Brain morphology, autistic traits, and polygenic risk for autism: A population-based neuroimaging study / Silvia ALEMANY in Autism Research, 14-10 (October 2021)  

Public ISBD [article]  inAutism Research > 14-10 (October 2021) . - p.2085-2099 Titre : Brain morphology, autistic traits, and polygenic risk for autism: A population-based neuroimaging study Type de document : Texte imprimé et/ou numérique Auteurs : Silvia ALEMANY, Auteur ; E. BLOK, Auteur ; P. R. JANSEN, Auteur ; R. L. MUETZEL, Auteur ; T. WHITE, Auteur Article en page(s) : p.2085-2099 Langues : Anglais (eng) Mots-clés : Autism Spectrum Disorder/diagnostic imaging/genetics  Autistic Disorder/diagnostic imaging/genetics  Brain/diagnostic imaging  Child  Humans  Magnetic Resonance Imaging  Neuroimaging  autism  cortical thickness  genetics  gyrification  surface area Index. décimale : PER Périodiques Résumé : Autism spectrum disorders (ASD) are associated with widespread brain alterations. Previous research in our group linked autistic traits with altered gyrification, but without pronounced differences in cortical thickness. Herein, we aim to replicate and extend these findings using a larger and older sample. Additionally, we examined whether (a) brain correlates of autistic traits were associated with polygenic risk scores (PRS) for ASD, and (b) autistic traits are related with brain morphological changes over time in a subset of children with longitudinal data available. The sample included 2400 children from the Generation R cohort. Autistic traits were measured using the Social Responsiveness Scale (SRS) at age 6?years. Gyrification, cortical thickness, surface area, and global morphological measures were obtained from high-resolution structural MRI scans at ages 9-to-12?years. We performed multiple linear regression analyses on a vertex-wise level. Corresponding regions of interest were tested for association with PRS. Results showed that autistic traits were related to (a) lower gyrification in the lateral occipital and the superior and inferior parietal lobes, (b) lower cortical thickness in the superior frontal region, and (c) lower surface area in inferior temporal and rostral middle frontal regions. PRS for ASD and longitudinal analyses showed significant associations that did not survive correction for multiple testing. Our findings support stability in the relationship between higher autistic symptoms and lower gyrification and smaller surface areas in school-aged children. These relationships remained when excluding ASD cases, providing neurobiological evidence for the extension of autistic traits into the general population. LAY SUMMARY: We found that school-aged children with higher levels of autistic traits had smaller total brain volume, cerebellum, cortical thickness, and surface area. Further, we also found differences in the folding patterns of the brain (gyrification). Overall, genetic susceptibility for autism spectrum disorders was not related to these brain regions suggesting that other factors could be involved in their origin. These results remained significant when excluding children with a diagnosis of ASD, providing support for the extension of the relationship between autistic traits and brain findings into the general population. En ligne : http://dx.doi.org/10.1002/aur.2576 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=450 [article] Brain morphology, autistic traits, and polygenic risk for autism: A population-based neuroimaging study [Texte imprimé et/ou numérique] / Silvia ALEMANY, Auteur ; E. BLOK, Auteur ; P. R. JANSEN, Auteur ; R. L. MUETZEL, Auteur ; T. WHITE, Auteur . - p.2085-2099. Langues : Anglais (eng) in Autism Research > 14-10 (October 2021) . - p.2085-2099 Mots-clés : Autism Spectrum Disorder/diagnostic imaging/genetics  Autistic Disorder/diagnostic imaging/genetics  Brain/diagnostic imaging  Child  Humans  Magnetic Resonance Imaging  Neuroimaging  autism  cortical thickness  genetics  gyrification  surface area Index. décimale : PER Périodiques Résumé : Autism spectrum disorders (ASD) are associated with widespread brain alterations. Previous research in our group linked autistic traits with altered gyrification, but without pronounced differences in cortical thickness. Herein, we aim to replicate and extend these findings using a larger and older sample. Additionally, we examined whether (a) brain correlates of autistic traits were associated with polygenic risk scores (PRS) for ASD, and (b) autistic traits are related with brain morphological changes over time in a subset of children with longitudinal data available. The sample included 2400 children from the Generation R cohort. Autistic traits were measured using the Social Responsiveness Scale (SRS) at age 6?years. Gyrification, cortical thickness, surface area, and global morphological measures were obtained from high-resolution structural MRI scans at ages 9-to-12?years. We performed multiple linear regression analyses on a vertex-wise level. Corresponding regions of interest were tested for association with PRS. Results showed that autistic traits were related to (a) lower gyrification in the lateral occipital and the superior and inferior parietal lobes, (b) lower cortical thickness in the superior frontal region, and (c) lower surface area in inferior temporal and rostral middle frontal regions. PRS for ASD and longitudinal analyses showed significant associations that did not survive correction for multiple testing. Our findings support stability in the relationship between higher autistic symptoms and lower gyrification and smaller surface areas in school-aged children. These relationships remained when excluding ASD cases, providing neurobiological evidence for the extension of autistic traits into the general population. LAY SUMMARY: We found that school-aged children with higher levels of autistic traits had smaller total brain volume, cerebellum, cortical thickness, and surface area. Further, we also found differences in the folding patterns of the brain (gyrification). Overall, genetic susceptibility for autism spectrum disorders was not related to these brain regions suggesting that other factors could be involved in their origin. These results remained significant when excluding children with a diagnosis of ASD, providing support for the extension of the relationship between autistic traits and brain findings into the general population. En ligne : http://dx.doi.org/10.1002/aur.2576 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=450

The predictive capacity of psychiatric and psychological polygenic risk scores for distinguishing cases in a child and adolescent psychiatric sample from controls / A. G. JANSEN in Journal of Child Psychology and Psychiatry, 62-9 (September 2021)  

Public ISBD [article]  inJournal of Child Psychology and Psychiatry > 62-9 (September 2021) . - p.1079-1089 Titre : The predictive capacity of psychiatric and psychological polygenic risk scores for distinguishing cases in a child and adolescent psychiatric sample from controls Type de document : Texte imprimé et/ou numérique Auteurs : A. G. JANSEN, Auteur ; P. R. JANSEN, Auteur ; Jeanne E. SAVAGE, Auteur ; J. KRAFT, Auteur ; N. SKARABIS, Auteur ; Tinca J. C. POLDERMAN, Auteur ; G. C. DIELEMAN, Auteur Article en page(s) : p.1079-1089 Langues : Anglais (eng) Mots-clés : Adolescent  Adult  Aged  Aged, 80 and over  Anxiety Disorders/epidemiology/genetics  Attention Deficit Disorder with Hyperactivity  Child  Child, Preschool  Depressive Disorder, Major  Humans  Infant  Middle Aged  Multifactorial Inheritance/genetics  Risk Factors  Young Adult  Genetics  comorbidity  general P factor  neurodevelopmental disorders  psychiatry Index. décimale : PER Périodiques Résumé : BACKGROUND: Psychiatric traits are heritable, highly comorbid and genetically correlated, suggesting that genetic effects that are shared across disorders are at play. The aim of the present study is to quantify the predictive capacity of common genetic variation of a variety of traits, as captured by their PRS, to predict case-control status in a child and adolescent psychiatric sample including controls to reveal which traits contribute to the shared genetic risk across disorders. METHOD: Polygenic risk scores (PRS) of 14 traits were used as predictor phenotypes to predict case-control status in a clinical sample. Clinical cases (N = 1,402), age 1-21, diagnostic categories: Autism spectrum disorders (N = 492), Attention-deficit/ hyperactivity disorders (N = 471), Anxiety (N = 293), disruptive behaviors (N = 101), eating disorders (N = 97), OCD (N = 43), Tic disorder (N = 50), Disorder of infancy, childhood or adolescence NOS (N = 65), depression (N = 64), motor, learning and communication disorders (N = 59), Anorexia Nervosa (N = 48), somatoform disorders (N = 47), Trauma/stress (N = 39) and controls (N = 1,448, age 17-84) of European ancestry. First, these 14 PRS were tested in univariate regression analyses. The traits that significantly predicted case-control status were included in a multivariable regression model to investigate the gain in explained variance when leveraging the genetic effects of multiple traits simultaneously. RESULTS: In the univariate analyses, we observed significant associations between clinical status and the PRS of educational attainment (EA), smoking initiation (SI), intelligence, neuroticism, alcohol dependence, ADHD, major depression and anti-social behavior. EA (p-value: 3.53E-20, explained variance: 3.99%, OR: 0.66), and SI (p-value: 4.77E-10, explained variance: 1.91%, OR: 1.33) were the most predictive traits. In the multivariable analysis with these eight significant traits, EA and SI, remained significant predictors. The explained variance of the PRS in the model with these eight traits combined was 5.9%. CONCLUSION: Our study provides more insights into the genetic signal that is shared between childhood and adolescent psychiatric disorders. As such, our findings might guide future studies on psychiatric comorbidity and offer insights into shared etiology between psychiatric disorders. The increase in explained variance when leveraging the genetic signal of different predictor traits supports a multivariable approach to optimize precision accuracy for general psychopathology. En ligne : http://dx.doi.org/10.1111/jcpp.13370 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=456 [article] The predictive capacity of psychiatric and psychological polygenic risk scores for distinguishing cases in a child and adolescent psychiatric sample from controls [Texte imprimé et/ou numérique] / A. G. JANSEN, Auteur ; P. R. JANSEN, Auteur ; Jeanne E. SAVAGE, Auteur ; J. KRAFT, Auteur ; N. SKARABIS, Auteur ; Tinca J. C. POLDERMAN, Auteur ; G. C. DIELEMAN, Auteur . - p.1079-1089. Langues : Anglais (eng) in Journal of Child Psychology and Psychiatry > 62-9 (September 2021) . - p.1079-1089 Mots-clés : Adolescent  Adult  Aged  Aged, 80 and over  Anxiety Disorders/epidemiology/genetics  Attention Deficit Disorder with Hyperactivity  Child  Child, Preschool  Depressive Disorder, Major  Humans  Infant  Middle Aged  Multifactorial Inheritance/genetics  Risk Factors  Young Adult  Genetics  comorbidity  general P factor  neurodevelopmental disorders  psychiatry Index. décimale : PER Périodiques Résumé : BACKGROUND: Psychiatric traits are heritable, highly comorbid and genetically correlated, suggesting that genetic effects that are shared across disorders are at play. The aim of the present study is to quantify the predictive capacity of common genetic variation of a variety of traits, as captured by their PRS, to predict case-control status in a child and adolescent psychiatric sample including controls to reveal which traits contribute to the shared genetic risk across disorders. METHOD: Polygenic risk scores (PRS) of 14 traits were used as predictor phenotypes to predict case-control status in a clinical sample. Clinical cases (N = 1,402), age 1-21, diagnostic categories: Autism spectrum disorders (N = 492), Attention-deficit/ hyperactivity disorders (N = 471), Anxiety (N = 293), disruptive behaviors (N = 101), eating disorders (N = 97), OCD (N = 43), Tic disorder (N = 50), Disorder of infancy, childhood or adolescence NOS (N = 65), depression (N = 64), motor, learning and communication disorders (N = 59), Anorexia Nervosa (N = 48), somatoform disorders (N = 47), Trauma/stress (N = 39) and controls (N = 1,448, age 17-84) of European ancestry. First, these 14 PRS were tested in univariate regression analyses. The traits that significantly predicted case-control status were included in a multivariable regression model to investigate the gain in explained variance when leveraging the genetic effects of multiple traits simultaneously. RESULTS: In the univariate analyses, we observed significant associations between clinical status and the PRS of educational attainment (EA), smoking initiation (SI), intelligence, neuroticism, alcohol dependence, ADHD, major depression and anti-social behavior. EA (p-value: 3.53E-20, explained variance: 3.99%, OR: 0.66), and SI (p-value: 4.77E-10, explained variance: 1.91%, OR: 1.33) were the most predictive traits. In the multivariable analysis with these eight significant traits, EA and SI, remained significant predictors. The explained variance of the PRS in the model with these eight traits combined was 5.9%. CONCLUSION: Our study provides more insights into the genetic signal that is shared between childhood and adolescent psychiatric disorders. As such, our findings might guide future studies on psychiatric comorbidity and offer insights into shared etiology between psychiatric disorders. The increase in explained variance when leveraging the genetic signal of different predictor traits supports a multivariable approach to optimize precision accuracy for general psychopathology. En ligne : http://dx.doi.org/10.1111/jcpp.13370 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=456

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