Dissecting in silico Mutation Prediction of Variants in African Genomes: Challenges and Perspectives - Bope, Christian Domilongo; Chimusa, Emile R.; Nembaware, Victoria; Mazandu, Gaston K.; de Vries, Jantina; Wonkam, Ambroise (Frontiers in Genetics, 2019)
Genomic medicine is set to drastically improve clinical care, globally, due to high throughput technologies, which enable speedy in silico detection and analysis of clinically relevant mutations. However, the variability in the in silico prediction methods and categorisation of functionally relevant genetic variants’ can pose specific challenges in some populations. In silico mutation, prediction tools could lead to high rates of false positive/negative results, particularly in African genomes that harbour the highest genetic diversity, and that are disproportionately underrepresented in public databases and reference panels. These issues are particularly relevant with the recent increase in initiatives such as the Human Heredity and Health (H3Africa) that are generating huge amounts of genomic sequence data in the absence of policies to guide genomic researchers to return results of variants in so-called actionable genes to research participants. This report aims to: (i) provide an inventory of publicly available Whole Exome/ Genome data from Africa which could help improve reference panels, develop an African reference genome build, and explore the frequency of pathogenic variants in actionable genes and related challenges, (ii) review available in silico prediction mutation tools, and the criteria for categorisation of pathogenicity of novel variants, (iii) propose recommendations for analysing pathogenic variants in African genomes, for their use in research and clinical practice.