The African Society of Human Genetics successfully launches global data science workshops.

To accelerate the impact of African genomics on human health, data science skills and awareness of Africa's rich genetic diversity must be strengthened globally. We describe the first African genomics data science workshop, implemented by the African Society of Human Genetics (AfSHG) and international partners, providing a framework for future workshops.

High-depth African genomes inform human migration and health.

The African continent is regarded as the cradle of modern humans and African genomes contain more genetic variation than those from any other continent, yet only a fraction of the genetic diversity among African individuals has been surveyed1. Here we performed whole-genome sequencing analyses of 426 individuals-comprising 50 ethnolinguistic groups, including previously unsampled populations-to explore the breadth of genomic diversity across Africa. We uncovered more than 3 million previously undescribed variants, most of which were found among individuals from newly sampled ethnolinguistic groups, as well as 62 previously unreported loci that are under strong selection, which were predominantly found in genes that are involved in viral immunity, DNA repair and metabolism. We observed complex patterns of ancestral admixture and putative-damaging and novel variation, both within and between populations, alongside evidence that Zambia was a likely intermediate site along the routes of expansion of Bantu-speaking populations. Pathogenic variants in genes that are currently characterized as medically relevant were uncommon-but in other genes, variants denoted as 'likely pathogenic' in the ClinVar database were commonly observed. Collectively, these findings refine our current understanding of continental migration, identify gene flow and the response to human disease as strong drivers of genome-level population variation, and underscore the scientific imperative for a broader characterization of the genomic diversity of African individuals to understand human ancestry and improve health.

Genome-wide association study identifies novel candidate malaria resistance genes in Cameroon.

Recent data suggest that only a small fraction of severe malaria heritability is explained by the totality of genetic markers discovered so far. The extensive genetic diversity within African populations means that significant associations are likely to be found in Africa. In their series of multi-site genome-wide association studies (GWAS) across sub-Saharan Africa, the Malaria Genomic Epidemiology Network (MalariaGEN) observed specific limitations and encouraged country-specific analyses. Here, we present findings of a GWAS of Cameroonian participants that contributed to MalariaGEN projects (n = 1103). We identified protective associations at polymorphisms within the enhancer region of CHST15 [Benjamin-Hochberg false discovery rate (FDR) < 0.02] that are specific to populations of African ancestry, and that tag strong eQTLs of CHST15 in hepatic cells. In-silico functional analysis revealed a signature of epigenetic regulation of CHST15 that is preserved in populations in historically malaria endemic regions, with haplotype analysis revealing a haplotype that is specific to these populations. Association analysis by ethnolinguistic group identified protective associations within SOD2 (FDR < 0.04), a gene previously shown to be significantly induced in pre-asymptomatic malaria patients from Cameroon. Haplotype analysis revealed substantial heterogeneity within the beta-like globin (HBB) gene cluster amongst the major ethnic groups in Cameroon confirming differential malaria pressure and underscoring age-old fine-scale genetic structure within the country. Our findings revealed novel insights in the evolutionary genetics of populations living in Cameroon under malaria pressure with new significant protective loci (CHST15 and SOD2) and emphasized the significant attenuation of genetic association signals by fine-scale genetic structure.

Whole exome sequencing reveals pathogenic variants in MYO3A, MYO15A and COL9A3 and differential frequencies in ancestral alleles in hearing impairment genes among individuals from Cameroon.

There is scarcity of known gene variants of hearing impairment (HI) in African populations. This knowledge deficit is ultimately affecting the development of genetic diagnoses. We used whole exome sequencing to investigate gene variants, pathways of interactive genes and the fractions of ancestral overderived alleles for 159 HI genes among 18 Cameroonian patients with non-syndromic HI (NSHI) and 129 ethnically matched controls. Pathogenic and likely pathogenic (PLP) variants were found in MYO3A, MYO15A and COL9A3, with a resolution rate of 50% (9/18 patients). The study identified significant genetic differentiation in novel population-specific gene variants at FOXD4L2, DHRS2L6, RPL3L and VTN between HI patients and controls. These gene variants are found in functional/co-expressed interactive networks with other known HI-associated genes and in the same pathways with VTN being a hub protein, that is, focal adhesion pathway and regulation of the actin cytoskeleton (P-values <0.05). The results suggest that these novel population-specific gene variants are possible modifiers of the HI phenotypes. We found a high proportion of ancestral allele versus derived at low HI patients-specific minor allele frequency in the range of 0.0-0.1. The results showed a relatively low pickup rate of PLP variants in known genes in this group of Cameroonian patients with NSHI. In addition, findings may signal an evolutionary enrichment of some variants of HI genes in patients, as the result of polygenic adaptation, and suggest the possibility of multigenic influence on the phenotype of congenital HI, which deserves further investigations.

Heritability jointly explained by host genotype and microbiome: will improve traits prediction?

As we observe the $70$th anniversary of the publication by Robertson that formalized the notion of 'heritability', geneticists remain puzzled by the problem of missing/hidden heritability, where heritability estimates from genome-wide association studies (GWASs) fall short of that from twin-based studies. Many possible explanations have been offered for this discrepancy, including existence of genetic variants poorly captured by existing arrays, dominance, epistasis and unaccounted-for environmental factors; albeit these remain controversial. We believe a substantial part of this problem could be solved or better understood by incorporating the host's microbiota information in the GWAS model for heritability estimation and may also increase human traits prediction for clinical utility. This is because, despite empirical observations such as (i) the intimate role of the microbiome in many complex human phenotypes, (ii) the overlap between genetic variants associated with both microbiome attributes and complex diseases and (iii) the existence of heritable bacterial taxa, current GWAS models for heritability estimate do not take into account the contributory role of the microbiome. Furthermore, heritability estimate from twin-based studies does not discern microbiome component of the observed total phenotypic variance. Here, we summarize the concept of heritability in GWAS and microbiome-wide association studies, focusing on its estimation, from a statistical genetics perspective. We then discuss a possible statistical method to incorporate the microbiome in the estimation of heritability in host GWAS.

Reviewing and assessing existing meta-analysis models and tools.

Over the past few years, meta-analysis has become popular among biomedical researchers for detecting biomarkers across multiple cohort studies with increased predictive power. Combining datasets from different sources increases sample size, thus overcoming the issue related to limited sample size from each individual study and boosting the predictive power. This leads to an increased likelihood of more accurately predicting differentially expressed genes/proteins or significant biomarkers underlying the biological condition of interest. Currently, several meta-analysis methods and tools exist, each having its own strengths and limitations. In this paper, we survey existing meta-analysis methods, and assess the performance of different methods based on results from different datasets as well as assessment from prior knowledge of each method. This provides a reference summary of meta-analysis models and tools, which helps to guide end-users on the choice of appropriate models or tools for given types of datasets and enables developers to consider current advances when planning the development of new meta-analysis models and more practical integrative tools.

Drug response in association with pharmacogenomics and pharmacomicrobiomics: towards a better personalized medicine.

Researchers have long been presented with the challenge imposed by the role of genetic heterogeneity in drug response. For many years, Pharmacogenomics and pharmacomicrobiomics has been investigating the influence of an individual's genetic background to drug response and disposition. More recently, the human gut microbiome has proven to play a crucial role in the way patients respond to different therapeutic drugs and it has been shown that by understanding the composition of the human microbiome, we can improve the drug efficacy and effectively identify drug targets. However, our knowledge on the effect of host genetics on specific gut microbes related to variation in drug metabolizing enzymes, the drug remains limited and therefore limits the application of joint host-microbiome genome-wide association studies. In this paper, we provide a historical overview of the complex interactions between the host, human microbiome and drugs. While discussing applications, challenges and opportunities of these studies, we draw attention to the critical need for inclusion of diverse populations and the development of an innovative and combined pharmacogenomics and pharmacomicrobiomics approach, that may provide an important basis in personalized medicine.

IHP-PING-generating integrated human protein-protein interaction networks on-the-fly.

Advances in high-throughput sequencing technologies have resulted in an exponential growth of publicly accessible biological datasets. In the 'big data' driven 'post-genomic' context, much work is being done to explore human protein-protein interactions (PPIs) for a systems level based analysis to uncover useful signals and gain more insights to advance current knowledge and answer specific biological and health questions. These PPIs are experimentally or computationally predicted, stored in different online databases and some of PPI resources are updated regularly. As with many biological datasets, such regular updates continuously render older PPI datasets potentially outdated. Moreover, while many of these interactions are shared between these online resources, each resource includes its own identified PPIs and none of these databases exhaustively contains all existing human PPI maps. In this context, it is essential to enable the integration of or combining interaction datasets from different resources, to generate a PPI map with increased coverage and confidence. To allow researchers to produce an integrated human PPI datasets in real-time, we introduce the integrated human protein-protein interaction network generator (IHP-PING) tool. IHP-PING is a flexible python package which generates a human PPI network from freely available online resources. This tool extracts and integrates heterogeneous PPI datasets to generate a unified PPI network, which is stored locally for further applications.

Simulation of African and non-African low and high coverage whole genome sequence data to assess variant calling approaches.

Current variant calling (VC) approaches have been designed to leverage populations of long-range haplotypes and were benchmarked using populations of European descent, whereas most genetic diversity is found in non-European such as Africa populations. Working with these genetically diverse populations, VC tools may produce false positive and false negative results, which may produce misleading conclusions in prioritization of mutations, clinical relevancy and actionability of genes. The most prominent question is which tool or pipeline has a high rate of sensitivity and precision when analysing African data with either low or high sequence coverage, given the high genetic diversity and heterogeneity of this data. Here, a total of 100 synthetic Whole Genome Sequencing (WGS) samples, mimicking the genetics profile of African and European subjects for different specific coverage levels (high/low), have been generated to assess the performance of nine different VC tools on these contrasting datasets. The performances of these tools were assessed in false positive and false negative call rates by comparing the simulated golden variants to the variants identified by each VC tool. Combining our results on sensitivity and positive predictive value (PPV), VarDict [PPV = 0.999 and Matthews correlation coefficient (MCC) = 0.832] and BCFtools (PPV = 0.999 and MCC = 0.813) perform best when using African population data on high and low coverage data. Overall, current VC tools produce high false positive and false negative rates when analysing African compared with European data. This highlights the need for development of VC approaches with high sensitivity and precision tailored for populations characterized by high genetic variations and low linkage disequilibrium.

Genome-wide heritability analysis of severe malaria resistance reveals evidence of polygenic inheritance.

BACKGROUND: Estimating single nucleotide polymorphism (SNP)-heritability (h2g) of severe malaria resistance and its distribution across the genome might shed new light in to the underlying biology. METHOD: We investigated h2g of severe malaria resistance from a genome-wide association study (GWAS) dataset (sample size = 11 657). We estimated the h2g and partitioned in to chromosomes, allele frequencies and annotations using the genetic relationship-matrix restricted maximum likelihood approach. We further examined non-cell type-specific and cell type-specific enrichments from GWAS-summary statistics. RESULTS: The h2g of severe malaria resistance was estimated at 0.21 (se = 0.05, P = 2.7 × 10-5), 0.20 (se = 0.05, P = 7.5 × 10-5) and 0.17 (se = 0.05, P = 7.2 × 10-4) in Gambian, Kenyan and Malawi populations, respectively. A comparable range of h2g [0.21 (se = 0.02, P < 1 × 10-5)] was estimated from GWAS-summary statistics meta-analysed across the three populations. Partitioning analysis from raw genotype data showed significant enrichment of h2g in genic SNPs while summary statistics analysis suggests evidences of enrichment in multiple categories. Supporting the polygenic inheritance, the h2g of severe malaria resistance is distributed across the chromosomes and allelic frequency spectrum. However, the h2g is disproportionately concentrated on three chromosomes (chr 5, 11 and 20), suggesting cost-effectiveness of targeting these chromosomes in future malaria genomic sequencing studies. CONCLUSION: We report for the first time that the heritability of malaria resistance is largely ascribed by common SNPs and the causal variants are overrepresented in protein coding regions of the genome. Further studies with larger sample sizes are needed to better understand the underpinning genetics of severe malaria resistance.

Dissecting genome-wide studies for microbiome-related metabolic diseases.

Despite the meteoric rise in genome-wide association studies for metabolic diseases (MetD) over the last few years, our understanding of the pathogenesis of these diseases is still far from complete. Recent developments have established that MetD arises from complex interactions between host genetics, the gut microbiome and the environment. However, our knowledge of the genetic and microbiome components involved and the underlying molecular mechanisms remains limited. Here, we review and summarize recent studies investigating the genetic and microbiome basis of MetD. Then, given the critical importance of study-individual's ancestry in these studies, we leverage 4932 whole-genome sequence samples from 18 worldwide ethnic groups to examine genetic diversity in currently reported variants associated with MetD. The analyses show marked differences in gene-specific proportion of pathogenic single-nucleotide polymorphisms (SNPs) and gene-specific SNPs MAFs across ethnic groups, highlighting the importance of population- and ethnic-specific investigations in pinpointing the causative factors for MetD. We conclude with a discussion of research areas where further investigation on interactions between host genetics, microbiome and the environment is needed.

Dating admixture events is unsolved problem in multi-way admixed populations.

Advances in human sequencing technologies, coupled with statistical and computational tools, have fostered the development of methods for dating admixture events. These methods have merits and drawbacks in estimating admixture events in multi-way admixed populations. Here, we first provide a comprehensive review and comparison of current methods pertinent to dating admixture events. Second, we assess various admixture dating tools. We do so by performing various simulations. Third, we apply the top two assessed methods to real data of a uniquely admixed population from South Africa. Results reveal that current dating admixture models are not sufficiently equipped to estimate ancient admixtures events and to identify multi-faceted admixture events in complex multi-way admixed populations. We conclude with a discussion of research areas where further work on dating admixture-based methods is needed.

Computational/in silico methods in drug target and lead prediction.

Drug-like compounds are most of the time denied approval and use owing to the unexpected clinical side effects and cross-reactivity observed during clinical trials. These unexpected outcomes resulting in significant increase in attrition rate centralizes on the selected drug targets. These targets may be disease candidate proteins or genes, biological pathways, disease-associated microRNAs, disease-related biomarkers, abnormal molecular phenotypes, crucial nodes of biological network or molecular functions. This is generally linked to several factors, including incomplete knowledge on the drug targets and unpredicted pharmacokinetic expressions upon target interaction or off-target effects. A method used to identify targets, especially for polygenic diseases, is essential and constitutes a major bottleneck in drug development with the fundamental stage being the identification and validation of drug targets of interest for further downstream processes. Thus, various computational methods have been developed to complement experimental approaches in drug discovery. Here, we present an overview of various computational methods and tools applied in predicting or validating drug targets and drug-like molecules. We provide an overview on their advantages and compare these methods to identify effective methods which likely lead to optimal results. We also explore major sources of drug failure considering the challenges and opportunities involved. This review might guide researchers on selecting the most efficient approach or technique during the computational drug discovery process.

Detecting genetic modifiers of spondyloepimetaphyseal dysplasia with joint laxity in the Caucasian Afrikaner community.

Spondyloepimetaphyseal dysplasia with joint laxity (SEMDJL) is an autosomal-recessive skeletal dysplasia. A relatively large number of patients with SEMDJL have been identified in the Caucasian Afrikaans-speaking community in South Africa. We used a combination of Genome-Wide Human Single Nucleotide Polymorphism (SNP) Array 6.0 data and whole exomic data to potentially dissect genetic modifiers associated with SEMDJL in Caucasian Afrikaans-speaking patients. Leveraging the family-based association signal in prioritizing candidate mutations, we identified two potential modifier genes, COL1A2 and MATN1, and replicating previously identified mutation in KIF22. Importantly, our findings of genetic modifier genes and previously identified mutations are layered on the same sub-network implicated in syndromes characterized by skeletal abnormalities and intellectual disability, bone and connective tissue fragility. This study has potentially provided crucial insights in identifying the indirect modifying mutation(s) linked to the true causal mutation associated with SEMDJL. It is a critical lesson that one may use constructively especially when the pace of exomic sequencing of rare disorders continues apace.

Post genome-wide association analysis: dissecting computational pathway/network-based approaches.

Over thousands of genetic associations to diseases have been identified by genome-wide association studies (GWASs), which conceptually is a single-marker-based approach. There are potentially many uses of these identified variants, including a better understanding of the pathogenesis of diseases, new leads for studying underlying risk prediction and clinical prediction of treatment. However, because of inadequate power, GWAS might miss disease genes and/or pathways with weak genetic or strong epistatic effects. Driven by the need to extract useful information from GWAS summary statistics, post-GWAS approaches (PGAs) were introduced. Here, we dissect and discuss advances made in pathway/network-based PGAs, with a particular focus on protein-protein interaction networks that leverage GWAS summary statistics by combining effects of multiple loci, subnetworks or pathways to detect genetic signals associated with complex diseases. We conclude with a discussion of research areas where further work on summary statistic-based methods is needed.

A comprehensive survey of models for dissecting local ancestry deconvolution in human genome.

Over the past decade, studies of admixed populations have increasingly gained interest in both medical and population genetics. These studies have so far shed light on the patterns of genetic variation throughout modern human evolution and have improved our understanding of the demographics and adaptive processes of human populations. To date, there exist about 20 methods or tools to deconvolve local ancestry. These methods have merits and drawbacks in estimating local ancestry in multiway admixed populations. In this article, we survey existing ancestry deconvolution methods, with special emphasis on multiway admixture, and compare these methods based on simulation results reported by different studies, computational approaches used, including mathematical and statistical models, and biological challenges related to each method. This should orient users on the choice of an appropriate method or tool for given population admixture characteristics and update researchers on current advances, challenges and opportunities behind existing ancestry deconvolution methods.

Network-driven analysis of human-Plasmodium falciparum interactome: processes for malaria drug discovery and extracting in silico targets.

BACKGROUND: The emergence and spread of malaria drug resistance have resulted in the need to understand disease mechanisms and importantly identify essential targets and potential drug candidates. Malaria infection involves the complex interaction between the host and pathogen, thus, functional interactions between human and Plasmodium falciparum is essential to obtain a holistic view of the genetic architecture of malaria. Several functional interaction studies have extended the understanding of malaria disease and integrating such datasets would provide further insights towards understanding drug resistance and/or genetic resistance/susceptibility, disease pathogenesis, and drug discovery. METHODS: This study curated and analysed data including pathogen and host selective genes, host and pathogen protein sequence data, protein-protein interaction datasets, and drug data from literature and databases to perform human-host and P. falciparum network-based analysis. An integrative computational framework is presented that was developed and found to be reasonably accurate based on various evaluations, applications, and experimental evidence of outputs produced, from data-driven analysis. RESULTS: This approach revealed 8 hub protein targets essential for parasite and human host-directed malaria drug therapy. In a semantic similarity approach, 26 potential repurposable drugs involved in regulating host immune response to inflammatory-driven disorders and/or inhibiting residual malaria infection that can be appropriated for malaria treatment. Further analysis of host-pathogen network shortest paths enabled the prediction of immune-related biological processes and pathways subverted by P. falciparum to increase its within-host survival. CONCLUSIONS: Host-pathogen network analysis reveals potential drug targets and biological processes and pathways subverted by P. falciparum to enhance its within malaria host survival. The results presented have implications for drug discovery and will inform experimental studies.

Utilization of Pneumococcal Vaccine and Penicillin Prophylaxis in Sickle Cell Disease in Three African Countries: Assessment among Healthcare Providers in SickleInAfrica.

Sickle cell disease is a genetic disease with a predisposition to infections caused by encapsulated organisms, especially Streptococcus pneumoniae. Pneumococcal vaccines and prophylactic penicillin have reduced the rate of this infection and mortality in sickle cell disease. However, implementation of these interventions is limited in Africa. The objectives of the study were to assess health care providers' behaviors with the implementation of pneumococcal vaccination and penicillin prophylaxis and to identify barriers to their use. A 25-item online questionnaire was administered through SickleinAfrica: a network of researchers, and healthcare providers, in Ghana, Nigeria, and Tanzania, working to improve health outcomes of sickle cell disease in Africa. Data was collected and managed using the Research Electronic Data Capture (REDCap), tools and data analysis was done using STATA version 13 and R statistical software. Eighty-two medical practitioners responded to the questionnaire. Only 54.0 and 48.7% of respondents indicated the availability of published guidelines on sickle cell disease management and pneumococcal vaccine use, respectively, at their facilities. The majority (54.0%) perceived that the vaccines are effective but over 20.0% were uncertain of their usefulness. All respondents from Ghana and Tanzania affirmed the availability of guidelines for penicillin prophylaxis in contrast to 44.1% in Nigeria. Eighty-five percent of respondents affirmed the need for penicillin prophylaxis but 15.0% had a contrary opinion for reasons including the rarity of isolation of Streptococcus pneumoniae in African studies, and therefore, the uncertainty of its benefit. Lack of published guidelines on the management of sickle cell disease and doubts about the necessity of prophylactic measures are potential barriers to the implementation of effective interventions.

Large-scale data-driven integrative framework for extracting essential targets and processes from disease-associated gene data sets.

Populations worldwide currently face several public health challenges, including growing prevalence of infections and the emergence of new pathogenic organisms. The cost and risk associated with drug development make the development of new drugs for several diseases, especially orphan or rare diseases, unappealing to the pharmaceutical industry. Proof of drug safety and efficacy is required before market approval, and rigorous testing makes the drug development process slow, expensive and frequently result in failure. This failure is often because of the use of irrelevant targets identified in the early steps of the drug discovery process, suggesting that target identification and validation are cornerstones for the success of drug discovery and development. Here, we present a large-scale data-driven integrative computational framework to extract essential targets and processes from an existing disease-associated data set and enhance target selection by leveraging drug-target-disease association at the systems level. We applied this framework to tuberculosis and Ebola virus diseases combining heterogeneous data from multiple sources, including protein-protein functional interaction, functional annotation and pharmaceutical data sets. Results obtained demonstrate the effectiveness of the pipeline, leading to the extraction of essential drug targets and to the rational use of existing approved drugs. This provides an opportunity to move toward optimal target-based strategies for screening available drugs and for drug discovery. There is potential for this model to bridge the gap in the production of orphan disease therapies, offering a systematic approach to predict new uses for existing drugs, thereby harnessing their full therapeutic potential.