Recent studies point to overlap between neuropsychiatric disorders in symptomatology and genetic aetiology.

To systematically investigate genomics overlap between childhood and adult attention-deficit hyperactivity disorder (ADHD), autism spectrum disorder (ASD) and major depressive disorder (MDD).

Analysis of whole-genome blood gene expression and genetic risk scores of 318 individuals. Participants included individuals affected with adult ADHD (n = 93), childhood ADHD (n = 17), MDD (n = 63), ASD (n = 51), childhood dual diagnosis of ADHD–ASD (n = 16) and healthy controls (n = 78).

Weighted gene co-expression analysis results reveal disorder-specific signatures for childhood ADHD and MDD, and also highlight two immune-related gene co-expression modules correlating inversely with MDD and adult ADHD disease status. We find no significant relationship between polygenic risk scores and gene expression signatures.

Our results reveal disorder overlap and specificity at the genetic and gene expression level. They suggest new pathways contributing to distinct pathophysiology in psychiatric disorders and shed light on potential shared genomic risk factors.

Many adults with autism spectrum disorder (ASD) remain undiagnosed. Specialist assessment clinics enable the detection of these cases, but such services are often overstretched. It has been proposed that unnecessary referrals to these services could be reduced by prioritizing individuals who score highly on the Autism-Spectrum Quotient (AQ), a self-report questionnaire measure of autistic traits. However, the ability of the AQ to predict who will go on to receive a diagnosis of ASD in adults is unclear.

We studied 476 adults, seen consecutively at a national ASD diagnostic referral service for suspected ASD. We tested AQ scores as predictors of ASD diagnosis made by expert clinicians according to International Classification of Diseases (ICD)-10 criteria, informed by the Autism Diagnostic Observation Schedule-Generic (ADOS-G) and Autism Diagnostic Interview-Revised (ADI-R) assessments.

Of the participants, 73% received a clinical diagnosis of ASD. Self-report AQ scores did not significantly predict receipt of a diagnosis. While AQ scores provided high sensitivity of 0.77 [95% confidence interval (CI) 0.72–0.82] and positive predictive value of 0.76 (95% CI 0.70–0.80), the specificity of 0.29 (95% CI 0.20–0.38) and negative predictive value of 0.36 (95% CI 0.22–0.40) were low. Thus, 64% of those who scored below the AQ cut-off were ‘false negatives’ who did in fact have ASD. Co-morbidity data revealed that generalized anxiety disorder may ‘mimic’ ASD and inflate AQ scores, leading to false positives.

The AQ's utility for screening referrals was limited in this sample. Recommendations supporting the AQ's role in the assessment of adult ASD, e.g. UK NICE guidelines, may need to be reconsidered.

Many studies have examined the efficacy of psychotherapy for major depressive disorder (MDD) but publication bias against null results may exist in this literature. However, to date, the presence of an excess of significant findings in this literature has not been explicitly tested.

We used a database of 1344 articles on the psychological treatment of depression, identified through systematic search in PubMed, PsycINFO, EMBASE and the Cochrane database of randomized trials. From these we identified 149 studies eligible for inclusion that provided 212 comparisons. We tested for an excess of significant findings using the method developed by Ioannidis and Trikalinos (2007), and compared the distribution of p values in this literature with the distribution in the antidepressant literature, where publication bias is known to be operating.

The average statistical power to detect the effect size indicated by the meta-analysis was 49%. A total of 123 comparisons (58%) reported a statistically significant difference between treatment and control groups, but on the basis of the average power observed, we would only have expected 104 (i.e. 49%) to do so. There was therefore evidence of an excess of significance in this literature (p = 0.010). Similar results were obtained when these analyses were restricted to studies including a cognitive behavioural therapy (CBT) arm. Finally, the distribution of p values for psychotherapy studies resembled that for published antidepressant studies, where publication bias against null results has already been established.

The small average size of individual psychotherapy studies is only sufficient to detect large effects. Our results indicate an excess of significant findings relative to what would be expected, given the average statistical power of studies of psychotherapy for major depression.