The worldwide burden of tuberculosis (TB) remains an enormous problem, and is particularly severe in the admixed South African Coloured (SAC) population residing in the Western Cape. Despite evidence from twin studies suggesting a strong genetic component to TB resistance, only a few loci have been identified to date. In this work, we conduct a genome-wide association study (GWAS), meta-analysis and trans-ethnic fine mapping to attempt the replication of previously identified TB susceptibility loci. Our GWAS results confirm the WT1 chr11 susceptibility locus (rs2057178: odds ratio = 0.62, P = 2.71e(-06)) previously identified by Thye et al., but fail to replicate previously identified polymorphisms in the TLR8 gene and locus 18q11.2. Our study demonstrates that the genetic contribution to TB risk varies between continental populations, and illustrates the value of including admixed populations in studies of TB risk and other complex phenotypes. Our evaluation of local ancestry based on the real and simulated data demonstrates that case-only admixture mapping is currently impractical in multi-way admixed populations, such as the SAC, due to spurious deviations in average local ancestry generated by current local ancestry inference methods. This study provides insights into identifying disease genes and ancestry-specific disease risk in multi-way admixed populations.

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.

The Southern African Human Genome Programme is a national initiative that aspires to unlock the unique genetic character of southern African populations for a better understanding of human genetic diversity. In this pilot study the Southern African Human Genome Programme characterizes the genomes of 24 individuals (8 Coloured and 16 black southeastern Bantu-speakers) using deep whole-genome sequencing. A total of ~16 million unique variants are identified. Despite the shallow time depth since divergence between the two main southeastern Bantu-speaking groups (Nguni and Sotho-Tswana), principal component analysis and structure analysis reveal significant (p < 10−6) differentiation, and FST analysis identifies regions with high divergence. The Coloured individuals show evidence of varying proportions of admixture with Khoesan, Bantu-speakers, Europeans, and populations from the Indian sub-continent. Whole-genome sequencing data reveal extensive genomic diversity, increasing our understanding of the complex and region-specific history of African populations and highlighting its potential impact on biomedical research and genetic susceptibility to disease.

The involvement of the microbiome in health and disease is well established. Microbiome genome-wide association studies (mGWAS) are used to elucidate the interaction of host genetic variation with the microbiome. The emergence of this relatively new field has been facilitated by the advent of next generation sequencing technologies that enable the investigation of the complex interaction between host genetics and microbial communities. In this paper, we review recent studies investigating host–microbiome interactions using mGWAS. Additionally, we highlight the marked disparity in the sampling population of mGWAS carried out to date and draw attention to the critical need for inclusion of diverse populations.

SUMMARY Micro-albuminuria and glomerular hyperfiltration are primary indicators of renal dysfunctions in Sickle Cell Disease (SCD), with more severe manifestations previously associated with variants in APOL1 and HMOX1 among African Americans. We have investigated 413 SCD patients from Cameroon. Anthropometric variables, haematological indices, crude albuminuria, albumin-to-creatinine ratio (ACR) and estimated glomerular filtration rate (eGFR) were measured. Patients were genotyped for 3.7 kB alpha-globin gene (HBA1/HBA2) deletion, and for variants in APOL1 (G1/G2; rs60910145, rs73885319, rs71785313) and HMOX1 (rs3074372, rs743811). The median age was 15 years; the majority presented with micro-albuminuria (60.9%; n = 248), and approximately half with glomerular hyperfiltration (49.5%; n = 200). Age, male sex, haemoglobin level, leucocyte count, mean corpuscular volume, blood pressure, body mass index and creatinine levels significantly affected albuminuria and/or eGFR. Co-inheritance of alpha-thalassaemia was protective against macro-albuminuria (p = 0.03). APOL1 G1/G2 risk variants were significantly associated with the ACR (p = 0.01) and borderline with eGFR (p = 0.07). HMOX1 - rs743811 was borderline associated with micro-albuminuria (p = 0.07) and macro-albuminuria (p = 0.06). The results revealed a high proportion of micro-albuminuria and glomerular hyperfiltration among Cameroonian SCD patients, and support the possible use of targeted genetic biomarkers for risks assessment.

We aimed to investigate the clinical and genetic predictors of painful vaso-occlusive crises (VOC) in sickle cell disease (SCD) in Cameroon. Socio-demographics, clinical variables/events and haematological indices were acquired. Genotyping was performed for 40 variants in 17 pain-related genes, three fetal haemoglobin (HbF)-promoting loci, two kidney dysfunctions-related genes, and HBA1/HBA2 genes. Statistical models using regression frameworks were performed in R . A total of 436 hydoxycarbamide- and opioid-naïve patients were studied; median age was 16 years. Female sex, body mass index, Hb/HbF, blood transfusions, leucocytosis and consultation or hospitalisation rates significantly correlated with VOC. Three pain-related genes variants correlated with VOC (CACNA2D3-rs6777055, P = 0·025; DRD2-rs4274224, P = 0·037; KCNS1-rs734784, P = 0·01). Five pain-related genes variants correlated with hospitalisation/consultation rates. (COMT-rs6269, P = 0·027; FAAH-rs4141964, P = 0·003; OPRM1-rs1799971, P = 0·031; ADRB2-rs1042713; P < 0·001; UGT2B7-rs7438135, P = 0·037). The 3·7 kb HBA1/HBA2 deletion correlated with increased VOC (P = 0·002). HbF-promoting loci variants correlated with decreased hospitalisation (BCL11A-rs4671393, P = 0·026; HBS1L-MYB-rs28384513, P = 0·01). APOL1 G1/G2 correlated with increased hospitalisation (P = 0·048). This first study from Africa has provided evidence supporting possible development of genetic risk model for pain in SCD.

Gene Ontology (GO) semantic similarity tools enable retrieval of semantic similarity scores, which incorporate biological knowledge embedded in the GO structure for comparing or classifying different proteins or list of proteins based on their GO annotations. This facilitates a better understanding of biological phenomena underlying the corresponding experiment and enables the identification of processes pertinent to different biological conditions. Currently, about 14 tools are available, which may play an important role in improving protein analyses at the functional level using different GO semantic similarity measures. Here we survey these tools to provide a comprehensive view of the challenges and advances made in this area to avoid redundant effort in developing features that already exist, or implementing ideas already proven to be obsolete in the context of GO. This helps researchers, tool developers, as well as end users, understand the underlying semantic similarity measures implemented through knowledge of pertinent features of, and issues related to, a particular tool. This should empower users to make appropriate choices for their biological applications and ensure effective knowledge discovery based on GO annotations.

Admixture is a well known confounder in genetic association studies. If genome-wide data is not available, as would be the case for candidate gene studies, ancestry informative markers (AIMs) are required in order to adjust for admixture. The predominant population group in the Western Cape, South Africa, is the admixed group known as the South African Coloured (SAC). A small set of AIMs that is optimized to distinguish between the five source populations of this population (African San, African non-San, European, South Asian, and East Asian) will enable researchers to cost-effectively reduce false-positive findings resulting from ignoring admixture in genetic association studies of the population. Using genome-wide data to find SNPs with large allele frequency differences between the source populations of the SAC, as quantified by Rosenberg et. al's -statistic, we developed a panel of AIMs by experimenting with various selection strategies. Subsets of different sizes were evaluated by measuring the correlation between ancestry proportions estimated by each AIM subset with ancestry proportions estimated using genome-wide data. We show that a panel of 96 AIMs can be used to assess ancestry proportions and to adjust for the confounding effect of the complex five-way admixture that occurred in the South African Coloured population.