Using genomic prediction to study the relationship between autism liability and phenotype
- Awarded: 2018
- Award Type: Explorer
- Award #: 558819
The etiology of autism spectrum disorders (ASDs) is complex, involving both genetic and environmental contributions to individual and population-level liability. Accumulating genetic and phenotypic data are congruent with a polygenic inheritance model, with results notably showing high heritability estimates for ASD and familial aggregation of subclinical autistic traits in ASD families. There are, however, some results that seem at odds with our understanding of ASD genetic risk. For example, there is a low correlation between parents and probands with respect to autistic traits. This deviation could be due to ascertainment. However, it might instead reflect a lack of fit in the current model. Thus, an important part of the ASD-risk puzzle may be missing.
To form a complete picture of the architecture of genetic risk for ASD, there is a need for a better understanding of the relationship between genetic risk for ASD and autistic traits in the general population. Pauline Chaste proposes to explore this issue using novel analyses, both in ASD families (e.g., the Simons Simplex Collection) and in the general population (Avon Longitudinal Study of Parents and Children cohort), by capitalizing on a method underutilized in human genetics. This approach, called ‘genomic prediction,’1 estimates individual genetic risk for ASD from common variants and is a more promising approach to identify true risk than traditional human genetics methods, like the polygenic risk score.
The main aims of the study will be i) to estimate the relationship between ASD liability and a quantitative measure of autistic traits in ASD families and ii) to estimate the relationship between ASD genetic risk and social communication skills in the general population, accounting for IQ, sex and age. The insights gained from this study should provide a new understanding of the continuous risk model for ASD.