Attention deficit hyperactivity disorder (ADHD) is a common neurodevelopmental disorder, starting in early childhood and persisting into adulthood in the majority of cases. Family and twin studies have demonstrated the importance of genetic factors and candidate gene association studies have identified several loci that exert small but significant effects on ADHD. To provide further clarification of reported associations and identify novel associated genes, we examined 1038 single-nucleotide polymorphisms (SNPs) spanning 51 candidate genes involved in the regulation of neurotransmitter pathways, particularly dopamine, norepinephrine and serotonin pathways, in addition to circadian rhythm genes. Analysis used within family tests of association in a sample of 776 DSM-IV ADHD combined type cases ascertained for the International Multi-centre ADHD Gene project. We found nominal significance with one or more SNPs in 18 genes, including the two most replicated findings in the literature: DRD4 and DAT1. Gene-wide tests, adjusted for the number of SNPs analysed in each gene, identified associations with TPH2, ARRB2, SYP, DAT1, ADRB2, HES1, MAOA and PNMT. Further studies will be needed to confirm or refute the observed associations and their generalisability to other samples.

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.

Results of behavioral genetic and molecular genetic studies have converged to suggest that genes substantially contribute to the development of attention deficit/hyperactivity disorder (ADHD), a common disorder with an onset in childhood. Yet, despite numerous linkage and candidate gene studies, strongly consistent and replicable association has eluded detection. To search for ADHD susceptibility genes, we genotyped approximately 600,000 SNPs in 958 ADHD affected family trios. After cleaning the data, we analyzed 438,784 SNPs in 2,803 individuals comprising 909 complete trios using ADHD diagnosis as phenotype. We present the initial TDT findings as well as considerations for cleaning family‐based TDT data. None of the SNP association tests achieved genome‐wide significance, indicating that larger samples may be required to identify risk loci for ADHD. We additionally identify a systemic bias in family‐based association, and suggest that variable missing genotype rates may be the source of this bias.