Polygenic risk scores have shown great promise in predicting complex disease risk and will become more accurate as training sample sizes increase. The standard approach for calculating risk scores involves linkage disequilibrium (LD)-based marker pruning and applying a p value threshold to association statistics, but this discards information and can reduce predictive accuracy. We introduce LDpred, a method that infers the posterior mean effect size of each marker by using a prior on effect sizes and LD information from an external reference panel. Theory and simulations show that LDpred outperforms the approach of pruning followed by thresholding, particularly at large sample sizes. Accordingly, predicted R(2) increased from 20.1% to 25.3% in a large schizophrenia dataset and from 9.8% to 12.0% in a large multiple sclerosis dataset. A similar relative improvement in accuracy was observed for three additional large disease datasets and for non-European schizophrenia samples. The advantage of LDpred over existing methods will grow as sample sizes increase.

Schizophrenia and bipolar disorder are two distinct diagnoses that share symptomology. Understanding the genetic factors contributing to the shared and disorder-specific symptoms will be crucial for improving diagnosis and treatment. In genetic data consisting of 53,555 cases (20,129 bipolar disorder [BD], 33,426 schizophrenia [SCZ]) and 54,065 controls, we identified 114 genome-wide significant loci implicating synaptic and neuronal pathways shared between disorders. Comparing SCZ to BD (23,585 SCZ, 15,270 BD) identified four genomic regions including one with disorder-independent causal variants and potassium ion response genes as contributing to differences in biology between the disorders. Polygenic risk score (PRS) analyses identified several significant correlations within case-only phenotypes including SCZ PRS with psychotic features and age of onset in BD. For the first time, we discover specific loci that distinguish between BD and SCZ and identify polygenic components underlying multiple symptom dimensions. These results point to the utility of genetics to inform symptomology and potential treatment.

The association between the 10-repeat allele of the dopamine transporter gene (DAT) and attention deficit hyperactivity disorder (ADHD) is uncertain. This study aimed to conduct a meta-analysis of the association between the 10-repeat allele of a variable number tandem repeat (VNTR) polymorphism in the 3'-untranslated region (UTR) of the DAT1 gene and ADHD. We pooled up 18 published transmission disequilibrium test (TDT) studies between the 40-base pair VNTR polymorphism in the3'-UTR of the DAT1 gene and ADHD. It included a total of 1,373 informative meioses, 7 haplotype-based haplotype relative risk (HHRR) studies, and 6 case-control-based association studies. There were statistically significant evidences for heterogeneity of the odds ratio in TDT and HHRR studies (P < 0.10), but not in case-control studies. The results of random effects model showed small but significant association between ADHD and the DAT1 gene in TDT studies (OR = 1.17, 95% CI = 1.05-1.30, chi-square = 8.11, df = 1, P = 0.004), but not in HHRR and case-control studies. The 10-repeat allele of a VNTR polymorphism in the 3'-UTR the DAT1 gene has a small but significant role in the genetic susceptibility of ADHD. These meta-analysis findings support the involvement of the dopamine system genes in ADHD liability variation. However, more work is required to further identify the functional allelic variants/mutations that are responsible for this association.

Atomoxetine is efficacious in reducing ADHD symptoms. It may have a role in treating comorbid ODD or depression, and probably in comorbid anxiety.