scholarly journals Modeling familial predictors of proband outcomes in neurogenetic disorders: initial application in XYY syndrome

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Kathleen E. Wilson ◽  
Ari M. Fish ◽  
Catherine Mankiw ◽  
Anastasia Xenophontos ◽  
Allysa Warling ◽  
...  
Keyword(s):  
Cluster Analysis ◽  
Sex Chromosome ◽  
Gene Dosage ◽  
Parental Psychopathology ◽  
Autism Spectrum ◽  
Family Data ◽  
Endocrine Disorders ◽  
Xyy Syndrome ◽  
Increase Risk ◽  
Neurogenetic Disorders

Abstract Background Disorders of gene dosage can significantly increase risk for psychopathology, but outcomes vary greatly amongst carriers of any given chromosomal aneuploidy or sub-chromosomal copy number variation (CNV). One potential path to advance precision medicine for neurogenetic disorders is modeling penetrance in probands relative to observed phenotypes in their non-carrier relatives. Here, we seek to advance this general analytic framework by developing new methods in application to XYY syndrome—a sex chromosome aneuploidy that is known to increase risk for psychopathology. Methods We analyzed a range of cognitive and behavioral domains in XYY probands and their non-carrier family members (n = 58 families), including general cognitive ability (FSIQ), as well as continuous measures of traits related to autism spectrum disorder (ASD) and attention deficit hyperactivity disorder (ADHD). Proband and relative scores were compared using covariance, regression and cluster analysis. Comparisons were made both within and across traits. Results Proband scores were shifted away from family scores with effect sizes varying between 0.9 and 2.4 across traits. Only FSIQ and vocabulary scores showed a significant positive correlation between probands and their non-carrier relatives across families (R2 ~ 0.4). Variability in family FSIQ also cross-predicted variability in proband ASD trait severity. Cluster analysis across all trait-relative pairings revealed that variability in parental psychopathology was more weakly coupled to their XYY versus their euploid offspring. Conclusions We present a suite of generalizable methods for modeling variable penetrance in aneuploidy and CNV carriers using family data. These methods update estimates of phenotypic penetrance for XYY and suggest that the predictive utility of family data is likely to vary for different traits and different gene dosage disorders. Trial registrations ClinicalTrials.govNCT00001246, “89-M-0006: Brain Imaging of Childhood Onset Psychiatric Disorders, Endocrine Disorders and Healthy Controls.” Date of registry: 01 October 1989.

scholarly journals Modeling Familial Predictors of Proband Outcomes in Neurogenetic Disorders: Initial Application in XYY Syndrome

2021 ◽  
Author(s):  
Kathleen E. Wilson ◽  
Ari M. Fish ◽  
Catherine Mankiw ◽  
Anastasia Xenophontos ◽  
Allysa Warling ◽  
...  
Keyword(s):  
Cluster Analysis ◽  
Sex Chromosome ◽  
Gene Dosage ◽  
Parental Psychopathology ◽  
Autism Spectrum ◽  
Family Data ◽  
Endocrine Disorders ◽  
Xyy Syndrome ◽  
Increase Risk ◽  
Neurogenetic Disorders

Abstract Background: Disorders of gene dosage can significantly increase risk for psychopathology, but outcomes vary greatly amongst carriers of any given chromosomal aneuploidy or sub-chromosomal copy number variation (CNV). One potential path to advance precision medicine for neurogenetic disorders is modeling penetrance in probands relative to observed phenotypes in their non-carrier relatives. Here, we seek to advance this general analytic framework by developing new methods in application to XYY syndrome – a sex chromosome aneuploidy that is known to increase risk for psychopathology.Methods: We analyzed a range of cognitive and behavioral domains in XYY probands and their non-carrier family members (n = 58 families), including general cognitive ability (FSIQ), as well as continuous measures of traits related to autism spectrum disorder (ASD) and attention deficit hyperactivity disorder (ADHD). Proband and relative scores were compared using covariance, regression and cluster analysis. Comparisons were made both within and across traits.Results: Proband scores were shifted away from family scores with effect sizes varying between 0.9 and 2.4 across traits. Only FSIQ and vocabulary scores showed a significant positive correlation between probands and their non-carrier relatives across families (R2 ~ 0.4). Variability in family FSIQ also cross-predicted variability in proband ASD trait severity. Cluster analysis across all trait-relative pairings revealed that variability in parental psychopathology was more weakly coupled to their XYY versus their euploid offspring.Conclusions: We present a suite of generalizable methods for modeling variable penetrance in aneuploidy and CNV carriers using family data. These methods update estimates of phenotypic penetrance for XYY and suggests that the predictive utility of family data is likely to vary for different traits and different gene dosage disorders. Trial registrations: ClinicalTrials.gov NCT00001246, “89-M-0006: Brain Imaging of Childhood Onset Psychiatric Disorders, Endocrine Disorders and Healthy Controls.” Date of registry: 01/10/1989.

scholarly journals Modeling Familial Predictors of Proband Outcomes in Neurogenetic Disorders: Initial Application in XYY Syndrome

2020 ◽  
Author(s):  
Kathleen E. Wilson ◽  
Ari M. Fish ◽  
Catherine Mankiw ◽  
Anastasia Xenophontos ◽  
Allysa Warling ◽  
...  
Keyword(s):  
Cluster Analysis ◽  
Sex Chromosome ◽  
Gene Dosage ◽  
Parental Psychopathology ◽  
Autism Spectrum ◽  
Family Data ◽  
Endocrine Disorders ◽  
Xyy Syndrome ◽  
Increase Risk ◽  
Neurogenetic Disorders

Abstract Background: Disorders of gene dosage can significantly increase risk for psychopathology, but outcomes vary greatly amongst carriers of any given chromosomal aneuploidy or sub-chromosomal copy number variation (CNV). One potential path to advance precision medicine for neurogenetic disorders is modeling penetrance in probands relative to observed phenotypes in their non-carrier relatives. Here, we seek to advance this general analytic framework by developing new methods in application to XYY syndrome – a sex chromosome aneuploidy that is known to increase risk for psychopathology.Methods: We analyzed a range of cognitive and behavioral domains in XYY probands and their non-carrier family members (n = 58 families), including general cognitive ability (FSIQ), as well as continuous measures of traits related to autism spectrum disorder (ASD) and attention deficit hyperactivity disorder (ADHD). Proband and relative scores were compared using covariance, regression and cluster analysis. Comparisons were made both within and across traits.Results: Proband scores were shifted away from family scores with effect sizes varying between 0.9 and 2.4 across traits. Only FSIQ and vocabulary scores showed a significant positive correlation between probands and their non-carrier relatives across families (R2 ~ 0.4). Variability in family FSIQ also cross-predicted variability in proband ASD trait severity. Cluster analysis across all trait-relative pairings revealed that variability in parental psychopathology was more weakly coupled to their XYY versus their euploid offspring.Conclusions: We present a suite of generalizable methods for modeling variable penetrance in aneuploidy and CNV carriers using family data. These methods update estimates of phenotypic penetrance for XYY and suggests that the predictive utility of family data is likely to vary for different traits and different gene dosage disorders. Trial registrations: ClinicalTrials.gov NCT00001246, “89-M-0006: Brain Imaging of Childhood Onset Psychiatric Disorders, Endocrine Disorders and Healthy Controls.” Date of registry: 01/10/1989.

scholarly journals Modeling Familial Predictors of Proband Outcomes in Neurogenetic Disorders: Initial Application in XYY Syndrome

2020 ◽  
Author(s):  
Kathleen E. Wilson ◽  
Ari M. Fish ◽  
Catherine Mankiw ◽  
Anastasia Xenophontos ◽  
Allysa Warling ◽  
...  
Keyword(s):  
Cluster Analysis ◽  
Parental Psychopathology ◽  
Autism Spectrum ◽  
Copy Number Variations ◽  
Family Data ◽  
Endocrine Disorders ◽  
Initial Application ◽  
Increase Risk ◽  
Neurogenetic Disorders ◽  
And Cluster Analysis

Abstract Background Genomic copy number variations (CNVs) can significantly increase risk for psychopathology, but outcomes vary greatly amongst carriers of any given CNV. One potential path to advance precision medicine for neurogenetic disorders is modeling penetrance in probands relative to observed phenotypes in their non-carrier relatives. Here, we seek to advance this general analytic framework by developing new methods in application to XYY syndrome.Methods We analyzed a range of cognitive and behavioral domains in XYY probands and their non-carrier family members (n = 58 families), including general cognitive ability (FSIQ), as well as continuous measures of traits related to autism spectrum disorder (ASD) and attention deficit hyperactivity disorder (ADHD). Proband and relative scores were compared using covariance, regression and cluster analysis. Comparisons were made both within and across traits.Results Proband scores were shifted away from family scores with effect sizes varying between 0.9 and 2.4 across traits. Only FSIQ and vocabulary scores showed a significant positive correlation between probands and their non-carrier relatives across families (R2 ~ 0.4). Variability in family FSIQ also cross-predicted variability in proband ASD trait severity. Cluster analysis across all trait-relative pairings revealed that variability in parental psychopathology was more weakly coupled to their XYY versus their euploid offspring.Conclusions We present a suite of generalizable methods for modeling variable penetrance in CNV carriers using family data. These methods advance precision psychiatry by updating estimates of phenotypic penetrance for XYY and suggesting that the predictive utility of family data is likely both trait- and CNV-specific.Trial registrations:ClinicalTrials.gov NCT00001246, “89-M-0006: Brain Imaging of Childhood Onset Psychiatric Disorders, Endocrine Disorders and Healthy Controls.” Date of registry: 01/10/1989.

scholarly journals Abnormal Auditory Mismatch Fields in Children and Adolescents with 47,XYY Syndrome

2019 ◽  
Vol 41 (1-2) ◽  
pp. 123-131 ◽  
Author(s):  
Junko Matsuzaki ◽  
Luke Bloy ◽  
Lisa Blaskey ◽  
Judith Miller ◽  
Emily S. Kuschner ◽  
...  
Keyword(s):  
Y Chromosome ◽  
Language Impairment ◽  
Sex Chromosome ◽  
Autism Spectrum ◽  
High Rate ◽  
Oddball Paradigm ◽  
Tall Stature ◽  
Chromosome Aneuploidy ◽  
Significant Difference ◽  
Xyy Syndrome

47,XYY syndrome (XYY) is one of the common forms of sex chromosome aneuploidy in males. XYY males tend to have tall stature, early speech, motor delays, social and behavioral challenges, and a high rate of language impairment. Recent studies indicate that 20–40% of males with XYY meet diagnostic criteria for autism spectrum disorder (ASD; the rate in the general population is 1–2%). Although many studies have examined the neural correlates of language impairment in ASD, few similar studies have been conducted on individuals with XYY. Studies using magnetoencephalography (MEG) in idiopathic ASD (ASD-I) have demonstrated delayed neurophysiological responses to changes in the auditory stream, revealed in the mismatch negativity or its magnetic counterpart, the mismatch field (MMF). This study investigated whether similar findings are observed in XYY-associated ASD and whether delayed processing is also present in individuals with XYY without ASD. MEG measured MMFs arising from the left and the right superior temporal gyrus during an auditory oddball paradigm with vowel stimuli (/a/ and /u/) in children/adolescents with XYY both with and without a diagnosis of ASD, as well as in those with ASD-I and in typically developing controls (TD). Ninety male participants (6–17 years old) were included in the final analyses (TD, n = 38, 11.50 ± 2.88 years; ASD-I, n = 21, 13.83 ± 3.25 years; XYY without ASD, n = 15, 12.65 ± 3.91 years; XYY with ASD, n = 16, 12.62 ± 3.19 years). The groups did not differ significantly in age (p > 0.05). There was a main effect of group on MMF latency (p < 0.001). Delayed MMF latencies were found in participants with XYY both with and without an ASD diagnosis, as well as in the ASD-I group compared to the TD group (ps < 0.001). Furthermore, participants with XYY (with and without ASD) showed a longer MMF latency than the ASD-I group (ps < 0.001). There was, however, no significant difference in MMF latency between individuals with XYY with ASD and those with XYY without ASD. Delayed MMF latencies were associated with severity of language impairment. Our findings suggest that auditory MMF latency delays are pronounced in this specific Y chromosome aneuploidy disorder, both with and without an ASD diagnosis, and thus may implicate the genes of the Y chromosome in mediating atypical MMF activity.

Improving a Parent Coaching Model of Music Interventions for Young Autistic Children

2021 ◽  
Author(s):  
Eugenia Hernandez Ruiz ◽  
Blair B Braden
Keyword(s):  
Parental Stress ◽  
Family Satisfaction ◽  
Autism Spectrum ◽  
Music Intervention ◽  
Parent Coaching ◽  
Coaching Model ◽  
Parental Responsiveness ◽  
Observational Measures ◽  
Increase Risk ◽  
Parent Child

Abstract Parenting a child on the autism spectrum can be rewarding and enriching, but it may also increase risk of parental fatigue, stress, anxiety, and depression. Parent-mediated interventions contribute to increase family satisfaction and child social communication while helping to decrease parental stress and fatigue. Parent coaching, the education of parents in evidence-based strategies, has become common in the autism field. However, parent coaching in music therapy has only recently emerged and has limited research with families with an autistic member. In this study, we attempted to improve a previously published model of parent coaching, adapting only one aspect of the Early Start Denver Model (ESDM), the sensory social routine (SSR) to create a music intervention. Four parents participated in this 6-session parent coaching study. We compared the SSR-based intervention with and without music, in an alternating treatment design. Measures included parental responsiveness, child receptive and initiation joint attention, parent–child similar affect and synchronized gaze, and the Parent Coaching-ESDM (PC-ESDM) parent fidelity rating system. Results from these observational measures were mixed, with better parental responses in the no-music condition, but improved child responses and parent–child synchrony in the music condition for 3 out of the 4 participants. Parent learning increased for all participants, and 3 out of the 4 reached fidelity (a score of at least 80%), according to the PC-ESDM. Although mixed results were observed across participants, implications for practice are possible. Better outcome measures of this complex intervention are needed.

scholarly journals Dopaminergic Gene Dosage in Autism versus Developmental Delay: From Complex Networks to Machine Learning approaches

2020 ◽  
Author(s):  
André Santos ◽  
Francisco Caramelo ◽  
Joana Barbosa de Melo ◽  
Miguel Castelo-Branco
Keyword(s):  
Machine Learning ◽  
Autism Spectrum Disorders ◽  
Network Analysis ◽  
Developmental Delay ◽  
Gene Dosage ◽  
Genetic Alterations ◽  
Autism Spectrum ◽  
Compact Set ◽  
Neural Basis ◽  
Spectrum Disorders

AbstractThe neural basis of behavioural changes in Autism Spectrum Disorders (ASD) remains a controversial issue. One factor contributing to this challenge is the phenotypic heterogeneity observed in ASD, which suggests that several different system disruptions may contribute to diverse patterns of impairment between and within study samples. Here, we took a retrospective approach, using SFARI data to study ASD by focusing on participants with genetic imbalances targeting the dopaminergic system. Using complex network analysis, we investigated the relations between participants, Gene Ontology (GO) and gene dosage related to dopaminergic neurotransmission from a polygenic point of view. We converted network analysis into a machine learning binary classification problem to differentiate ASD diagnosed participants from DD (developmental delay) diagnosed participants. Using 1846 participants to train a Random Forest algorithm, our best classifier achieved on average a diagnosis predicting accuracy of 85.18% (sd 1.11%) on a test sample of 790 participants using gene dosage features. In addition, we observed that if the classifier uses GO features it was also able to infer a correct response based on the previous examples because it is tied to a set of biological process, molecular functions and cellular components relevant to the problem. This yields a less variable and more compact set of features when comparing with gene dosage classifiers. Other facets of knowledge-based systems approaches addressing ASD through network analysis and machine learning, providing an interesting avenue of research for the future, are presented through the study.Lay SummaryThere are important issues in the differential diagnosis of Autism Spectrum Disorders. Gene dosage effects may be important in this context. In this work, we studied genetic alterations related to dopamine processes that could impact brain development and function of 2636 participants. On average, from a genetic sample we were able to correctly separate autism from developmental delay with an accuracy of 85%.

The Role of Copy Number Variation in Psychiatric Disorders

2018 ◽  
pp. 84-95
Author(s):  
Elliott Rees ◽  
George Kirov
Keyword(s):  
Autism Spectrum Disorders ◽  
Copy Number ◽  
Selection Pressure ◽  
Copy Number Variants ◽  
Autism Spectrum ◽  
High Odds ◽  
Strong Selection ◽  
Increase Risk ◽  
Number Variation

Copy number variants (CNVs) are deletions, duplications, inversions, or translocations of large DNA segments. They can play a significant role in human disease. Thirteen CNVs have received strong statistical support for involvement in schizophrenia. They are all rare in cases (<1%), much rarer among controls, and have high odds ratios (ORs) for causing disease. The same CNVs also increase risk for autism spectrum disorders, developmental delay, and medical/physical comorbidities. The penetrance of these CNVs for any disorder is relatively high, ranging from 10% for 15q11.2 deletions to nearly 100% for deletions at 22q11.2. Strong selection pressure operates against carriers of these CNVs. Most of these are formed by non-allelic homologous recombination (NAHR), which leads to high mutation rates, thus maintaining the rates of these CNVs in the general population, despite the strong selection forces.

Electroencephalographic Examination in the XYY Syndrome and in Klinefelter's Syndrome

1974 ◽  
Vol 125 (586) ◽  
pp. 236-237 ◽  
Author(s):  
Johannes Nielsen ◽  
Takayuki Tsuboi
Keyword(s):  
General Population ◽  
Chromosome Aberrations ◽  
Sex Chromosome ◽  
Klinefelter’S Syndrome ◽  
Klinefelter's Syndrome ◽  
Xyy Syndrome

Previous electroencephalographic studies of persons with sex chromosome aberrations have indicated that there might be more electroencephalographic aberrations in males with double Y and double X than in the general population. A survey of these studies has recently been made by Fenton et al. (1971).

scholarly journals A network of networks approach for modeling interconnected brain tissue-specific networks

2019 ◽  
Vol 35 (17) ◽  
pp. 3092-3101 ◽  
Author(s):  
Hideko Kawakubo ◽  
Yusuke Matsui ◽  
Itaru Kushima ◽  
Norio Ozaki ◽  
Teppei Shimamura
Keyword(s):  
Learning Algorithm ◽  
Simulated Data ◽  
Autism Spectrum ◽  
Supplementary Information ◽  
Sparse Learning ◽  
Topological Information ◽  
Infinite Point ◽  
Neurogenetic Disorders ◽  
Information Matrices ◽  
Network Of Networks

Abstract Motivation Recent sequence-based analyses have identified a lot of gene variants that may contribute to neurogenetic disorders such as autism spectrum disorder and schizophrenia. Several state-of-the-art network-based analyses have been proposed for mechanical understanding of genetic variants in neurogenetic disorders. However, these methods were mainly designed for modeling and analyzing single networks that do not interact with or depend on other networks, and thus cannot capture the properties between interdependent systems in brain-specific tissues, circuits and regions which are connected each other and affect behavior and cognitive processes. Results We introduce a novel and efficient framework, called a ‘Network of Networks’ approach, to infer the interconnectivity structure between multiple networks where the response and the predictor variables are topological information matrices of given networks. We also propose Graph-Oriented SParsE Learning, a new sparse structural learning algorithm for network data to identify a subset of the topological information matrices of the predictors related to the response. We demonstrate on simulated data that propose Graph-Oriented SParsE Learning outperforms existing kernel-based algorithms in terms of F-measure. On real data from human brain region-specific functional networks associated with the autism risk genes, we show that the ‘Network of Networks’ model provides insights on the autism-associated interconnectivity structure between functional interaction networks and a comprehensive understanding of the genetic basis of autism across diverse regions of the brain. Availability and implementation Our software is available from https://github.com/infinite-point/GOSPEL. Supplementary information Supplementary data are available at Bioinformatics online.

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