Personalizing medicine in Africa: current state, progress and challenges.

Personalized medicine has been identified as a powerful tool for addressing the myriad of health issues facing different health systems globally. Although recent studies have expanded our understanding of how different factors such as genetics and the environment play significant roles in affecting the health of individuals, there are still several other issues affecting their translation into personalizing health interventions globally. Since African populations have demonstrated huge genetic diversity, there is a significant need to apply the concepts of personalized medicine to overcome various African-specific health challenges. Thus, we review the current state, progress, and challenges facing the adoption of personalized medicine in Africa with a view to providing insights to critical stakeholders on the right approach to deploy.

A deep population reference panel of tandem repeat variation.

Tandem repeats (TRs) represent one of the largest sources of genetic variation in humans and are implicated in a range of phenotypes. Here we present a deep characterization of TR variation based on high coverage whole genome sequencing from 3550 diverse individuals from the 1000 Genomes Project and H3Africa cohorts. We develop a method, EnsembleTR, to integrate genotypes from four separate methods resulting in high-quality genotypes at more than 1.7 million TR loci. Our catalog reveals novel sequence features influencing TR heterozygosity, identifies population-specific trinucleotide expansions, and finds hundreds of novel eQTL signals. Finally, we generate a phased haplotype panel which can be used to impute most TRs from nearby single nucleotide polymorphisms (SNPs) with high accuracy. Overall, the TR genotypes and reference haplotype panel generated here will serve as valuable resources for future genome-wide and population-wide studies of TRs and their role in human phenotypes.

A deep population reference panel of tandem repeat variation.

Tandem repeats (TRs) represent one of the largest sources of genetic variation in humans and are implicated in a range of phenotypes. Here we present a deep characterization of TR variation based on high coverage whole genome sequencing from 3,550 diverse individuals from the 1000 Genomes Project and H3Africa cohorts. We develop a method, EnsembleTR, to integrate genotypes from four separate methods resulting in high-quality genotypes at more than 1.7 million TR loci. Our catalog reveals novel sequence features influencing TR heterozygosity, identifies population-specific trinucleotide expansions, and finds hundreds of novel eQTL signals. Finally, we generate a phased haplotype panel which can be used to impute most TRs from nearby single nucleotide polymorphisms (SNPs) with high accuracy. Overall, the TR genotypes and reference haplotype panel generated here will serve as valuable resources for future genome-wide and population-wide studies of TRs and their role in human phenotypes.

SysBiolPGWAS: simplifying post-GWAS analysis through the use of computational technologies and integration of diverse omics datasets.

MOTIVATION: Post-genome-wide association studies (pGWAS) analysis is designed to decipher the functional consequences of significant single-nucleotide polymorphisms (SNPs) in the era of GWAS. This can be translated into research insights and clinical benefits such as the effectiveness of strategies for disease screening, treatment and prevention. However, the setup of pGWAS (pGWAS) tools can be quite complicated, and it mostly requires big data. The challenge however is, scientists are required to have sufficient experience with several of these technically complex and complicated tools in order to complete the pGWAS analysis. RESULTS: We present SysBiolPGWAS, a pGWAS web application that provides a comprehensive functionality for biologists and non-bioinformaticians to conduct several pGWAS analyses to overcome the above challenges. It provides unique functionalities for analysis involving multi-omics datasets and visualization using various bioinformatics tools. SysBiolPGWAS provides access to individual pGWAS tools and a novel custom pGWAS pipeline that integrates several individual pGWAS tools and data. The SysBiolPGWAS app was developed to be a one-stop shop for pGWAS analysis. It targets researchers in the area of the human genome and performs its analysis mainly in the autosomal chromosomes. AVAILABILITY AND IMPLEMENTATION: SysBiolPGWAS web app was developed using JavaScript/TypeScript web frameworks and is available at: https://spgwas.waslitbre.org/. All codes are available in this GitHub repository https://github.com/covenant-university-bioinformatics.

Computational identification of Plasmodium falciparum RNA pseudouridylate synthase as a viable drug target, its physicochemical properties, 3D structure prediction and prediction of potential inhibitors.

The increased resistance to the currently effective antimalarial drugs against Plasmodium falciparum has necessitated the development of new drugs for malaria treatment. Many proteins have been predicted using various means as potential drug targets for the treatment of the P. falciparum malaria infection. Meanwhile, only a few studies went on to predict the 3-dimensional (3D) structure of potential target. Therefore, this study aimed to predict potential antimalarial drug targets against the deadliest malaria parasite P. falciparum as well as to determine the 3D structure and possible inhibitors of one of the targets. We employed machine learning approach to predict suitable drug targets in P. falciparum. Five of the predicted protein targets were considered as potential drug targets as they were non-homologous to their human counterparts. Out of these, we determined the physicochemical properties, predicted the 3D structure and carried out docking-based virtual screening of P. falciparum RNA pseudouridylate synthase, putative (PfRPuSP). The PfRPuSP was one of the potential five target proteins. Homology modelling and the ab initio methods were used to predict the 3D structure of PfRPuSP. Then, a compound library of 5621 molecules was constructed from PubChem and ChEMBL databases using 5-fluorouridine as the control inhibitor. Docking-based virtual screening was performed using Autodock 4.2 and Autodock Vina to select compounds with high binding affinity. A total of 11 compounds were selected based on their binding energies from 881 compounds which were manually examined after docking. Seven of the 11 compounds that exhibited remarkable interactions with the residues in the active sites of PfRPuSP were analysed. These compounds performed favourably when compared to the control inhibitor and predicted to bind better than 5-fluorouridine. These seven compounds are suggested as new potential lead structures for antimalarial treatment.

Polygenic Risk Score in African populations: progress and challenges.

Polygenic Risk Score (PRS) analysis is a method that predicts the genetic risk of an individual towards targeted traits. Even when there are no significant markers, it gives evidence of a genetic effect beyond the results of Genome-Wide Association Studies (GWAS). Moreover, it selects  single nucleotide polymorphisms (SNPs) that  contribute to the disease with low effect size  making it more precise at individual level risk prediction. PRS  analysis addresses the shortfall of GWAS by taking into account the SNPs/alleles with  low effect size but play an indispensable role to the observed phenotypic/trait variance.  PRS analysis has  applications that investigate the genetic basis of several traits, which includes rare diseases. However, the accuracy of PRS analysis depends on the genomic data of the underlying population. For instance, several studies  show   that obtaining higher prediction power of PRS analysis is challenging for non-Europeans. In this manuscript, we review the conventional PRS methods and their application to sub-Saharan African communities. We conclude that  lack of sufficient GWAS data and tools is  the limiting factor of applying PRS analysis to sub-Saharan populations.   We recommend developing Africa-specific PRS methods and tools for estimating and analyzing  African population data   for clinical  evaluation of PRSs of interest and predicting  rare diseases.

Plant genomics in Africa: present and prospects.

Plants are the world's most consumed goods. They are of high economic value and bring many health benefits. In most countries in Africa, the supply and quality of food will rise to meet the growing population's increasing demand. Genomics and other biotechnology tools offer the opportunity to improve subsistence crops and medicinal herbs in the continent. Significant advances have been made in plant genomics, which have enhanced our knowledge of the molecular processes underlying both plant quality and yield. The sequencing of complex genomes of African plant species, facilitated by the continuously evolving next-generation sequencing technologies and advanced bioinformatics approaches, has provided new opportunities for crop improvement. This review summarizes the achievements of genome sequencing projects of endemic African plants in the last two decades. We also present perspectives and challenges for future plant genomic studies that will accelerate important plant breeding programs for African communities. These challenges include a lack of basic facilities, a lack of sequencing and bioinformatics facilities, and a lack of skills to design genomics studies. However, it is imperative to state that African countries have become key players in the plant genome revolution and genome derived-biotechnology. Therefore, African governments should invest in public plant genomics research and applications, establish bioinformatics platforms and training programs, and stimulate university and industry partnerships to fully deploy plant genomics, particularly in the fields of agriculture and medicine.

Performing post-genome-wide association study analysis: overview, challenges and recommendations.

Genome-wide association studies (GWAS) provide  huge information on statistically significant single-nucleotide polymorphisms (SNPs) associated with various human complex traits and diseases. By performing GWAS studies, scientists have successfully identified the association of hundreds of thousands to  millions of SNPs to a single phenotype. Moreover, the association of some SNPs with rare diseases has been intensively tested. However, classic GWAS studies have not yet provided solid, knowledgeable insight into functional and biological mechanisms underlying phenotypes or mechanisms of diseases. Therefore, several post-GWAS (pGWAS) methods have been recommended. Currently, there is no simple scientific document to provide a quick guide for performing pGWAS analysis. pGWAS is a crucial step for a better understanding of the biological machinery beyond the SNPs. Here, we provide an overview to performing pGWAS analysis and demonstrate the challenges behind each method. Furthermore, we direct readers to key articles for each pGWAS method and present the overall issues in pGWAS analysis.  Finally, we include a custom pGWAS pipeline to guide new users when performing their research.