Establishment of “The South African Bioinformatics Student Council” and activity highlights:

The South African Society for Bioinformatics1 (SASBi) was officially formed in September 2012 during a joint Congress with the South African Genetics Society (SAGS). Prior to this there was no official body to represent bioinformatic researchers and students in the country. The establishment of SASBi also led to the establishment of the Student Society as a platform for students to meet and discuss their research activities, but also to socialise and broaden their network of knowledge and friendships. A small group of students joined as volunteers to pioneer and set up a SASBi Student Council (SASBiSC). As a first step, one representative, selected from the attendees present at the first Joint Congress of SASBi and SAGS, was elected to the main SASBi Council

Pharmacogenetics of CYP2A6, CYP2B6, and UGT2B7 in the Context of HIV Treatments in African Populations

Objectives: This study focuses on identifying variations in selected CYP genes related to treatment responses in patients with HIV in African populations by investigating variant characteristics and effects in African cohorts. Design: Cytochrome P450 (CYP) 2A6, 2B6, and Uridine 5’-diphospho-glucuronosyltransferase (UGT) 2B7 allele frequencies were studied using public-domain datasets obtained from the 1000 Genomes Phase 3 project, the African Genome Variation Project (AGVP), and the South African Human Genome Programme (SAHGP). Methods: Variant annotations were performed using self-identified ethnicities to conduct allele frequency analysis in a population-stratification-sensitive manner. The NCBI DB-SNP database was used to identify documented variants and standard frequencies, and the E! Ensembl Variant Effect Predictor tool was used to perform the prediction of possible deleterious variants. Results: A total of 4468 variants were identified across 3676 individuals following pre-filtering. Seventy-one variants were identified at an allelic frequency (1% or more in at least one population), which were predicted to be linked to existing disease associations and, in some cases, linked to drug metabolisms. This list was further studied to identify 23 alleles with disease considerations found at significantly different frequencies in one or more populations. Conclusions: This study describes allele frequencies observed in African populations at significantly different frequencies relative to at least one other reference population and identifies a subset of variants of clinical interest. Despite the inclusion of mixed sequence coverage datasets, the variants identified pose notable avenues for future inquiries. A subset of variants of clinical interest with statistically significant inter-population frequency differences was identified for further inspection, which provides evidence of an African population-specific variant frequency profile. This study highlights the need for additional research and African genetics data given the presence of this unique frequency profile to better facilitate the genetic pre-screening of patients as a standard of practice in HIV care, particularly on the African continent where HIV is highly prevalent

Geospatial distribution of <i>Mycobacterium tuberculosis</i> genotypes in Africa

<div><p>Objective</p><p>To investigate the distribution of <i>Mycobacterium tuberculosis</i> genotypes across Africa.</p><p>Methods</p><p>The SITVIT2 global repository and PUBMED were searched for spoligotype and published genotype data respectively, of <i>M</i>. <i>tuberculosis</i> from Africa. <i>M</i>. <i>tuberculosis</i> lineages in Africa were described and compared across regions and with those from 7 European and 6 South-Asian countries. Further analysis of the major lineages and sub-lineages using Principal Component analysis (PCA) and hierarchical cluster analysis were done to describe clustering by geographical regions. Evolutionary relationships were assessed using phylogenetic tree analysis.</p><p>Results</p><p>A total of 14727 isolates from 35 African countries were included in the analysis and of these 13607 were assigned to one of 10 major lineages, whilst 1120 were unknown. There were differences in geographical distribution of major lineages and their sub-lineages with regional clustering. Southern African countries were grouped based on high prevalence of LAM11-ZWE strains; strains which have an origin in Portugal. The grouping of North African countries was due to the high percentage of LAM9 strains, which have an origin in the Eastern Mediterranean region. East African countries were grouped based on Central Asian (CAS) and East-African Indian (EAI) strain lineage possibly reflecting historic sea trade with Asia, while West African Countries were grouped based on Cameroon lineage of unknown origin. A high percentage of the Haarlem lineage isolates were observed in the Central African Republic, Guinea, Gambia and Tunisia, however, a mixed distribution prevented close clustering.</p><p>Conclusions</p><p>This study highlighted that the TB epidemic in Africa is driven by regional epidemics characterized by genetically distinct lineages of <i>M</i>. <i>tuberculosis</i>. <i>M</i>. <i>tuberculosis</i> in these regions may have been introduced from either Europe or Asia and has spread through pastoralism, mining and war. The vast array of genotypes and their associated phenotypes should be considered when designing future vaccines, diagnostics and anti-TB drugs.</p></div

Clustering of countries according the proportion of <i>M</i>. <i>tuberculosis</i> isolates present in a specific lineage.

<p>Only data from the Beijing, Cameroon, CAS, EAI, H, LAM, Manu, and S lineages was included. Country codes according to (<a href="http://www.worldatlas.com/aatlas/ctycodes.htm" target="_blank">http://www.worldatlas.com/aatlas/ctycodes.htm</a>). (A) Principle component analysis: African countries in the PCA plot are coloured based on their most dominant lineage: CAS (red), Cameroon (green), H (purple), LAM (brown), Manu (blue), and EAI (yellow). European and Asian countries are shown in black. Overlapping country codes in the PCA plot indicate a similar distribution of <i>M</i>. <i>tuberculosis</i> lineages in the respective countries. (B) pvclust analysis: The clusters edges are numbered in grey and the AU p-values are shown in black. Strongly supported clusters with AU greater than 95% are highlighted with a dotted line.</p

Clustering oof countries according the proportion of <i>M</i>. <i>tuberculosis</i> isolates belonging to different LAM sub-lineages.

<p>(A) Principle component analysis: African countries in the PCA plot are coloured based on their most dominant LAM sub-lineage: LAM1 (blue), LAM3 (blown), LAM9 (purple), LAM11-ZIM (red). PCA plot axes have been labelled with an “L” to indicate LAM followed by the sub-lineage number. European and Asian countries are shown in black. Overlapping country codes in the PCA plot (Morocco and Italy, Tunisia and France) indicate a similar distribution of LAM sub-lineages in the respective countries. (B) pvclust analysis: The clusters edges are numbered in grey and the AU p-values are shown in black. Strongly supported clusters with AU greater than 95% are highlighted with a dotted line. Country codes (<a href="http://www.worldatlas.com/aatlas/ctycodes.htm" target="_blank">http://www.worldatlas.com/aatlas/ctycodes.htm</a>).</p

Geospatial distribution of <i>M</i>. <i>tuberculosis</i> isolates belonging to the T sub-lineages.

<p>Country specific spoligotype data was only included if the country had >100 <i>M</i>. <i>tuberculosis</i> isolates and ≥15% of these isolates were from the T lineage. The sizes of the pie chart segments depict the proportion of isolates belonging to the different T sub-lineages (see colour chart for the respective sub-lineages). Each country has been shaded according to the proportion of T sub-lineages isolates present in that country (see colour intensity chart). Country codes (<a href="http://www.worldatlas.com/aatlas/ctycodes.htm" target="_blank">http://www.worldatlas.com/aatlas/ctycodes.htm</a>).</p

Geospatial distribution of <i>M</i>. <i>tuberculosis</i> lineages in Africa.

<p>Each pie chart segment reflects the relative proportion of <i>M</i>. <i>tuberculosis</i> isolates belonging to respective major lineages for each country (see colour chart for the respective major lineages). Each country has been shaded according to the number of isolates contributed to the analysis (see colour intensity chart). Country codes (<a href="http://www.worldatlas.com/aatlas/ctycodes.htm" target="_blank">http://www.worldatlas.com/aatlas/ctycodes.htm</a>).</p

Geospatial distribution of <i>M</i>. <i>tuberculosis</i> isolates belonging to the LAM sub-lineage.

<p>Country specific spoligotype data was only included if the country had >100 <i>M</i>. <i>tuberculosis</i> isolates and ≥15% of these isolates were from the LAM lineage. The sizes of the pie chart segments depict the proportion of isolates belonging to the different LAM sub-lineages (see colour chart for the respective sub-lineages). Each country has been shaded according to the proportion of LAM lineages isolates present in that country (see colour intensity chart). Country codes (<a href="http://www.worldatlas.com/aatlas/ctycodes.htm" target="_blank">http://www.worldatlas.com/aatlas/ctycodes.htm</a>).</p