONCLUSION The high degree of genetic correlation among many of the psychiatric disorders adds further evidence that their current clinical boundaries do not reflect distinct underlying pathogenic processes, at least on the genetic level. This suggests a deeply interconnected nature for psychiatric disorders, in contrast to neurological disorders, and underscores the need to refine psychiatric diagnostics. Genetically informed analyses may provide important "scaffolding" to support such restructuring of psychiatric nosology, which likely requires incorporating many levels of information. By contrast, we find limited evidence for widespread common genetic risk sharing among neurological disorders or across neurological and psychiatric disorders. We show that both psychiatric and neurological disorders have robust correlations with cognitive and personality measures. Further study is needed to evaluate whether overlapping genetic contributions to psychiatric pathology may influence treatment choices. Ultimately, such developments may pave the way toward reduced heterogeneity and improved diagnosis and treatment of psychiatric disorders

Ultra-rare genetic variation in common epilepsies: a case-control sequencing study

BACKGROUND:Despite progress in understanding the genetics of rare epilepsies, the more common epilepsies have proven less amenable to traditional gene-discovery analyses. We aimed to assess the contribution of ultra-rare genetic variation to common epilepsies. METHODS:We did a case-control sequencing study with exome sequence data from unrelated individuals clinically evaluated for one of the two most common epilepsy syndromes: familial genetic generalised epilepsy, or familial or sporadic non-acquired focal epilepsy. Individuals of any age were recruited between Nov 26, 2007, and Aug 2, 2013, through the multicentre Epilepsy Phenome/Genome Project and Epi4K collaborations, and samples were sequenced at the Institute for Genomic Medicine (New York, USA) between Feb 6, 2013, and Aug 18, 2015. To identify epilepsy risk signals, we tested all protein-coding genes for an excess of ultra-rare genetic variation among the cases, compared with control samples with no known epilepsy or epilepsy comorbidity sequenced through unrelated studies. FINDINGS:We separately compared the sequence data from 640 individuals with familial genetic generalised epilepsy and 525 individuals with familial non-acquired focal epilepsy to the same group of 3877 controls, and found significantly higher rates of ultra-rare deleterious variation in genes established as causative for dominant epilepsy disorders (familial genetic generalised epilepsy: odd ratio [OR] 2·3, 95% CI 1·7-3·2, p=9·1 × 10-8; familial non-acquired focal epilepsy 3·6, 2·7-4·9, p=1·1 × 10-17). Comparison of an additional cohort of 662 individuals with sporadic non-acquired focal epilepsy to controls did not identify study-wide significant signals. For the individuals with familial non-acquired focal epilepsy, we found that five known epilepsy genes ranked as the top five genes enriched for ultra-rare deleterious variation. After accounting for the control carrier rate, we estimate that these five genes contribute to the risk of epilepsy in approximately 8% of individuals with familial non-acquired focal epilepsy. Our analyses showed that no individual gene was significantly associated with familial genetic generalised epilepsy; however, known epilepsy genes had lower p values relative to the rest of the protein-coding genes (p=5·8 × 10-8) that were lower than expected from a random sampling of genes. INTERPRETATION:We identified excess ultra-rare variation in known epilepsy genes, which establishes a clear connection between the genetics of common and rare, severe epilepsies, and shows that the variants responsible for epilepsy risk are exceptionally rare in the general population. Our results suggest that the emerging paradigm of targeting of treatments to the genetic cause in rare devastating epilepsies might also extend to a proportion of common epilepsies. These findings might allow clinicians to broadly explain the cause of these syndromes to patients, and lay the foundation for possible precision treatments in the future. FUNDING:National Institute of Neurological Disorders and Stroke (NINDS), and Epilepsy Research UK

High temporal resolution motion estimation using a self-navigated simultaneous multi-slice echo planar imaging acquisition.

BackgroundSubject motion is known to produce spurious covariance among time-series in functional connectivity that has been reported to induce distance-dependent spurious correlations.PurposeTo present a feasibility study for applying the extended Kalman filter (EKF) framework for high temporal resolution motion correction of resting state functional MRI (rs-fMRI) series using each simultaneous multi-slice (SMS) echo planar imaging (EPI) shot as its own navigator.Study typeProspective feasibility study.Population/subjectsThree human volunteers.Field strength/sequence3T GE DISCOVERY MR750 scanner using a 32-channel head coil. Simultaneous multi-slice rs-fMRI sequence with repetition time (TR)/echo time (TE) = 800/30 ms, and SMS factor 6.AssessmentMotion estimates were computed using two techniques: a conventional rigid-body volume-wise registration; and a high-temporal resolution motion estimation rigid-body approach. The reference image was resampled using the estimates obtained from both approaches and the difference between these predicted volumes and the original moving series was summarized using the normalized mean squared error (NMSE).Statistical testsDirect comparison of NMSE values.ResultsHigh-temporal motion estimation was always superior to volume-wise motion estimation for the sample presented. For staged continuous rotations, the NMSE using high-temporal resolution motion estimates ranged between [0.130, 0.150] for the first volunteer (in-plane rotations), between [0.060, 0.068] for the second volunteer (in-plane rotations), and between [0.063, 0.080] for the third volunteer (through-plane rotations). These values went up to [0.384, 0.464]; [0.136, 0.179]; and [0.080, 0.096], respectively, when using volume-wise motion estimates.Data conclusionAccurate high-temporal rigid-body motion estimates can be obtained for rs-fMRI taking advantage of simultaneous multi-slice EPI sub-TR shots.Level of evidence2 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2018