Quantitative Trait Loci mapping
The genetic heterogeneity among the ASD population is reflected in the variation in clinical expression from individual to individual.
QTL is used to increase phenotypic and genetic homogeneity of study samples. Impairments in the main deficit areas of social behevior, language, and repetitive behaviors have been observed in first- and second- degree non-affected relatives of people with Autism Spectrum Disorders, which suggests familial genetic transmission. These autism-related traits can be used to identify genes which contribute to the behavioral deficits related to autism. Using these traits to stratify affected families into groups with common characteristics for quantitative linkage analysis can be used to identify quantitative trait loci (QTL). QTL are genes of small to moderate effect that contribute to continuous variation in a phenotype and allow researchers to use behavioral traits to detect genes with small effects related to autism. In fact, the Psychiatric Genomewide Association STudies Consortium Steering Committee uses phenotypes rather than diagnoses for finding genes that present risk for complex behavioral traits. Traits that have been used to identify QTL include language related traits such as non-verbal communication or age at first word, social communication scores on the Social Responsiveness Scale (SRS). 1
There are some concerns with using fMRI data to directly inform genetic studies. If the rare genetic variants hypothesis is correct, the neural heterogeneity to inform genetic studies by identifying subgroups that map onto these systems may be problematic, resulting in similar neural phenotypes. However, from current studies, it seems unlikely that different phenotypic characteristics with differing neural etiologies are indistinguishable.
It would also be problematic if there were considerable phenotypic heterogeneity in relation to a certain genetic variant, or a genetic variant affected a part of neurodevelopment common to several neural systems that could account for impairments in social communication seen in ASD. Furthermore, there are a few issues that pertain to the imaging genetic approaches on neural systems activation patterns. The effects measured are relatively small, such as identifying significant gene-related inter-individual variability in neural functions in a given neural system, compared to larger effects found in variables such as age, IQ, gender, and environmental factors. If the rare variant hypothesis of autism is true, focusing on one polymorphism set, compared to millions of polymorphisms, requires the stringent control of confounding genetic variability, such as the variability obtained through population stratification.
Another concern is that imaging genetic studies rely on imaging techniques that are sensitive to the neural system under study; therefore, these tasks need to vigorously engage only the neural networks of interest, as well as capture the variability across subjects and controls. A final concern with imaging genetic studies, is the need for homogeneous groups, which can possibly be achieved through the use of behavioral and physiological measures that capture the biologically relevant phenotypic characteristics of the neural system under study.
Other concerns include the inclusion of numerous types of measurement errors associated with the fMRI techniques themselves, such as cognitive confounds like attention to the stimuli influences variability of results between subjects and within subjects across several trials. Problems may arise from identifying the neural systems phenotype themselves, as IQ confound can occur when comparing ASD to age-matched controls and when individuals with ASD are compared to developmental controls. The only way these problems may be resolved is improved understanding of the developmental trajectories of the construct under study.1
1. Piggot, J. Neural systems approaches to the neurogenetics of autism spectrum disorders.Neuroscience. 2009 May 29 PMID 19482063