International Meeting for Autism Research: Genetic Analysis of Latent Phenotypes in Autism Spectrum Disorders

Genetic Analysis of Latent Phenotypes in Autism Spectrum Disorders

Friday, May 21, 2010
Franklin Hall B Level 4 (Philadelphia Marriott Downtown)
10:00 AM
X. Q. Liu , Program in Genetics and Genome Biology, The Hospital for Sick Children, Toronto, ON, Canada
S. Georgiades , The Offord Centre for Child Studies, McMaster University, Hamilton, ON, Canada
E. Duku , The Offord Centre for Child Studies, McMaster University, Hamilton, ON, Canada
A. P. Thompson , The Offord Centre for Child Studies, McMaster University, Hamilton, ON, Canada
A. D. Paterson , Program in Genetics and Genome Biology, The Hospital for Sick Children, Toronto, ON, Canada
P. Szatmari , The Offord Centre for Child Studies, McMaster University, Hamilton, ON, Canada
Background: A better understanding of the latent phenotype constructs of autism spectrum disorders (ASD) will help us identify the etiological factors of the disorder. To date, even though many studies have been conducted to detect latent constructs of ASD, no study has systematically explored the genetic components of ASD using latent phenotypes derived from factor analysis. 

Objectives: 1) To detect latent phenotype constructs underlying ASD; 2) To identify genetic loci that are linked to these latent phenotypes.

Methods: Exploratory factor analyses were applied to two independent datasets using 28 selected ADI-R algorithm items. The first dataset was from the Autism Genome Project (AGP) phase I (1,009 individuals from 618 families); the second was from the AGP phase II (1,034 unrelated individuals). Latent phenotypes derived from the factor analysis were then used in genome-wide multipoint variance components linkage analyses.

Results: Six latent factors which accounted for all the common variance from the 28 ADI-R algorithm items were retained for both datasets. Based on the common characteristics of items in each factor, the factors represent 1) social interaction and communication; 2) joint attention; 3) non-verbal communication; 4) sensory-motor; 5) peer interaction; and 6) insistence on sameness. The factor loading patterns from the two datasets were in high agreement with each other (coefficients of factor congruence from 0.84 to 0.99). All latent phenotypes showed familial aggregation with heritability estimates ranging from 29 to 67% (before adjustment for ascertainment). A strong linkage signal was obtained for the social interaction and communication factor on chromosome 11q23 (logarithm of odds (LOD) score=4.00) which contains candidate genes involved in neural cell adhesion and synapse function. A strong linkage signal was also obtained for the sensory-motor factor on chromosome 19q13.3 (LOD score=4.92).

Conclusions: The top linkage findings from this study do not overlap with the most significant linkage results from  the AGP linkage study using ASD diagnosis as a primary outcome (AGP 2007) and another AGP linkage study using either subsets of families or ADI-R domain total scores as traits (Liu et al. 2008). This once again reflects the complexity of gene mapping for ASD. However, this study does demonstrate that the latent constructs of ASD is replicable in different datasets and the derived latent phenotypes are suitable and informative for genetic studies.

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