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Trans-ancestral variations exist for various anthropometric traits. These traits, including cardiovascular traits, are likely associated with different loci in individuals of different ancestry groups. We assessed the difference in systolic blood pressure, diastolic blood pressure, pulse rate and maximum heart rate among African and European ancestry individuals. Furthermore, we conducted a genome-wide association study of cardiovascular traits in 383,471 Europeans and 5,978 Africans represented in the UK Biobank. Here, we report 2 and 1,202 variants associated with cardiovascular traits in Africans and Europeans, respectively. We identify 2 novel variants in Africans, including rs9388010 located in the GJA1 gene previously associated with numerous cardiomyopathies. Remarkably, we find the associated variants are primarily unique to each ancestry group and map to largely different genes. Through integrative enrichment analyses we find that gene sets within each ancestral group are significantly enriched for pathways and phenotypes related to cardiovascular physiology and disease. Our discoveries provide a better understanding of variations in cardiovascular traits and the different associated variants in Africans and Europeans.

We previously proposed a structure for recording consent-based data use 'categories' and 'requirements' - Consent Codes - with a view to supporting maximum use and integration of genomic research datasets, and reducing uncertainty about permissible re-use of shared data. Here we discuss clarifications and subsequent updates to the Consent Codes (v4) based on new areas of application (e.g., the neurosciences, biobanking, H3Africa), policy developments (e.g., return of research results), and further practical considerations, including developments in automated approaches to consent management. SOMD, SD, ACE and JK were supported by The Neuro Tanenbaum Open Science Institute, the Canadian Open Neuroscience Platform (funded in part by Brain Canada), and McGill Healthy Brains for Healthy Lives. NM and LZ are funded by the NIH under grant number U24HG006941. MM is funded by EUH2020 CINECA grant number 825775. NM, VN and NSM are funded by the NHLBI award number U24HL135600. JDS and GK are funded by the Wellcome Trust grant 360G-Wellcome-201535_Z_16_Z and previously the EU H2020 Corbel grant number 645248.

Pulmonary function is an indicator of well-being, and pulmonary pathologies are the third major cause of death worldwide. FEV1, FVC, and PEF are quantitively used to assess pulmonary function. We conducted a genome-wide association analysis of pulmonary function in 383,471 individuals of European and 5,978 African descent represented in the UK Biobank. Here, we report 817 variants in Europeans and 3 in Africans associated (p-values < 5 × 10−8) with three pulmonary function parameters; FEV1, FVC and PEF. In addition to 377 variants in Europeans previously reported to be associated with phenotypes related to pulmonary function, we identified 330 novel loci, including an ISX intergenic variant rs369476290 on chromosome 22 in Africans and a KDM2A intron variant rs12790261 on chromosome 11 in Europeans. Remarkably, we find no shared variants among Africans and Europeans. Enrichment analyses of variants separately for each ancestry background revealed significant enrichment for terms related to pulmonary phenotypes in Europeans but not Africans. Further analysis of studies of pulmonary phenotypes revealed individuals of European background are disproportionally overrepresented in datasets compared to Africans, with the gap widening over the past five years. Our findings offer a better understanding of the different variants that modify pulmonary function in Africans and Europeans, a significant finding for future GWAS studies and medicine.

Investigating variation in genes involved in theabsorption, distribution, metabolism, andexcretion(ADME) of drugs are key to characterising pharmacogenomic (PGx) relationships. ADME gene variation is relatively well characterised in European and Asian populations, but African populations are under-studied – which has implications for safe and effective drug use in Africa.The genetic diversity of ADME genes across sub-Saharan African populations is large. The Southern African population cluster is most distinct from that of far West Africa. PGx strategies based on European variants will be of limited use in African populations.Although established variants are important, PGx must take into account the full range of African variation. This work urges further characterisation of variants in African populations includingin vitroandin silicostudies, and to consider the unique African ADME landscape when developing precision medicine guidelines and tools for African populations.Author summaryThe ADME genes are a group of genes that play a key role in absorption, distribution, metabolism and excretion of drugs. Variations in these genes can have a significant impact on drug safety and efficacy. Africa has a high level of genetic variation and is under-studied in drug development, which makes study of variations in these genes in African populations very important. Using a new data set of 458 high-coverage genomes from across Africa, we characterise the extent and impact of variation in the ADME genes, looking at both single nucleotide and copy number variations. We identified 343,368 variants, including 40,692 novel variants, and 930 coding variants which are predicted to have high impact on function. Our discovery curves indicate that there will be considerable value in sequencing more African genomes. Moreover, relatively few of these novel variants are captured on common genotyping arrays. We show that there is considerable diversity within Africa in some important genes, and this will have significant consequences for the emerging field of precision medicine in Africa.

A Correction to this paper has been published: https://doi.org/10.1038/s41586-021-03286-9.

International audience; Sickle cell disease (SCD) is a debilitating single gene disorder caused by a single point mutation that results in physical deformation (i.e. sickling) of erythrocytes at reduced oxygen tensions. Up to 75% of SCD in newborns world-wide occurs in sub-Saharan Africa, where neonatal and childhood mortality from sickle cell related complications is high. While SCD research across the globe is tackling the disease on multiple fronts, advances have yet to significantly impact on the health and quality of life of SCD patients, due to lack of coordination of these disparate efforts. Ensuring data across studies is directly comparable through standardization is a necessary step towards realizing this goal. Such a standardization requires the development and implementation of a disease-specific ontology for SCD that is applicable globally. Ontology development is best achieved by bringing together experts in the domain to contribute their knowledge.The SCD community and H3ABioNet members joined forces at a recent SCD Ontology workshop to develop an ontology covering aspects of SCD under the classes: phenotype, diagnostics, therapeutics, quality of life, disease modifiers and disease stage. The aim of the workshop was for participants to contribute their expertise to development of the structure and contents of the SCD ontology. Here we describe the proceedings of the Sickle Cell Disease Ontology Workshop held in Cape Town South Africa in February 2016 and its outcomes. The objective of the workshop was to bring together experts in SCD from around the world to contribute their expertise to the development of various aspects of the SCD ontology.

Aim: To provide pharmacogenomics reporting guidelines, the information and tools required for reporting to public omic databases. Material & methods: For effective DMET data interpretation, sharing, interoperability, reproducibility and reporting, we propose the Minimum Information required for a DMET Experiment (MIDE) reporting. Results: MIDE provides reporting guidelines and describes the information required for reporting, data storage and data sharing in the form of XML. Conclusion: The MIDE guidelines will benefit the scientific community with pharmacogenomics experiments, including reporting pharmacogenomics data from other technology platforms, with the tools that will ease and automate the generation of such reports using the standardized MIDE XML schema, facilitating the sharing, dissemination, reanalysis of datasets through accessible and transparent pharmacogenomics data reporting.

The H3ABioNet pan-African bioinformatics network, which is funded to support the Human Heredity and Health in Africa (H3Africa) program, has developed node-assessment exercises to gauge the ability of its participating research and service groups to analyze typical genome-wide datasets being generated by H3Africa research groups. We describe a framework for the assessment of computational genomics analysis skills, which includes standard operating procedures, training and test datasets, and a process for administering the exercise. We present the experiences of 3 research groups that have taken the exercise and the impact on their ability to manage complex projects. Finally, we discuss the reasons why many H3ABioNet nodes have declined so far to participate and potential strategies to encourage them to do so., Author summary Many programs have been developed to boost the technical and computational skills of scientists working in low to medium income countries (LMIC), who often struggle to remain competitive with their peers in more developed parts of the world. Typically, these programs rely on intensive workshops where students acquire and exercise these skills under the supervision of experienced trainers. However, when trainees return to their home institutions, even after extensive exposure to state of the art techniques, they often find it difficult to put the skills they have acquired into practice and to establish themselves as fully independent practitioners. We have attempted to build a framework through which teams of scientists in African research groups can demonstrate that they have acquired the necessary skills to analyze different types of genomic datasets. Three teams of scientists who have successfully submitted to this assessment exercise report their positive experiences. Many potential participants have so far declined the opportunity, and we discuss the reasons for their reluctance as well as possible ways to facilitate their engagement and provide them with incentives. We argue that assessments such as this could be part of any program aiming to develop technical skills in scientists wishing to support genomic research programs.

ObjectiveThis project aimed to develop and propose a standardised reporting guideline for kidney disease research and clinical data reporting, in order to improve kidney disease data quality and integrity, and combat challenges associated with the management and challenges of ‘Big Data’.MethodsA list of recommendations was proposed for the reporting guideline based on the systematic review and consolidation of previously published data collection and reporting standards, including PhenX measures and Minimal Information about a Proteomics Experiment (MIAPE). Thereafter, these recommendations were reviewed by domain-specialists using an online survey, developed in Research Electronic Data Capture (REDCap). Following interpretation and consolidation of the survey results, the recommendations were mapped to existing ontologies using Zooma, Ontology Lookup Service and the Bioportal search engine. Additionally, an associated eXtensible Markup Language schema was created for the REDCap implementation to increase user friendliness and adoption.ResultsThe online survey was completed by 53 respondents; the majority of respondents were dual clinician-researchers (57%), based in Australia (35%), Africa (33%) and North America (22%). Data elements within the reporting standard were identified as participant-level, study-level and experiment-level information, further subdivided into essential or optional information.ConclusionThe reporting guideline is readily employable for kidney disease research projects, and also adaptable for clinical utility. The adoption of the reporting guideline in kidney disease research can increase data quality and the value for long-term preservation, ensuring researchers gain the maximum benefit from their collected and generated data.

The need for portable and reproducible genomics analysis pipelines is growing globally as well as in Africa, especially with the growth of collaborative projects like the Human Health and Heredity in Africa Consortium (H3Africa). The Pan-African H3Africa Bioinformatics Network (H3ABioNet) recognized the need for portable, reproducible pipelines adapted to heterogeneous computing environments, and for the nurturing of technical expertise in workflow languages and containerization technologies. Building on the network’s Standard Operating Procedures (SOPs) for common genomic analyses, H3ABioNet arranged its first Cloud Computing and Reproducible Workflows Hackathon in 2016, with the purpose of translating those SOPs into analysis pipelines able to run on heterogeneous computing environments and meeting the needs of H3Africa research projects. This paper describes the preparations for this hackathon and reflects upon the lessons learned about its impact on building the technical and scientific expertise of African researchers. The workflows developed were made publicly available in GitHub repositories and deposited as container images on Quay.io.

The management and analyses of large datasets is one of the grand challenges of modern biomedical research. Establishing methods to harmonise and standardise data collection, reporting, sharing and the employed data dictionaries, can support the resolution of these challenges whilst improving research quality, data quality and integrity, allowing sustainable knowledge transfer through re-usability, interoperability, reproducibility. The current project aimed to develop and propose a standardised reporting guideline for stroke research and clinical data reporting. Through systematic consolidation and harmonization of published data collection and reporting standards, several recommendations were drafted for the proposed guideline. These recommendations were reviewed by domain-researchers and clinicians using an online survey, developed in REDCap. The survey was completed by 20 international stroke-specialists, majority of respondents were based in Africa (10), followed by America, Europe and Australia (10). Of these respondents; the majority were working as dual clinician-researchers (57%) with more than 10 years’ experience in the field (78%). Data elements within the reporting standard were classified as participant-, study- and experiment-level information, further subdivided into essential or optional information, and defined using existing ontologies. The proposed reporting guideline can be employed for research utility and adapted for clinical utility as well. It is accompanied with an associated XML schema for REDCap implementation, to increase the user friendliness of data capturing, sharing, reporting and governance. Ultimately, the adoption of common reporting in stroke research has the potential to ensure that researchers gain the maximum benefit from their generated data and data collections.

Summary: Gene Ontology (GO) semantic similarity measures are being used for biological knowledge discovery based on GO annotations by integrating biological information contained in the GO structure into data analyses. To empower users to quickly compute, manipulate and explore these measures, we introduce A-DaGO-Fun (ADaptable Gene Ontology semantic similarity-based Functional analysis). It is a portable software package integrating all known GO information content-based semantic similarity measures and relevant biological applications associated with these measures. A-DaGO-Fun has the advantage not only of handling datasets from the current high-throughput genome-wide applications, but also allowing users to choose the most relevant semantic similarity approach for their biological applications and to adapt a given module to their needs. Availability and implementation: A-DaGO-Fun is freely available to the research community at http://web.cbio.uct.ac.za/ITGOM/adagofun. It is implemented in Linux using Python under free software (GNU General Public Licence). Contact: gmazandu@cbio.uct.ac.za or Nicola.Mulder@uct.ac.za Supplementary information: Supplementary data are available at Bioinformatics online.

A shortage of practical skills and relevant expertise is possibly the primary obstacle to social upliftment and sustainable development in Africa. The “omics” fields, especially genomics, are increasingly dependent on the effective interpretation of large and complex sets of data. Despite abundant natural resources and population sizes comparable with many first-world countries from which talent could be drawn, countries in Africa still lag far behind the rest of the world in terms of specialized skills development. Moreover, there are serious concerns about disparities between countries within the continent. The multidisciplinary nature of the bioinformatics field, coupled with rare and depleting expertise, is a critical problem for the advancement of bioinformatics in Africa. We propose a formalized matchmaking system, which is aimed at reversing this trend, by introducing the Knowledge Transfer Programme (KTP). Instead of individual researchers travelling to other labs to learn, researchers with desirable skills are invited to join African research groups for six weeks to six months. Visiting researchers or trainers will pass on their expertise to multiple people simultaneously in their local environments, thus increasing the efficiency of knowledge transference. In return, visiting researchers have the opportunity to develop professional contacts, gain industry work experience, work with novel datasets, and strengthen and support their ongoing research. The KTP develops a network with a centralized hub through which groups and individuals are put into contact with one another and exchanges are facilitated by connecting both parties with potential funding sources. This is part of the PLOS Computational Biology Education collection.

Motivation: Despite numerous successful Genome-wide Association Studies (GWAS), detecting variants that have low disease risk still poses a challenge. GWAS may miss disease genes with weak genetic effects or strong epistatic effects due to the single-marker testing approach commonly used. GWAS may thus generate false negative or inconclusive results, suggesting the need for novel methods to combine effects of single nucleotide polymorphisms within a gene to increase the likelihood of fully characterizing the susceptibility gene. Results: We developed ancGWAS, an algebraic graph-based centrality measure that accounts for linkage disequilibrium in identifying significant disease sub-networks by integrating the association signal from GWAS data sets into the human protein–protein interaction (PPI) network. We validated ancGWAS using an association study result from a breast cancer data set and the simulation of interactive disease loci in the simulation of a complex admixed population, as well as pathway-based GWAS simulation. This new approach holds promise for deconvoluting the interactions between genes underlying the pathogenesis of complex diseases. Results obtained yield a novel central breast cancer sub-network of the human interactome implicated in the proteoglycan syndecan-mediated signaling events pathway which is known to play a major role in mesenchymal tumor cell proliferation, thus providing further insights into breast cancer pathogenesis. Availability and implementation: The ancGWAS package and documents are available at http://www.cbio.uct.ac.za/~emile/software.html Contact: emile.chimusa@uct.ac.za, Nicola.Mulder@uct.ac.za Supplementary information: Supplementary data are available at Bioinformatics online.

Background: Although pockets of bioinformatics excellence have developed in Africa, generally, large-scale genomic data analysis has been limited by the availability of expertise and infrastructure. H3ABioNet, a pan-African bioinformatics network, was established to build capacity specifically to enable H3Africa (Human Heredity and Health in Africa) researchers to analyze their data in Africa. Since the inception of the H3Africa initiative, H3ABioNet’s role has evolved in response to changing needs from the consortium and the African bioinformatics community.Objectives: H3ABioNet set out to develop core bioinformatics infrastructure and capacity for genomics research in various aspects of data collection, transfer, storage, and analysis.Methods and Results: Various resources have been developed to address genomic data management and analysis needs of H3Africa researchers and other scientific communities on the continent. NetMap was developed and used to build an accurate picture of network performance within Africa and between Africa and the rest of the world, and Globus Online has been rolled out to facilitate data transfer. A participant recruitment database was developed to monitor participant enrollment, and data is being harmonized through the use of ontologies and controlled vocabularies. The standardized metadata will be integrated to provide a search facility for H3Africa data and biospecimens. Because H3Africa projects are generating large-scale genomic data, facilities for analysis and interpretation are critical. H3ABioNet is implementing several data analysis platforms that provide a large range of bioinformatics tools or workflows, such as Galaxy, the Job Management System, and eBiokits. A set of reproducible, portable, and cloud-scalable pipelines to support the multiple H3Africa data types are also being developed and dockerized to enable execution on multiple computing infrastructures. In addition, new tools have been developed for analysis of the uniquely divergent African data and for downstream interpretation of prioritized variants. To provide support for these and other bioinformatics queries, an online bioinformatics helpdesk backed by broad consortium expertise has been established. Further support is provided by means of various modes of bioinformatics training.Conclusions: For the past 4 years, the development of infrastructure support and human capacity through H3ABioNet, have significantly contributed to the establishment of African scientific networks, data analysis facilities, and training programs. Here, we describe the infrastructure and how it has affected genomics and bioinformatics research in Africa.HighlightsH3ABioNet is building capacity to enable analysis of genomic data in Africa.Infrastructure has been built for clinical and genomic data storage, management, and analysis.New algorithms and pipelines for African genomic data analysis have been developed.Data are being harmonized using ontologies to enable easy search and retrieval.Genomics training is implemented using various online and face-to-face approaches.