Different Neurodevelopmental Symptoms Have A Common Genetic Etiology
Abstract
Although neurodevelopmental disorders are demarcated as discrete entities in the Diagnostic Statistical Manual of mental disorders, empirical evidence indicates that there is a high degree of overlap among them. The first aim of this investigation was to explore if a single general factor could account for the large degree of observed overlap among neurodevelopmental problems, and explore whether this potential factor was primarily genetic or environmental in origin. The second aim was to explore whether there was systematic covariation, either genetic or environmental, over and above that contributed by the potential general factor, unique to each syndrome.
Parents of all Swedish 9- and 12-year-old twin pairs born between 1992 and 2002 were targeted for interview regarding problems typical of autism spectrum disorders, ADHD and other neurodevelopmental conditions (response rate: 80 percent). Structural equation modeling was conducted on 6,595 pairs to examine the genetic and environmental structure of 53 neurodevelopmental problems.
One general genetic factor accounted for a large proportion of the phenotypic covariation among the 53 symptoms. Three specific genetic subfactors identified 'impulsivity,' 'learning problems,' and 'tics and autism,' respectively. Three unique environment factors identified 'autism,' 'hyperactivity and impulsivity,' and 'inattention and learning problems,' respectively.
One general genetic factor was responsible for the wide-spread phenotypic overlap among all neurodevelopmental symptoms, highlighting the importance of addressing broad patient needs rather than specific diagnoses. The unique genetic factors may help guide diagnostic nomenclature, whereas the unique environmental factors may highlight that neurodevelopmental symptoms are responsive to change at the individual level and may provide clues into different mechanisms and treatments. Future research would benefit from assessing the general factor separately from specific factors to better understand observed overlap among neurodevelopmental problems.
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Available from: Erik Pettersson, Aug 15, 2014
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- "Finally, although we demonstrate an association between superior visual search at 9 and 15 months and the severity of ASD symptoms at 3 years of age, no association with ADHD or anxiety symptoms was found. Many of the previously identified infant markers of ASD are based on impairments common to multiple neurodevelopmental outcomes (Jones et al., 2014) and it was suggested that common neurodevelopmental disorders may stem from common genetic etiology (Pettersson et al., 2013 ). Yet, superior perception had been singled out as a unique feature of ASD (Happé and Frith, 2006). "
[Show abstract] [Hide abstract] ABSTRACT: An enhanced ability to detect visual targets amongst distractors, known as visual search (VS), has often been documented in Autism Spectrum Disorders (ASD). Yet, it is unclear when this behaviour emerges in development and if it is specific to ASD. We followed up infants at high and low familial risk for ASD to investigate how early VS abilities links to later ASD diagnosis, the potential underlying mechanisms of this association and the specificity of superior VS to ASD. Clinical diagnosis of ASD as well as dimensional measures of ASD, attention-deficit/hyperactivity disorder (ADHD) and anxiety symptoms were ascertained at 3 years. At 9 and 15 months, but not at age 2 years, high-risk children who later met clinical criteria for ASD (HR-ASD) had better VS performance than those without later diagnosis and low-risk controls. Although HR-ASD children were also more attentive to the task at 9 months, this did not explain search performance. Superior VS specifically predicted 3 year-old ASD but not ADHD or anxiety symptoms. Our results demonstrate that atypical perception and core ASD symptoms of social interaction and communication are closely and selectively associated during early development, and suggest causal links between perceptual and social features of ASD.- "For example, current brief screening tools for assessing child mental health, such as the SDQ [50], often do not include an assessment of motor skill. Meanwhile , large-scale epidemiological studies explicitly examining co-occurrence in developmental disorders have often focused their efforts on understanding social, emotional and cognitive aspects of child development at the expense of assessing motor skills with equal rigor [43]. These points run contrary to motor function being acknowledged alongside other Disorders of Psychological Function in the ICD-10 disease classification framework [3]. "
[Show abstract] [Hide abstract] ABSTRACT: Motor coordination impairments frequently co-occur with other developmental disorders and mental health problems in clinically referred populations. But does this reflect a broader dimensional relationship within the general population? A clearer understanding of this relationship might inform improvements in mental health service provision. However, ascertainment and referral bias means that there is limited value in conducting further research with clinically referred samples. We, therefore, conducted a cross-sectional population-based study investigating children’s manual coordination using an objective computerised test. These measures were related to teacher-completed responses on a behavioural screening questionnaire [the Strength and Difficulties Questionnaire (SDQ)]. We sampled 298 children (4–11 years old; 136 males) recruited from the general population. Hierarchical (logistic and linear) regression modelling indicated significant categorical and continuous relationships between manual coordination and overall SDQ score (a dimensional measure of psychopathology). Even after controlling for gender and age, manual coordination explained 15 % of the variance in total SDQ score. This dropped to 9 % after exclusion of participants whose SDQ responses indicated potential mental health problems. These results: (1) indicate that there is a clear relationship between children’s motor and mental health development in community-based samples; (2) demonstrate the relationship’s dimensional nature; and (3) have implications for service provision.- "As we believe that understanding physiological mechanisms should be the proximate goal in psychiatric science, this provides ample motivation for continuing to pursue informative endophenotypes. From single major genes to polygenic control: Emerging roles of common versus rare variants Twin and family based studies have provided estimates of up to 90% heritability for some psychiatric disorders (Pettersson et al., 2013; Plomin, Owen, & Mcguffin, 1994). The publication of the human genome sequence in 2001 and the development of new genomic technologies made genotyping practically available and thus greatly accelerated the pace of identifying genetic factors associated with the causes of mental disorders. "
[Show abstract] [Hide abstract] ABSTRACT: Background and scope: Psychiatric science remains descriptive, with a categorical nosology intended to enhance interobserver reliability. Increased awareness of the mismatch between categorical classifications and the complexity of biological systems drives the search for novel frameworks including discovery science in Big Data. In this review, we provide an overview of incipient approaches, primarily focused on classically categorical diagnoses such as schizophrenia (SZ), autism spectrum disorder (ASD), and attention-deficit/hyperactivity disorder (ADHD), but also reference convincing, if focal, advances in cancer biology, to describe the challenges of Big Data and discovery science, and outline approaches being formulated to overcome existing obstacles. Findings: A paradigm shift from categorical diagnoses to a domain/structure-based nosology and from linear causal chains to complex causal network models of brain-behavior relationship is ongoing. This (r)evolution involves appreciating the complexity, dimensionality, and heterogeneity of neuropsychiatric data collected from multiple sources ('broad' data) along with data obtained at multiple levels of analysis, ranging from genes to molecules, cells, circuits, and behaviors ('deep' data). Both of these types of Big Data landscapes require the use and development of robust and powerful informatics and statistical approaches. Thus, we describe Big Data analysis pipelines and the promise and potential limitations in using Big Data approaches to study psychiatric disorders. Conclusion: We highlight key resources available for psychopathological studies and call for the application and development of Big Data approaches to dissect the causes and mechanisms of neuropsychiatric disorders and identify corresponding biomarkers for early diagnosis.
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