Publications

Background: Substance use is prevalent among people with mental health issues, and patients with psychosis are more likely to use and misuse substances than the general population. Despite extensive research on substance abuse among the general public in Kenya, there is a scarcity of data comparing substance use among people with and without psychosis. This study investigates the association between psychosis and various substances in Kenya.Methods: This study utilized data from the Neuro-GAP Psychosis Case-Control Study between April 2018 and December 2022. The KEMRI-Wellcome Trust Research Programme recruited participants from various sites in Kenya, including Kilifi County, Malindi Sub-County, Port Reitz and Coast General Provincial Hospitals, and Moi Teaching and Referral Hospital, as well as affiliated sites in Webuye, Kapenguria, Kitale, Kapsabet, and Iten Kakamega. The collected data included sociodemographic information, substance use, and clinical diagnosis. We used the summary measures of frequency (percentages) and median (interquartile range) to describe the categorical and continuous data, respectively. We examined the association between categorical variables related to psychosis using the chi-square test. Logistic regression models were used to assess the factors associated with the odds of substance use, considering all relevant sociodemographic variables.Results: We assessed a total of 4,415 cases and 3,940 controls. Except for alcohol consumption (p-value=0.41), all forms of substance use showed statistically significant differences between the case and control groups. Cases had 16% higher odds of using any substance than controls (aOR: 1.16, 95%CI: 1.05-1.28, p=0.005). Moreover, males were 3.95 times more likely to use any substance than females (aOR:3.95; 95%CI: 3.43-4.56). All the categories of living arrangements were protective against substance use.Conclusion: The findings of this study suggest that psychotic illnesses are associated with an increased likelihood of using various substances. These findings are consistent with those of previous studies; however, it is crucial to investigate further the potential for reverse causality between psychosis and substance abuse using genetically informed methods.

We deployed the Blended Genome Exome (BGE), a DNA library blending approach that generates low pass whole genome (1-4x mean depth) and deep whole exome (30-40x mean depth) data in a single sequencing run. This technology is cost-effective, empowers most genomic discoveries possible with deep whole genome sequencing, and provides an unbiased method to capture the diversity of common SNP variation across the globe. To evaluate this new technology at scale, we applied BGE to sequence >53,000 samples from the Populations Underrepresented in Mental Illness Associations Studies (PUMAS) Project, which included participants across African, African American, and Latin American populations. We evaluated the accuracy of BGE imputed genotypes against raw genotype calls from the Illumina Global Screening Array. All PUMAS cohorts had R2 concordance >=95% among SNPs with MAF>=1%, and never fell below >=90% R2 for SNPs with MAF<1%. Furthermore, concordance rates among local ancestries within two recently admixed cohorts were consistent among SNPs with MAF>=1%, with only minor deviations in SNPs with MAF<1%. We also benchmarked the discovery capacity of BGE to access protein-coding copy number variants (CNVs) against deep whole genome data, finding that deletions and duplications spanning at least 3 exons had a positive predicted value of \~{}90%. Our results demonstrate BGE scalability and efficacy in capturing SNPs, indels, and CNVs in the human genome at 28% of the cost of deep whole-genome sequencing. BGE is poised to enhance access to genomic testing and empower genomic discoveries, particularly in underrepresented populations.Competing Interest StatementThe authors have declared no competing interest.