IntroductionSchizophrenia and bipolar disorder account for a large proportion of the global burden of disease. Despite their enormous impact, little is known about their pathophysiology. Given the high heritability of schizophrenia and bipolar disorder, unbiased genetic studies offer the opportunity to gain insight into their neurobiology. However, advances in understanding the genetic architecture of schizophrenia and bipolar disorder have been based almost exclusively on subjects of Northern European ancestry. The Neuropsychiatric Genetics of African Populations-Psychosis (NeuroGAP-Psychosis) project aims to expand our understanding of the causes of schizophrenia and bipolar disorder through large-scale sample collection and analyses in understudied African populations.Methods and analysisNeuroGAP-Psychosis is a case-control study of 34 000 participants recruited across multiple sites within Ethiopia, Kenya, South Africa and Uganda. Participants will include individuals who are at least 18 years old with a clinical diagnosis of schizophrenia or bipolar disorder (‘psychosis’) or those with no history of psychosis. Research assistants will collect phenotype data and saliva for DNA extraction. Data on mental disorders, history of physical health problems, substance use and history of past traumatic events will be collected from all participants.DNA extraction will take place in-country, with genotyping performed at the Broad Institute. The primary analyses will include identifying major groups of participants with similar ancestry using the computation-efficient programme single nucleotide polymorphisms (SNP) weights. This will be followed by a GWAS within and across ancestry groups.Ethics and disseminationAll participants will be assessed for capacity to consent using the University of California, San Diego Brief Assessment of Capacity to Consent. Those demonstrating capacity to consent will be required to provide informed consent. Ethical clearances to conduct this study have been obtained from all participating sites. Findings from this study will be disseminated in publications and shared with controlled access public databases, such as the database of Genotypes and Phenotypes, dbGaP.

Background : Genetic studies of biomedical phenotypes in underrepresented populations identify disproportionate numbers of novel associations. However, current genomics infrastructure--including most genotyping arrays and sequenced reference panels--best serves populations of European descent. A critical step for facilitating genetic studies in underrepresented populations is to ensure that genetic technologies accurately capture variation in all populations. Here, we quantify the accuracy of low-coverage sequencing in diverse African populations. Results : We sequenced the whole genomes of 91 individuals to high-coverage ( > 20X) from the Neuropsychiatric Genetics of African Population-Psychosis (NeuroGAP-Psychosis) study, in which participants were recruited from Ethiopia, Kenya, South Africa, and Uganda. We empirically tested two data generation strategies, GWAS arrays versus low-coverage sequencing, by calculating the concordance of imputed variants from these technologies with those from deep whole genome sequencing data. We show that low-coverage sequencing at a depth of ≥4X captures variants of all frequencies more accurately than all commonly used GWAS arrays investigated and at a comparable cost. Lower depths of sequencing (0.5-1X) performed comparable to commonly used low-density GWAS arrays. Low-coverage sequencing is also sensitive to novel variation, with 4X sequencing detecting 45% of singletons and 95% of common variants identified in high-coverage African whole genomes. Conclusion : These results indicate that low-coverage sequencing approaches surmount the problems induced by the ascertainment of common genotyping arrays, including those that capture variation most common in Europeans and Africans. Low-coverage sequencing effectively identifies novel variation (particularly in underrepresented populations), and presents opportunities to enhance variant discovery at a similar cost to traditional approaches.

Background : Genetic studies of biomedical phenotypes in underrepresented populations identify disproportionate numbers of novel associations. However, current genomics infrastructure--including most genotyping arrays and sequenced reference panels--best serves populations of European descent. A critical step for facilitating genetic studies in underrepresented populations is to ensure that genetic technologies accurately capture variation in all populations. Here, we quantify the accuracy of low-coverage sequencing in diverse African populations. Results : We sequenced the whole genomes of 91 individuals to high-coverage ( > 20X) from the Neuropsychiatric Genetics of African Population-Psychosis (NeuroGAP-Psychosis) study, in which participants were recruited from Ethiopia, Kenya, South Africa, and Uganda. We empirically tested two data generation strategies, GWAS arrays versus low-coverage sequencing, by calculating the concordance of imputed variants from these technologies with those from deep whole genome sequencing data. We show that low-coverage sequencing at a depth of ≥4X captures variants of all frequencies more accurately than all commonly used GWAS arrays investigated and at a comparable cost. Lower depths of sequencing (0.5-1X) performed comparable to commonly used low-density GWAS arrays. Low-coverage sequencing is also sensitive to novel variation, with 4X sequencing detecting 45% of singletons and 95% of common variants identified in high-coverage African whole genomes. Conclusion : These results indicate that low-coverage sequencing approaches surmount the problems induced by the ascertainment of common genotyping arrays, including those that capture variation most common in Europeans and Africans. Low-coverage sequencing effectively identifies novel variation (particularly in underrepresented populations), and presents opportunities to enhance variant discovery at a similar cost to traditional approaches.

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African populations are the most diverse in the world yet are sorely underrepresented in medical genetics research. Here, we examine the structure of African populations using genetic and comprehensive multigenerational ethnolinguistic data from the Neuropsychiatric Genetics of African Populations-Psychosis study (NeuroGAP-Psychosis) consisting of 900 individuals from Ethiopia, Kenya, South Africa, and Uganda. We find that self-reported language classifications meaningfully tag underlying genetic variation that would be missed with consideration of geography alone, highlighting the importance of culture in shaping genetic diversity. Leveraging our uniquely rich multi-generational ethnolinguistic metadata, we track language transmission through the pedigree, observing the disappearance of several languages in our cohort as well as notable shifts in frequency over three generations. We further find significantly higher language transmission rates for matrilineal groups as compared to patrilineal. We highlight both the diversity of variation within the African continent, as well as how within-Africa variation can be informative for broader variant interpretation; many variants appearing rare elsewhere are common in parts of Africa. The work presented here improves the understanding of the spectrum of genetic variation in African populations and highlights the enormous and complex genetic and ethnolinguistic diversity within Africa.

Background The Kessler Psychological Distress Scale (K-10) is a short screening tool developed to identify, with good sensitivity, non-specific psychological distress in the general population. Sensitivity and specificity of the K-10 have been examined in various clinical populations in South Africa; however, other psychometric properties, such as construct validity and factor structure, have not been evaluated. We present evidence of the prevalence and severity of psychological distress in an outpatient setting in South Africa and evaluate the internal reliability, construct validity, and factor structure of the K-10 in this population. Methods We explored prevalence estimates of psychological distress using previously established cutoffs and assessed the reliability (consistency) of the K-10 by calculating Cronbach’s alpha, item-total correlations and omega total and hierarchical coefficients. Construct validity and factor structure of the K-10 were examined through split-sample exploratory factor analysis (EFA) followed by confirmatory factor analysis (CFA), comparing several theoretical models and the EFA. Results Overall, there was low prevalence of psychological distress in our sample of 2591 adults, the majority of whom were between the ages of 18–44 (77.7%). The K-10 showed good construct validity and reliability, with a Cronbach’s alpha of 0.84 and omega total of 0.88. EFA yielded a four-factor solution with likely measurement artifacts. CFA showed that the four-factor model from EFA displayed the best comparative fit indices, but was likely overfitted. The unidimensional model with correlated errors was deemed the best fitting model based on fit indices, prior theory, and previous studies. Conclusion The K-10 displays adequate psychometric properties, good internal reliability, and good fit with a unidimensional-factor structure with correlated errors. Further work is required to determine appropriate cutoff values in different populations and clinical subgroups within South Africa to aid in determining the K-10’s clinical utility.

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