Predictive impact of rare genomic copy number variations in siblings of individuals with autism spectrum disorders.

Identification of genetic biomarkers associated with autism spectrum disorders (ASDs) could improve recurrence prediction for families with a child with ASD. Here, we describe clinical microarray findings for 253 longitudinally phenotyped ASD families from the Baby Siblings Research Consortium (BSRC), encompassing 288 infant siblings. By age 3, 103 siblings (35.8%) were diagnosed with ASD and 54 (18.8%) were developing atypically. Thirteen siblings have copy number variants (CNVs) involving ASD-relevant genes: 6 with ASD, 5 atypically developing, and 2 typically developing. Within these families, an ASD-related CNV in a sibling has a positive predictive value (PPV) for ASD or atypical development of 0.83; the Simons Simplex Collection of ASD families shows similar PPVs. Polygenic risk analyses suggest that common genetic variants may also contribute to ASD. CNV findings would have been pre-symptomatically predictive of ASD or atypical development in 11 (7%) of the 157 BSRC siblings who were eventually diagnosed clinically

Diagnostic stability in young children at risk for autism spectrum disorder:A baby siblings research consortium study

BACKGROUND: The diagnosis of autism spectrum disorder (ASD) made before age 3 has been found to be remarkably stable in clinic- and community-ascertained samples. The stability of an ASD diagnosis in prospectively ascertained samples of infants at risk for ASD due to familial factors has not yet been studied, however. The American Academy of Pediatrics recommends intensive surveillance and screening for this high-risk group, which may afford earlier identification. Therefore, it is critical to understand the stability of an ASD diagnosis made before age 3 in young children at familial risk. METHODS: Data were pooled across 7 sites of the Baby Siblings Research Consortium. Evaluations of 418 later-born siblings of children with ASD were conducted at 18, 24, and 36 months of age and a clinical diagnosis of ASD or Not ASD was made at each age. RESULTS: The stability of an ASD diagnosis at 18 months was 93% and at 24 months was 82%. There were relatively few children diagnosed with ASD at 18 or 24 months whose diagnosis was not confirmed at 36 months. There were, however, many children with ASD outcomes at 36 months who had not yet been diagnosed at 18 months (63%) or 24 months (41%). CONCLUSIONS: The stability of an ASD diagnosis in this familial-risk sample was high at both 18 and 24 months of age and comparable with previous data from clinic- and community-ascertained samples. However, almost half of children with ASD outcomes were not identified as being on the spectrum at 24 months and did not receive an ASD diagnosis until 36 months. Thus, longitudinal follow-up is critical for children with early signs of social-communication difficulties, even if they do not meet diagnostic criteria at initial assessment. A public health implication of these data is that screening for ASD may need to be repeated multiple times in the first years of life. These data also suggest that there is a period of early development in which ASD features unfold and emerge but have not yet reached levels supportive of a diagnosis

Infant EEG activity as a biomarker for autism: a promising approach or a false promise?

The ability to determine an infant's likelihood of developing autism via a relatively simple neurological measure would constitute an important scientific breakthrough. In their recent publication in this journal, Bosl and colleagues claim that a measure of EEG complexity can be used to detect, with very high accuracy, infants at high risk for autism (HRA). On the surface, this appears to be that very scientific breakthrough and as such the paper has received widespread media attention. But a close look at how these high accuracy rates were derived tells a very different story. This stems from a conflation between "high risk" as a population-level property and "high risk" as a property of an individual. We describe the approach of Bosl et al. and examine their results with respect to baseline prevalence rates, the inclusion of which is necessary to distinguish infants with a biological risk of autism from typically developing infants with a sibling with autism. This is an important distinction that should not be overlooked

Sleep onset problems and subcortical development in infants later diagnosed with autism spectrum disorder

Objective: Sleep patterns in children with autism spectrum disorder (ASD) appear to diverge from typical development in the second or third year of life. Little is known, however, about the occurrence of sleep problems in infants who later develop ASD and possible effects on early brain development. In a longitudinal neuroimaging study of infants at familial high or low risk for ASD, parent-reported sleep onset problems were examined in relation to subcortical brain volumes in the first 2 years of life. Methods: A total of 432 infants were included across three study groups: infants at high risk who developed ASD (N=71), infants at high risk who did not develop ASD (N=234), and infants at low risk (N=127). Sleep onset problem scores (derived from an infant temperament measure) were evaluated in relation to longitudinal high-resolution T1 and T2 structural imaging data acquired at 6, 12, and 24 months of age. Results: Sleep onset problems were more common at 6–12 months among infants who later developed ASD. Infant sleep onset problems were related to hippocampal volume trajectories from 6 to 24 months only for infants at high risk who developed ASD. Brain-sleep relationships were specific to the hippocampus; no significant relationships were found with volume trajectories of other subcortical structures examined (the amygdala, caudate, globus pallidus, putamen, and thalamus). Conclusions: These findings provide initial evidence that sleep onset problems in the first year of life precede ASD diagnosis and are associated with altered neurodevelopmental trajectories in infants at high familial risk who go on to develop ASD. If replicated, these findings could provide new insights into a potential role of sleep difficulties in the development of ASD

Commentary: Sex difference differences? A reply to Constantino Dr Meng-Chuan Lai

Messinger et al. found a 3.18 odds ratio of male to female ASD recurrence in 1241 prospectively followed high-risk (HR) siblings. Among high-risk siblings (with and without ASD), as well as among 583 low-risk controls, girls exhibited higher performance on the Mullen Scales of Early Learning, as well as lower restricted and repetitive behavior severity scores on the Autism Diagnostic Observation Schedule (ADOS) than boys. That is, female-favoring sex differences in developmental performance and autism traits were evident among low-risk and non-ASD high-risk children, as well as those with ASD. Constantino (Mol Autism) suggests that sex differences in categorical ASD outcomes in Messinger et al. should be understood as a female protective effect. We are receptive to Constantino's (Mol Autism) suggestion, and propose that quantitative sex differences in autism-related features are keys to understanding this female protective effect

Subcortical Brain and Behavior Phenotypes Differentiate Infants With Autism Versus Language Delay

Background Younger siblings of children with autism spectrum disorder (ASD) are themselves at increased risk for ASD and other developmental concerns. It is unclear if infants who display developmental concerns, but are unaffected by ASD, share similar or dissimilar behavioral and brain phenotypes to infants with ASD. Most individuals with ASD exhibit heterogeneous difficulties with language, and their receptive-expressive language profiles are often atypical. Yet, little is known about the neurobiology that contributes to these language difficulties. Methods In this study, we used behavioral assessments and structural magnetic resonance imaging to investigate early brain structures and associations with later language skills. High-risk infants who were later diagnosed with ASD (n = 86) were compared with high-risk infants who showed signs of early language delay (n = 41) as well as with high- and low-risk infants who did not have ASD or language delay (n = 255 and 143, respectively). Results Results indicated that diminished language skills were evident at 12 months in infants with ASD and infants with early language delay. At 24 months of age, only the infants with ASD displayed atypical receptive-expressive language profiles. Associations between 12-month subcortical volumes and 24-month language skills were moderated by group status, indicating disordinal brain-behavior associations among infants with ASD and infants with language delay. Conclusions These results suggest that there are different brain mechanisms influencing language development in infants with ASD and infants with language delay, and that the two groups likely experience unique sets of genetic and environmental risk factors

Do Parents Recognize Autistic Deviant Behavior Long before Diagnosis? Taking into Account Interaction Using Computational Methods

BACKGROUND: To assess whether taking into account interaction synchrony would help to better differentiate autism (AD) from intellectual disability (ID) and typical development (TD) in family home movies of infants aged less than 18 months, we used computational methods. METHODOLOGY AND PRINCIPAL FINDINGS: First, we analyzed interactive sequences extracted from home movies of children with AD (N = 15), ID (N = 12), or TD (N = 15) through the Infant and Caregiver Behavior Scale (ICBS). Second, discrete behaviors between baby (BB) and Care Giver (CG) co-occurring in less than 3 seconds were selected as single interactive patterns (or dyadic events) for analysis of the two directions of interaction (CG→BB and BB→CG) by group and semester. To do so, we used a Markov assumption, a Generalized Linear Mixed Model, and non negative matrix factorization. Compared to TD children, BBs with AD exhibit a growing deviant development of interactive patterns whereas those with ID rather show an initial delay of development. Parents of AD and ID do not differ very much from parents of TD when responding to their child. However, when initiating interaction, parents use more touching and regulation up behaviors as early as the first semester. CONCLUSION: When studying interactive patterns, deviant autistic behaviors appear before 18 months. Parents seem to feel the lack of interactive initiative and responsiveness of their babies and try to increasingly supply soliciting behaviors. Thus we stress that credence should be given to parents' intuition as they recognize, long before diagnosis, the pathological process through the interactive pattern with their child

A genome-wide scan for common alleles affecting risk for autism

Although autism spectrum disorders (ASDs) have a substantial genetic basis, most of the known genetic risk has been traced to rare variants, principally copy number variants (CNVs). To identify common risk variation, the Autism Genome Project (AGP) Consortium genotyped 1558 rigorously defined ASD families for 1 million single-nucleotide polymorphisms (SNPs) and analyzed these SNP genotypes for association with ASD. In one of four primary association analyses, the association signal for marker rs4141463, located within MACROD2, crossed the genome-wide association significance threshold of P < 5 × 10−8. When a smaller replication sample was analyzed, the risk allele at rs4141463 was again over-transmitted; yet, consistent with the winner's curse, its effect size in the replication sample was much smaller; and, for the combined samples, the association signal barely fell below the P < 5 × 10−8 threshold. Exploratory analyses of phenotypic subtypes yielded no significant associations after correction for multiple testing. They did, however, yield strong signals within several genes, KIAA0564, PLD5, POU6F2, ST8SIA2 and TAF1C

Construction and validation of an developmental profile assessment tool for children with autistic spectrum disorder

Nos últimos anos a investigação tem dado particular relevância às alterações do Processamento Sensorial nas crianças com perturbações do espectro do autismo (PEA) e a literatura refere que entre 42% a 88% das crianças com PEA apresentam este tipo de disfunção. Nesta linha foi definido um projeto de investigação centrado na construção de uma escala que avalie a tradicional tríade que caracteriza as crianças com PEA (Interação, Comunicação e Comportamento e interesses repetitivos e estereotipados), enriquecida pela inclusão de um novo domínio: o Processamento Sensorial. Com a construção e validação desta escala pretendemos que pais e profissionais utilizem colaborativamente um instrumento de avaliação da intervenção que lhes permita monitorizar o processo de apoio e adequar as suas práticas. Neste artigo descrevemos os procedimentos e os resultados das sucessivas fases de construção do instrumento, desde as análises iniciais mais qualitativas até aos estudos centrados na análise quantitativa dos itens.During the last few years, research has focused on changes in Sensory Processing in children with Autistic Spectrum Disorder (ASD). As a result, literature has shown that between 42% and 88% of children with ASD present this type of disorder. Based on these findings, a research project was designed centring on the construction of a tool to assess the traditional triad that characterizes children with ASD (Interaction, Communication and Behaviour and Repetitive and Stereotyped Interests), to which was added a new domain: Sensory Processing. By constructing and validating this assessment tool, the intention is for parents and professionals to collaboratively apply this intervention assessment instrument in order to monitor the support process and adapt their practices. In this paper, we describe procedures and results of the successive stages entailed in constructing this instrument, from the first primarily qualitative analyses up to the studies centred on the quantitative item analysis.Fundação para a Ciência e Tecnologia (FCT)ABPEE - Associação Brasileira de Pesquisadores em Educação EspecialConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)CAPESMinistério da Educação - BrasilMinistério da Ciência e da Tecnologia - BrasilGoverno Federal - Brasi