Complex traits are generally considered to be caused by many (often non-coding) variants of small effect across multiple loci, with genome-wide association studies (GWAS) typically used to identify the trait-associated variants. However, in 2020, Gulsuner et al. departed from this tradition by using whole-exome sequencing (WES) to search for rare pathogenic coding variants of large effect in individuals with schizophrenia. Moreover, by focussing on a population of nearly 1,000 individuals of Xhosa ancestry from South Africa, their choice of cohort also deviated from the norm of studying populations largely of white European descent. This unconventional approach paid dividends, supporting its utility in the study of complex disease.
Notably, the study uncovered very rare de novo, deleterious mutations in the coding regions of a few genes encoding synaptic proteins, the distributions of which differed in cases versus controls. Specifically, synaptic genes encoding glutamate (GRIA2), γ-aminobutyric acid (GABRB1, GABRB2, GABRA5 and GABRD) and dopamine (DRD2) were found to have damaging, often private, mutations. Furthermore, in multiple patients, disruptive mutations were found in CACNA1C and DLGAP1. These genes are generally well conserved, intolerant to mutations and associated with neurodevelopmental disorders. The findings support an oligogenic model of disease, whereby the clinical complications of schizophrenia could be associated with a few severely damaging variants in genes that are related to its pathophysiology, such as genes expressed in the brain. The results were replicated in a Swedish cohort of 5,000 individuals with schizophrenia — but the African cohort, despite being around five times smaller, yielded larger effect sizes. This finding emphasizes the importance of the greater genetic variation present in African populations, which in this study provided more power to detect genotype–phenotype relationships in schizophrenia. Indeed, genetic variation in exomes of individuals with Xhosa ancestry was far greater than among non-Africans. This observation is also consistent with GWAS data showing that linkage disequilibrium is lower in people with African ancestry, which improves fine mapping and identification of causative variants for complex traits.
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