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Genetic influences on externalizing psychopathology overlap with cognitive functioning and show developmental variation
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- Josephine Mollon, Emma E. M. Knowles, Samuel R. Mathias, Amanda Rodrigue, Tyler M. Moore, Monica E. Calkins, Ruben C. Gur, Juan Manuel Peralta, Daniel J. Weiner, Elise B. Robinson, Raquel E. Gur, John Blangero, Laura Almasy, David C. Glahn
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- Journal:
- European Psychiatry / Volume 64 / Issue 1 / 2021
- Published online by Cambridge University Press:
- 31 March 2021, e29
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- Article
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Background
Questions remain regarding whether genetic influences on early life psychopathology overlap with cognition and show developmental variation.
MethodsUsing data from 9,421 individuals aged 8–21 from the Philadelphia Neurodevelopmental Cohort, factors of psychopathology were generated using a bifactor model of item-level data from a psychiatric interview. Five orthogonal factors were generated: anxious-misery (mood and anxiety), externalizing (attention deficit hyperactivity and conduct disorder), fear (phobias), psychosis-spectrum, and a general factor. Genetic analyses were conducted on a subsample of 4,662 individuals of European American ancestry. A genetic relatedness matrix was used to estimate heritability of these factors, and genetic correlations with executive function, episodic memory, complex reasoning, social cognition, motor speed, and general cognitive ability. Gene × Age analyses determined whether genetic influences on these factors show developmental variation.
ResultsExternalizing was heritable (h2 = 0.46, p = 1 × 10−6), but not anxious-misery (h2 = 0.09, p = 0.183), fear (h2 = 0.04, p = 0.337), psychosis-spectrum (h2 = 0.00, p = 0.494), or general psychopathology (h2 = 0.21, p = 0.040). Externalizing showed genetic overlap with face memory (ρg = −0.412, p = 0.004), verbal reasoning (ρg = −0.485, p = 0.001), spatial reasoning (ρg = −0.426, p = 0.010), motor speed (ρg = 0.659, p = 1x10−4), verbal knowledge (ρg = −0.314, p = 0.002), and general cognitive ability (g)(ρg = −0.394, p = 0.002). Gene × Age analyses revealed decreasing genetic variance (γg = −0.146, p = 0.004) and increasing environmental variance (γe = 0.059, p = 0.009) on externalizing.
ConclusionsCognitive impairment may be a useful endophenotype of externalizing psychopathology and, therefore, help elucidate its pathophysiological underpinnings. Decreasing genetic variance suggests that gene discovery efforts may be more fruitful in children than adolescents or young adults.
4365 Family-Based Study of Sleep in Autism Spectrum Disorder without Intellectual Disability
- Stacey Elkhatib Smidt, Arpita Ghorai, Brielle Gehringer, Holly Dow, Zoe Smernoff, Sara Taylor, Jing Zhang, Daniel Rader, Laura Almasy, Edward Brodkin, Maja Bucan
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- Journal:
- Journal of Clinical and Translational Science / Volume 4 / Issue s1 / June 2020
- Published online by Cambridge University Press:
- 29 July 2020, p. 72
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OBJECTIVES/GOALS: Autism spectrum disorder (ASD) is characterized by difficulties in communication and social interaction as well as restricted and repetitive behaviors. Sleep problems are a common concern in children with ASD that can persist into adulthood. This study aims to further explore sleep in ASD without intellectual disability (ASD w/o ID). METHODS/STUDY POPULATION: We recruited individuals of both sexes with ASD w/o ID (probands) and relatives as part of the Autism Spectrum Program of Excellence (ASPE) at the University of Pennsylvania. Actimetry data were collected via a wrist-worn tri-axial accelerometer for 21 days. Data from 212 participants were considered. We analyzed sleep data using the algorithms GGIR, ChronoSapiens, and PennZzz. The sleep traits of proband and sibling pairs were compared using paired t-test or Wilcoxon signed-rank test. We used the Social Responsiveness Scale, Second Edition (SRS-2) to assess social impairment and restricted/repetitive traits. We compared SRS-2 scores to sleep traits using partial Spearman or Pearson correlations adjusting for age (171 participants). RESULTS/ANTICIPATED RESULTS: Probands demonstrated later sleep onset (p = 0.03), decreased M10 average (10-hour period of highest activity/day; p = 0.006), decreased relative amplitude (measure of rest-activity rhythm; p <0.001), and decreased total daytime activity (p = 0.005) compared to siblings. Regarding social function and restricted/repetitive traits, adult males showed an inverse correlation between SRS-2 total score and sleep efficiency (r = −0.2, p = 0.04) and a positive correlation between SRS-2 total score and intradaily variability (r = 0.3, p = 0.02). Adult females showed an inverse correlation between SRS-2 total score and M10 average (r = −0.3, p = 0.02) and between SRS-2 total score and relative amplitude (self-report r = −0.4, p = 0.001; informant r = −0.3, p = 0.005). DISCUSSION/SIGNIFICANCE OF IMPACT: This study focuses on the analysis of sleep traits in ASD including the relationship between social function and sleep. Thus far, the most robust findings are decreased daytime activity and relative amplitude in individuals with ASD w/o ID compared to siblings. We have also shown that ASD social impairment may be related to sleep dysfunction.
Chapter 11 - Mapping Genes Influencing Human Quantitative Trait Variation
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- By John Blangero, Southwest Foundation for Biomedical Research, Jeff Williams, Southwest Foundation for Biomedical Research, Laura Almasy, Southwest Foundation for Biomedical Research, Sarah Williams-Blangero, Southwest Foundation for Biomedical Research
- Edited by Michael H. Crawford, University of Kansas
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- Book:
- Anthropological Genetics
- Published online:
- 05 June 2012
- Print publication:
- 30 November 2006, pp 306-334
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Summary
Introduction
In the post-genomic era, the genetic analysis of common diseases will be one of the most critically important areas of biomedical science. Over the past two decades, it has become clear that many of the diseases that constitute the major public health burden in the United States – diseases such as diabetes, atherosclerosis, obesity, hypertension, depression, alcoholism, osteoporosis, and cancer – have a substantial genetic component. The genetic architecture of such diseases is complex, however, involving multiple genetic and environmental components and their interactions. The specific quantitative trait loci (QTLs) that are involved in the biological pathways of these diseases, and the individual effects of these QTLs in the general population, are still largely unknown. The stochastic complexity of the genotype-phenotype relationship of a common disease requires that statistical inference plays a prominent role in the dissection of the underlying genetic architecture. However, statistical genetic methods suitable for this immense task are still in their infancy. The genomic localization and identification of QTLs and characterization of their causal functional polymorphisms will require new advanced statistical genetic tools.
Over the past decade, we have been successful in developing the theoretical and empirical foundation requisite to a thorough understanding of the strengths and weaknesses of variance component-based quantitative trait linkage methods. We have incorporated many of our statistical genetic developments into our freely available computer package, SOLAR (Sequential Oligogenic Linkage Analysis Routines) (Almasy and Blangero, 1998).