H3Africa is developing capacity for health-related genomics research in Africa

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Introduction The Western Cape Provincial Health Data Centre (PHDC) consolidates person-level clinical data across government services, leveraging sustained investments in patient registration systems, a unique identifier, and maturation of administrative and clinical digital health systems. Objectives The PHDC supports clinical care directly through tools for clinicians which integrate patient data or identify patients in need of interventions, and indirectly through supporting operational and epidemiological analyses.  Methods The PHDC is housed entirely within government. Data are processed from a range of source systems, usually daily, through distinct harmonisation and curation, beneficiation, and reporting processes. Linkage is predominantly through the unique identifier which doubles as a pervasive folder number, augmented by other identifiers. Further data processing includes triangulation of multiple data sources for enumerating health conditions, with assignment of certainty levels for each enumeration. Outputs include patient-specific email alerts, a web-based consolidated patient clinical viewing platform, filterable line-listings of patients with specific conditions and associated characteristics and outcomes, management reports and dashboards, and data releases in response to operational and research data requests.  Strict architectural, administrative and governance processes ensure privacy-protection.  Results In the past decade 8 million unique people are recorded as having sought healthcare in the provincial public sector health services, with current utilisation at 15 million attendances or admissions a year.  Cross-sectional enumeration of health conditions includes over 430 000 people with HIV, 500 000 with hypertension, 235 000 with diabetes. 110 000 pregnancies and 54 000 patients with tuberculosis are enumerated annually. Each year over 50 data requests are processed for internal and external requesters in accordance with data request and release governance processes.  Conclusions The single consolidated environment for person-level health data in the Western Cape has created new opportunities for supporting patient care, while improving the governance around access to and release of sensitive patient data.

Genome-wide techniques such as microarray analysis, Serial Analysis of Gene Expression (SAGE), Massively Parallel Signature Sequencing (MPSS), linkage analysis and association studies are used extensively in the search for genes that cause diseases, and often identify many hundreds of candidate disease genes. Selection of the most probable of these candidate disease genes for further empirical analysis is a significant challenge. Additionally, identifying the genes that cause complex diseases is problematic due to low penetrance of multiple contributing genes. Here, we describe a novel bioinformatic approach that selects candidate disease genes according to their expression profiles. We use the eVOC anatomical ontology to integrate text-mining of biomedical literature and data-mining of available human gene expression data. To demonstrate that our method is successful and widely applicable, we apply it to a database of 417 candidate genes containing 17 known disease genes. We successfully select the known disease gene for 15 out of 17 diseases and reduce the candidate gene set to 63.3% (±18.8%) of its original size. This approach facilitates direct association between genomic data describing gene expression and information from biomedical texts describing disease phenotype, and successfully prioritizes candidate genes according to their expression in disease-affected tissues.

Genome-wide experimental methods to identify disease genes, such as linkage analysis and association studies, generate increasingly large candidate gene sets for which comprehensive empirical analysis is impractical. Computational methods employ data from a variety of sources to identify the most likely candidate disease genes from these gene sets. Here, we review seven independent computational disease gene prioritization methods, and then apply them in concert to the analysis of 9556 positional candidate genes for type 2 diabetes (T2D) and the related trait obesity. We generate and analyse a list of nine primary candidate genes for T2D genes and five for obesity. Two genes, LPL and BCKDHA, are common to these two sets. We also present a set of secondary candidates for T2D (94 genes) and for obesity (116 genes) with 58 genes in common to both diseases.

International audienceno abstrac

Rhabdomyosarcoma (RMS) is a common paediatric soft tissue sarcoma that resembles developing foetal skeletal muscle. Tumours of the alveolar subtype frequently harbour one of two characteristic translocations that juxtapose PAX3 or PAX7, and the forkheadrelated gene FKHR (FOXO1A). The embryonal subtype of RMS is not generally associated with these fusion genes. Here, we have quantified the relative levels of chimaeric and wild-type PAX transcripts in various subtypes of RMS (n ¼ 34) in order to assess the relevance of wild-type PAX3 and PAX7 gene expression in these tumours. We found that upregulation of wild-type PAX3 is independent of the presence of either fusion gene and is unlikely to contribute to tumorigenesis. Most strikingly, upregulated PAX7 expression is almost entirely restricted to cases without PAX3-FKHR or PAX7-FKHR fusion genes and may contribute to tumorigenesis in the absence of chimaeric PAX transcription factors. Furthermore, as myogenic satellite cells are known to express PAX7, this pattern of PAX7 expression suggests this cell type as the origin of these tumours. This is corroborated by the detection of MET (c-met) expression, a marker for the myogenic satellite cell lineage, in all RMS samples expressing wild-type PAX7.

BackgroundDeveloping countries of sub-Saharan Africa (SSA) face a double burden of non-communicable diseases (NCDs) and communicable diseases. As high blood pressure (BP) is a common global cardiovascular (CV) disorder associated with high morbidity and mortality, the relationship between gradients of BP and other CV risk factors was assessed in Abia State, Nigeria.MethodsUsing the WHO STEPwise approach to surveillance of chronic disease risk factors, we conducted a population-based cross-sectional survey in Abia state, Nigeria from August 2011 to March 2012. Data collected at various steps included: demographic and behavioral risk factors (Step 1); BP and anthropometric measurements (Step 2), and fasting blood cholesterol and glucose (Step 3).ResultsOf the 2983 subjects with complete data for analysis, 52.1% were females and 53.2% were rural dwellers. Overall, the distribution of selected CV disease risk factors was diabetes (3.6%), hypertension (31.4%), cigarette smoking (13.3%), use of smokeless tobacco (4.8%), physical inactivity (64.2%) and being overweight or obese (33.7%). Presence of hypertension, excessive intake of alcohol, smoking (cigarette and smokeless tobacco) and physical inactivity occurred more frequently in males than in females (p<0.05); while low income, lack of any formal education and use of smokeless tobacco were seen more frequently in rural dwellers than in those living in urban areas (p<0.05). The frequency of selected CV risk factors increased as BP was graded from optimal, normal to hypertension; and high BP correlated with age, gender, smokeless tobacco, overweight or obesity, annual income and level of education.ConclusionGiven the high prevalence of hypertension in this part of Nigeria, there is an urgent need to focus on the reduction of preventable CV risk factors we have observed to be associated with hypertension, in order to effectively reduce the burden of NCDs in Africa.