Autistic disorders affect an estimated 60 per 10,000 individuals (1, 2). The traits charateristic of autism spectrum disorders (social communication and language delays, restricted and repetitive interests), however, are normally distributed within the general population (3).

There is substantial IQ- and sex-based variability in the prevalence of both clinical and sub-clinical social communication deficit (4, 5). Specifically, there is evidence that IQ is protective against disordered social communication behavior in women, but less so in men. The degree to which this relationship is stable over time is unclear. Studies examining the longitudinal trajectory of social communication behavior are limited; those including members of the general population have been conducted exclusively among males (6). Further, the relationship between IQ and social developmental trajectory has yet to be established. The primary purpose of this project is to investigate the relationship between social/communication behavior and IQ. Specifically, we are interested in determining whether the longitudinal relationship between social behavior and IQ is modified by gender. The primary hypotheses of this study are outlined below:

1. Social and communication skills (assessed via the Social and Communication Disorders Checklist) will improve for some individuals over time. SCDC scores will be tracked at years 7.5, 10, 13/14, and 15/16.

1a. Higher IQ will predict greater improvement in social and communication skills in women but not in men. IQ will be assessed using the WISC-III at 8 and the WASI at 15/16 (scores will be averaged).

1b. Amongst individuals with diagnosed autistic disorder, asperger's disorder, or pervasive developmental disorder not otherwise specified, higher IQ will predict greater improvement in social and communication skills in women but not in men. Autism will be assessed using NHS data linked to ALSPAC by Williams and colleagues (7).

1c. These relationships will hold after controlling for a number of factors potentially related to both IQ and social behavior as measured. Covariates of interest include: language ability (CCC, WOLD); socioeconomic position (family educational background, indices of multiple deprivation, parental employment status, ethnicity, household income); child behavior (1970 birth cohort scales); hyperactiviy (SCQ); literacy and numeracy (WORD) and; prosocial behavior (parent and teacher reports, CHAMP). These covariates will also be assessed for distributional equivalency between sexes and across IQ groups.

Questions regarding phenotypic consistency are implicit within the above analyses. There exists substantial documentation regarding phenotypic heterogeneity in autism (8, 9). Said heterogeneity influences in the maner in which autism research should be conducted and interpreted (10). The variety of measured ASD-correlates in the ALSPAC data set affords a unique opportunity to examine heterogeneity within a representative sample of individuals diagnosed with autism. Specifically, it permits analysis of phenotypic patterning within subgroups. The secondary aim of this project is to assess the phenotypic consistency of autism 1) across the IQ spectrum and 2) between genders. The primary points of comparison will be the following:

2a. The conditional probability of a diagnosis of autism per a given SCDC score.

2b. Evidence of early childhood social/communication delay relative to overall development. Communication delay will be assessed via the communication and social subscales of the Denver at 6, 18, and 30 months and the MacArthur at 15, 24, and 36 months. Overall development in early childhood will be assessed through the remainder of the Denver at 6, 18, and 30 months and the Griffiths (18 months).

2c. The prevalence of associated phenotypes (Theory of Mind, Non-verbal information)

All longitudinal analyses will be conducted using generalized linear mixed effects models (11). The hypotheses of this project are in line with GLMM as, using this method, population averages are interpreted as conditional upon a system of random effects (individual trajectories). Using a mixed (random) effects technique, we will be able to a) control for within-individual clustering of social communication scores across time and b) evaluate between-individual heterogeneity in trajectories. We will use time-variant predictors when available. Accordingly, many covariates (e.g. verbal ability) have been requested at multiple time points. The comparisons involved in questions 2a-c involve basic statistical associations (probabilities, percents). All analyses will be conducted in SAS v. 9.2.

References

1. Yeargin-Allsopp M, Rice C, Karapurkar T, Doernberg N, Boyle C, Murphy C. Prevalence of Autism in a US Metropolitan Area. Journal of the American Medical Association. 2003;289:49-55.

2. Bertrand J, Mars A, Boyle C, Bove F, Yeargin-Allsopp M, Decoufle P. Prevalence of Autism in a United States Populations: The Brick Township, New Jersey, Investigation. Pediatrics. 2001;108:1155-1161.

3. Constantino J, Todd R. Autistic traits in the general population. Archives of General Psychiatry. 2003;60:524-530.

4. Fombonne E. Epidemiological Surveys of Autism and Other Pervasive Developmental Disorders: An Update. Journal of Autism and Developmental Disorders. 2003;33(4):365-382.

5. Skuse D, Mandy W, Steer C, et al. Social Communication Competence and Functional Adaptation in a General Population of Children: Preliminary Evidence for Sex-by-Verbal IQ Differential Risk. Journal of the American Academy of Child and Adolescent Psychiatry. 2009;48(2):128-137.

6. Constantino J, Abbacchi A, Lavesser P, et al. Developmental course of autistic social impairment in males. Development and Psychopathology. 2009;21:127-138.

7. Williams E, Thomas K, Sidebotham H, Emond A. Prevalence and characteristics of autism spectrum disorders in the ALSPAC cohort. Developmental Medicine and Child Neurology. 2008;50:672-677.

8. Spence S, Cantor R, Chung L, Kim S, Geschwind D, Alarcon M. Stratification Based on Language-Related Endophenotypes in Autism: Attempt to Replicate Reported Linkage. American Journal of Medical Genetics Part B: Neuropsychiatric Genetics. 2006; 141B: 591-598.

9. Spiker D, Lotspeich L, Dimicell S, Myers R, Risch N. Behavioral phenotypic variation in autism multiplex families: Evidence for a continuous severity gradient. American Journal of Medical Genetics Part B: Neuropsychiatric Genetics. 2002; 114: 129-136.

10. Happe F, Ronald A, Plomin R. Time to give up on a single explanation for autism. Nature Neuroscience. 2006;9:1218-1220.

11. Fitzmaurice G, Laird N, Ware J. Applied Longitudinal Analysis. Hoboken, NJ: Wiley; 2004.