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Genomics data are currently being produced at unprecedented rates, resulting in increased knowledge discovery and submission to public data repositories. Despite these advances, genomic information on African-ancestry populations remains significantly low compared with European- and Asian-ancestry populations. This information is typically segmented across several different biomedical data repositories, which often lack sufficient fine-grained structure and annotation to account for the diversity of African populations, leading to many challenges related to the retrieval, representation and findability of such information. To overcome these challenges, we developed the African Genomic Medicine Portal (AGMP), a database that contains metadata on genomic medicine studies conducted on African-ancestry populations. The metadata is curated from two public databases related to genomic medicine, PharmGKB and DisGeNET. The metadata retrieved from these source databases were limited to genomic variants that were associated with disease aetiology or treatment in the context of African-ancestry populations. Over 2000 variants relevant to populations of African ancestry were retrieved. Subsequently, domain experts curated and annotated additional information associated with the studies that reported the variants, including geographical origin, ethnolinguistic group, level of association significance and other relevant study information, such as study design and sample size, where available. The AGMP functions as a dedicated resource through which to access African-specific information on genomics as applied to health research, through querying variants, genes, diseases and drugs. The portal and its corresponding technical documentation, implementation code and content are publicly available.

Effective interventions and treatments for complex diseases have been implemented globally, however, coverage in Africa has been comparatively lower due to lack of capacity, clinical applicability and knowledge on the genetic contribution to disease and treatment. Currently, there is a scarcity of genetic data on African populations, which have enormous genetic diversity. Pharmacogenomics studies have the potential to revolutionise treatment of diseases, therefore, African populations are likely to benefit from these approaches to identify likely responders, reduce adverse side effects and optimise drug dosing. This review discusses clinical pharmacogenetics studies conducted in African populations, focusing on studies that examined drug response in complex diseases relevant to healthcare. Several pharmacogenetics associations have emerged from African studies, as have gaps in knowledge.

A previously reported microarray data analysis by RISS algorithm on breast cancer showed over-expression of the growth factor receptor (Grb7) and it also highlighted Tweety (TTYH1) gene to be under expressed in breast cancer for the first time. Our aim was to validate the results obtained from the microarray analysis with respect to these genes. Also, the relationship between their expression and the different prognostic indicators was addressed. RNA was extracted from the breast tissue of 30 patients with primary malignant breast cancer. Control samples from the same patients were harvested at a distance of ≥5 cm from the tumour. Semi-quantitative RT-PCR analysis was done on all samples. There was a significant difference between the malignant and control tissues as regards Grb7 expression. It was significantly related to the presence of lymph node metastasis, stage and histological grade of the malignant tumours. There was a significant inverse relation between expression of Grb7 and expression of both oestrogen and progesterone receptors. Grb7 was found to be significantly related to the biological classification of breast cancer. TTYH1 was not expressed in either the malignant or the control samples. The RISS by our group algorithm developed was laboratory validated for Grb7, but not for TTYH1. The newly developed software tool needs to be improved.

The objective of this study was to characterize the factors that influence the outcome of exposure to hepatitis C virus (HCV) genotype 4 (HCV-G4) and the course of recent infection. In this longitudinal study, we prospectively assessed the clinical, genetic, virological, and immunological parameters and retrospectively determined single-nucleotide polymorphisms at interleukin-28B (IL-28B) rs12979860 in a well-characterized large cohort recently exposed to HCV-G4. A total of 136 subjects with acute HCV (new viremia, seroconversion, and HCV-specific T-cell responses) were identified. Forty-eight subjects (35%) had spontaneous viral clearance and 88 subjects developed chronic HCV of which 42 subjects were treated with pegylated interferon monotherapy, with a sustained virologic response (SVR) rate of 88%. Twenty-six subjects developed HCV-specific T-cell immune responses without detectable viremia or seroconversion. IL-28B-CC (odds ratio (OR) 14.22; P<0.0001), multispecific T-cell responses (OR=11.66; P<0.0001), >300 IU/l alanine aminotransferase (ALT) decline within 4 weeks (OR=6.83; P<0.0001), jaundice (OR=3.54; P=0.001), female gender (OR=2.39; P=0.007), and >2.5 log10 HCV-RNA drop within 8 weeks (OR=2.48; P=0.016) were independently associated with spontaneous clearance. ALT normalization and undetectable HCV-RNA predicted SVR. Exposed apparently uninfected participants had a higher frequency of IL-28B-CC than patients with unresolved acute HCV (P<0.001). IL-28B-CC was associated with multispecific T-cell response (r(2)=0.0.835; P<0.001). IL-28B-CC genotype, multispecific HCV T-cell responses, rapid decline in ALT, and viral load predict spontaneous clearance and response to acute HCV-G 4 therapy. IL-28B-CC genotype correlates with developing early multispecific T-cell responses. These findings have important implications for predicting the outcome of HCV exposure and acute infection and identifying patients likely to benefit from therapy.

Hepatitis C virus (HCV) is a major cause of chronic liver disease, end stage liver disease and liver cancer in Egypt. Genotype 4 is the prevalent genotype in Egypt and has recently spread to Southern Europe particularly France, Italy, Greece and Spain. Recently, new direct acting antivirals (DAAs) have caused a revolution in HCV therapy with response rates approaching 100%. Despite the diversity of DAAs, treatment of chronic hepatitis C genotype 4 has not yet been optimized. The aim of this study is to build a framework to predict the response of chronic HCV genotype 4 patients to various DAAs by applying Data Mining Techniques (DMT) on clinical information. The framework consists of three phases which are data preprocessing phase to prepare the data before applying the DMT; DM phase to apply DMT, evaluation phase to evaluate the performance and accuracy of the built prediction model using a data mining evaluation technique. The experimental results showed that the model obtained acceptable results.