31022
A Comparison of Head Circumference Growth Trajectories in the Context of the CHD8 Regulatory Network

Poster Presentation
Thursday, May 2, 2019: 5:30 PM-7:00 PM
Room: 710 (Palais des congres de Montreal)
B. M. Boyd1, A. B. Arnett1, C. M. Hudac1, K. Hoekzema2, T. Turner3, B. J. O'Roak4, E. E. Eichler5 and R. Bernier1, (1)Psychiatry and Behavioral Sciences, University of Washington, Seattle, WA, (2)Genome Sciences, University of Washington, Seattle, WA, (3)University of Washington, Seattle, WA, (4)Molecular and Medical Genetics, Oregon Health & Science University, Portland, OR, (5)Department of Genome Science, University of Washington, Seattle, WA
Background: At least 30% of autism spectrum disorder (ASD) diagnoses are attributable to disruptive genetic events (Torre-Ubieta, Won, Stein, & Geschwind, 2016). Although no single genetic event explains the majority of ASD cases, functional genetic networks and shared deficiencies in neural growth pathways have been identified (Huguet, Ey, & Bourgeron, 2013). Research has examined converging regulatory networks, such as genes targeted by CHD8, a gene strongly associated with ASD (Cotney et al., 2015). One of the most prominent phenotypes in CHD8 is macrocephaly (Bernier et al., 2014; Barnard, Pomaville, & O’Roak, 2015). Despite the relevance of head size to brain development, growth trajectories have not been examined across functional gene classifications.

Objectives: Compare head circumference growth trajectories across functional genetic and clinical categories among individuals with disruptive genetic events associated with ASD either targeted or not targeted by CHD8.

Methods: Seventy-six participants with a disruptive mutation to an ASD-associated gene were included in the analyses (see Table 1). HC measurements were derived from medical record review and medical examination at the research visit. CHD8 target versus non-target functional gene groups were dichotomized based on prior research (Cotney et al., 2015). Participants were characterized as Macrocephalic or Microcephalic if they met criteria at any time point (i.e. +/- 2 population-based z-scores). Random effect, two-level models were tested in Mplus 7.3 with HC and age at the within level, and sex, gene functional category and clinical phenotype at the between level.

Results: Across the full sample, HC increased linearly (B = 2.71, SE = .28, p <.001) and showed quadratic deceleration (B = -.11, SE = .02, p < .001) with age, as shown in Figure 1. Females had smaller mean HC (B = -2.14, SE = .96, p = .026). Both functional gene group and clinical HC characterization moderated growth trajectories. The CHD8 Targets group had steeper linear growth (B = .22, SE = .08, p = .008) than non-Targets. The Macrocephaly group also had greater early linear growth (p < .001) relative to normal and Microcephaly groups. The Microcephaly group did not differ from the Normal group on growth rates (p > .10), indicating small and stable HC. Gene events in the Microcephaly and Macrocephaly groups were largely non-overlapping.

Conclusions: Growth patterns differ significantly within the population of individuals with ASD-associated gene disruptive events, and provide further evidence for discrete macrocephalic and microcephalic groups. Rapid early growth was characteristic of the broader CHD8 Target and Macrocephaly groups, indicating a shared phenotype and post-natal expression of this functional genetic class. In contrast, the Microcephalic group did not show atypical growth post-birth, suggesting related expression of these genes may be constrained to the prenatal period.

Grey matter overgrowth in the first years of life (Courchesne, Campbell, & Solso, 2011) and atypical levels of cerebral fluids (Shen et al., 2013) are common in ASD and may contribute to macro- and microcephalic phenotypes. HC phenotypes associated with genomic subtypes of ASD provide clues to the neurobiological and developmental etiology of neurodevelopmental disorders.