The prevalence and patterns of autism spectrum disorder (ASD) symptoms/traits and the associations of ASD with psychiatric and substance use disorders has not been documented in non-clinical students in Sub-Saharan Africa, and Kenya in particular.

To document the risk level of ASD and its traits in a Kenyan student population (high school, college and university) using the Autism-Spectrum Quotient (AQ); and to determine the associations between ASD and other psychiatric and substance use disorders.

This was a cross-sectional study among students (n = 9626). We used instruments with sufficient psychometric properties and good discriminative validity to collect data. A cut-off score of ≥32 on the AQ was used to identify those at high risk of ASD. We conducted the following statistical tests: (a) basic descriptive statistics; (b) chi-squared tests and Fisher's exact tests to analyse associations between categorical variables and ASD; (c) independent t-tests to examine two-group comparisons with ASD; (d) one-way analysis of variance to make comparisons between categorical variables with three or more groups and ASD; (e) statistically significant (P < 0.05) variables fitted into an ordinal logistic regression model to identify determinants of ASD; (f) Pearson's correlation and reliability analysis.

Of the total sample, 54 (0.56%) were at high risk of ASD. Sociodemographic differences were found in the mean scores for the various traits, and statistically significant (P < 0.05) associations we found between ASD and various psychiatric and substance use disorders.

Risk of ASD, gender characteristics and associations with psychiatric and substance use disorders are similar in this Kenyan sample to those found in Western settings in non-clinical populations.

– Comorbidity between psychiatric disorders is extensive but, from a genetic perspective, still poorly understood. Modern molecular genetic approaches to this problem are limited by a reliance on case–control designs.

– In 5 828 760 individuals born in Sweden from 1932–1995 with a mean (s.d.) age at follow-up of 54.4 (18.1), we examined family genetic risk score (FGRS) profiles including internalizing, psychotic, substance use and developmental disorders in 10 pairs of psychiatric and substance use disorders diagnosed from population registries. We examined these profiles in three groups of patients: disorder A only, disorder B only and comorbid cases with both disorders.

– The most common pattern of findings, seen in five pairings, was simple and quantitative. Comorbid cases had higher FGRS than both non-comorbid cases for all (or nearly all) disorders. However, the pattern was more complex in the remaining five pairings and included qualitative changes where the comorbid cases showed no increases in FGRS for certain disorders and in a few cases significant decreases. Several comparisons showed an asymmetric pattern of findings with increases, in comorbidity compared to single disorder cases, of the FGRS for only one of the two disorders.

– The examination of FGRS profiles in general population samples where all disorders are assessed in all subjects provides a fruitful line of inquiry to understand the origins of psychiatric comorbidity. Further work will be needed, with an expansion of analytic approaches, to gain deeper insights into the complex mechanisms likely involved.

How does genetic liability to suicide attempt (SA), suicide death (SD), major depression (MD), bipolar disorder (BD), schizophrenia (SZ), alcohol use disorder (AUD), and drug use disorder (DUD) impact on risk for SA and SD?

In the Swedish general population born 1932–1995 and followed through 2017 (n = 7 661 519), we calculate family genetic risk scores (FGRS) for SA, SD, MD, BD, SZ, AUD, and DUD. Registration for SA and SD was assessed from Swedish national registers.

In univariate and multivariate models predicting SA, FGRS were highest for SA, AUD, DUD, and MD. In univariate models predicting SD, the strongest FGRS were AUD, DUD, SA, and SD. In multivariate models, the FGRS for SA and AUD were higher in predicting SA while the FGRS for SD, BD, and SZ were higher in predicting SD. Higher FGRS for all disorders significantly predicted both younger age at first SA and frequency of attempts. For SD, higher FGRS for MD, AUD, and SD predicted later age at SD. Mediation of FGRS effects on SA and SD was more pronounced for SD than SA, strongest for AUD, DUD, and SZ FGRS and weakest for MD.

FGRS for both SA and SD and for our five psychiatric disorders impact on risk for SA and SD in a complex manner. While some of the impact of genetic risk factors for psychiatric disorders on risk for SA and SD is mediated through developing the disorders, these risks also predispose directly to suicidal behaviors.