Background: Autism spectrum disorders (ASD) are early onset neurodevelopmental syndromes typified by impairments in reciprocal social interaction and communication, accompanied by restricted and repetitive behaviors. While rare and especially de novo genetic variation are known to affect liability, whether common genetic polymorphism plays a substantial role is an open question and the relative contribution of genes and environment is contentious. It is probable that the relative contributions of rare and common variation, as well as environment, differs between ASD families having only a single affected individual (simplex) versus multiplex families who have two or more affected individuals.Methods: By using quantitative genetics techniques and the contrast of ASD subjects to controls, we estimate what portion of liability can be explained by additive genetic effects, known as narrow-sense heritability. We evaluate relatives of ASD subjects using the same methods to evaluate the assumptions of the additive model and partition families by simplex/multiplex status to determine how heritability changes with status.Results: By analyzing common variation throughout the genome, we show that common genetic polymorphism exerts substantial additive genetic effects on ASD liability and that simplex/multiplex family status has an impact on the identified composition of that risk. As a fraction of the total variation in liability, the estimated narrow-sense heritability exceeds 60% for ASD individuals from multiplex families and is approximately 40% for simplex families. By analyzing parents, unaffected siblings and alleles not transmitted from parents to their affected children, we conclude that the data for simplex ASD families follow the expectation for additive models closely. The data from multiplex families deviate somewhat from an additive model, possibly due to parental assortative mating.Conclusions: Our results, when viewed in the context of results from genome-wide association studies, demonstrate that a myriad of common variants of very small effect impacts ASD liability.

Genetic risk prediction has several potential applications in medical research and clinical practice and could be used, for example, to stratify a heterogeneous population of patients by their predicted genetic risk. However, for polygenic traits, such as psychiatric disorders, the accuracy of risk prediction is low. Here we use a multivariate linear mixed model and apply multi-trait genomic best linear unbiased prediction for genetic risk prediction. This method exploits correlations between disorders and simultaneously evaluates individual risk for each disorder. We show that the multivariate approach significantly increases the prediction accuracy for schizophrenia, bipolar disorder, and major depressive disorder in the discovery as well as in independent validation datasets. By grouping SNPs based on genome annotation and fitting multiple random effects, we show that the prediction accuracy could be further improved. The gain in prediction accuracy of the multivariate approach is equivalent to an increase in sample size of 34% for schizophrenia, 68% for bipolar disorder, and 76% for major depressive disorders using single trait models. Because our approach can be readily applied to any number of GWAS datasets of correlated traits, it is a flexible and powerful tool to maximize prediction accuracy. With current sample size, risk predictors are not useful in a clinical setting but already are a valuable research tool, for example in experimental designs comparing cases with high and low polygenic risk.