Abstract
Language is one of the cognitive domains often impaired across many neurodevelopmental disorders. While for some disorders the linguistic deficit is the primary impairment (e.g., specific language impairment, SLI), for others it may accompany broader behavioral problems (e.g., autism). The precise nature of this phenotypic overlap has been the subject of debate. Moreover, several studies have found genetic overlaps across neurodevelopmental disorders. This raises the question of whether these genetic overlaps may correlate with phenotypic overlaps and, if so, in what manner. Here, we apply a genome‐wide approach to the study of the linguistic deficit in SLI, autism spectrum disorder (ASD), and attention deficit/hyperactivity disorder (ADHD). Using a discovery genome‐wide association study of SLI, we generate polygenic risk scores (PRS) in an independent sample which includes children with language impairment, SLI, ASD or ADHD and age‐matched controls and perform regression analyses across groups. The SLI‐trained PRS significantly predicted risk in the SLI case–control group (adjusted R 2 = 6.24%; P = 0.024) but not in the ASD or ADHD case‐control groups (adjusted R 2 = 0.0004%, 0.01%; P = 0.984, 0.889, respectively) nor for height, used as a negative control (R 2 = 0.2%; P = 0.452). Additionally, there was a significant difference in the normalized PRS between children with SLI and children with ASD (common language effect size = 0.66; P = 0.044). Our study suggests no additive common‐variant genetic overlap between SLI and ASD and ADHD. This is discussed in the context of phenotypic studies of SLI and related disorders. Autism Res 2020, 13: 369–381. © 2019 The Authors. Autism Research published by International Society for Autism Research published by Wiley Periodicals, Inc.Lay SummaryLanguage deficits are characteristic of specific language impairment (SLI), but may also be found in other neurodevelopmental disorders, such as autism spectrum disorder (ASD) and attention deficit/hyperactivity disorder (ADHD). Many studies examined the overlaps and differences across the language deficits in these disorders, but few studies have examined the genetic aspect thereof. In this study, we use a genome‐wide approach to evaluate whether common genetic variants increasing risk of SLI may also be associated with ASD and ADHD in the same manner. Our results suggest that this is not the case, and we discuss this finding in the context of theories concerning the etiologies of these disorders.
Highlights
Specific language impairment (SLI) is a neurodevelopmental disorder characterized as a deficit in the development of language in an otherwise typically developing child [Bishop, 2006], with a prevalence of about 7% in kindergarten children [Norbury et al, 2016; Tomblin et al, 1997]
CNTNAP2 has been highlighted in studies of autism spectrum disorder (ASD) [Alarcon et al, 2008] and attention deficit/hyperactivity disorder (ADHD) [Elia et al, 2009], and rare variants in CNTNAP2 were found in children with childhood apraxia of speech [Worthey et al, 2013]; CMIP has been implicated in ASD [Van der Aa et al, 2012]; ATP2C2 has been implicated in ADHD [Lesch et al, 2008]
These values are close to the reported prevalence for SLI, but they are slightly higher than the population prevalence for ASD and ADHD
Summary
Specific language impairment (SLI) is a neurodevelopmental disorder characterized as a deficit in the development of language in an otherwise typically developing child [Bishop, 2006], with a prevalence of about 7% in kindergarten children [Norbury et al, 2016; Tomblin et al, 1997]. The conclusion of Bishop’s analysis was that a model that included genetic interaction effects could better explain the observed comorbidity of ASD and language impairment and the observed levels of language impairment in ASD and SLI probands and relatives of probands, while still incorporating potential genetic overlaps This is in contrast to a model with purely additive genetic effects shared by both disorders (referred to as additive pleiotropic effects in the paper). Bishop’s analysis showed that observational data fit a model better in which the genetic overlap between SLI and ASD was not purely additive (i.e., it is not likely the case that a genetic variant with a given additive effect on SLI risk has the same effect on ASD risk through the same mechanism)
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